Developing an Adaptive Model of Thermal Comfort and Preference
FINAL REPORT ASHRAE RP- 884
March 1997
Richard de DearÀ, Gail BragerÁ, Donna CooperÀ
À Macquarie Research Ltd., Macquarie University, Sydney, NSW 2109 AUSTRALIA
Á Center for Environmental Design Research, University of California, Berkeley, CA 94720 USA
“Results of Cooperative Research between the American Society of Heating, Refrigerating and Air Conditioning Engineers, Inc., and Macquarie Research, Ltd.”
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TABLE OF CONTENTS iii
ACKNOWLEDGMENTS vii
EXECUTIVE SUMMARY ix
CHAPTER 1 - INTRODUCTION & BACKGROUND 1
1.1. Introduction 1
1.2. Defining the adaptive process 3 1.2.1. The dialectic of contemporary thermal comfort theory 3 1.2.2. The “adaptive” hypothesis 4
1.3. A conceptual model of adaptation -- feedback loops 6 1.3.1. Behavioral feedback - adjustment 8 1.3.2. Physiological feedback -- acclimatization 10 1.3.3. Psychological feedback -- habituation and expectation 12
1.4. Literature review 13 1.4.1. Climate chamber evidence for adaptation to climate 13 1.4.2. Field evidence for adaptation 15 1.4.2.1. The earlier field evidence for adaptation 16 1.4.2.2. Analysis of neutral temperatures using recent field experiments 18 1.4.2.3. Evidence for behavioral adaptation - personal/environmental adjustment 22 1.4.2.4. Evidence for psychological adaptation - expectation and context 23
1.5. Implications for RP-884 26 1.5.1. Lessons from static heat balance models 26 1.5.2. Time scales of thermal adaptation 29
1.6. Aims 31
CHAPTER 2 - METHODS 33
2.1. Overview of the RP-884 approach 33
2.2. Establishing the database for RP-884 36 2.2.1. Sourcing the raw data 36 2.2.2. Ratings of raw data submitted to RP-884 40
2.3. Raw data standardisation 41 2.3.1. Creation of a standard data template 41 2.3.2. Consistent mean radiant temperatures within the database. 42 2.3.3. Consistent comfort index calculations within the database 42 2.3.4. Predicted draft risk index (PD) 43 2.3.5. Clothing insulation in the ASHRAE RP-884 database 44 2.3.5.1. Discrepancies between field estimation methods for clo. 45 2.3.5.2. The chair insulation effect 49
2.4. Developing an index for perceived thermal control 49
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2.5. Thermal acceptability issues within the RP-884 database 51 2.5.1. Developing a proxy variable for thermal acceptability based on thermal sensation votes. 51 2.5.2. Rating buildings in terms of their compliance with ASHRAE Standard 55 acceptable indoor climate guidelines 52
2.6. Outdoor meteorological/climatological data for the data base 52 2.6.1. Appending outdoor weather observations to each row of data 52 2.6.2. Climate classification applied to RP-884 raw data 53
2.7. Subdivision of the standardized field experiments 54
2.8. The meta-analysis 54 2.8.1. The unit of analysis for the RP-884 meta-analysis 54 2.8.2. Meta-file’s structure and coding conventions 55 2.8.3. General assumptions within the statistical meta-analysis 55 2.8.4. Statistical treatments on the various subjective thermal ratings 56 2.8.5. Preferred temperatures 59
2.9. The RP-884 database in the public domain and disseminated via the world wide web 60
2.10. Summary of the methods used in RP-884 64
CHAPTER 3 - BASIC RESULTS 67
3.1. Interactions with indoor climate 67 3.1.1. Thermal sensation 67 3.1.1.1. Dependence of thermal sensation on indoor operative temperature 68 3.1.1.2. Dependence of thermal sensation on indoor ET 69 3.1.1.3. Dependence of thermal sensation on PMV 70 3.1.1.4. Dependence of thermal sensation on indoor SET 71 3.1.2. Thermal neutrality 72 3.1.2.1. Neutral operative temperatures (neut_top) 72 3.1.2.2. Neutral effective temperatures (neut_et) 74 3.1.2.3. Neutral predicted mean votes (neut_pmv) 74 3.1.2.4. Predicted neutralities with the PMV heat balance model 75 3.1.2.5. Neutral standard effective temperatures (neut_set) 77 3.1.3. Thermal acceptability and indoor climate 78 3.1.3.1. Relationship between direct and inferred thermal acceptability 78 3.1.3.2. Directly determined thermal acceptability 80 3.1.3.3. Thermal acceptability inferred from thermal sensation 83 3.1.3.4. Thermal sensitivity and the range of thermally acceptable temperatures. 84 3.1.4. Thermal preferences and indoor climate 89 3.1.5. Comparisons between neutral and preferred temperatures indoors. 91 3.1.6. Behavioural adjustments to indoor climate 93 3.1.6.1. Thermal insulation adjustments indoors 94 3.1.6.2. Metabolic rate adjustments indoors 97 3.1.6.3. Air speed adjustments indoors 99
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3.2. Interactions with outdoor weather and climate 102 3.2.1. Thermal neutrality and outdoor climate 102 3.2.1.1. Seasonal comparisons 103 3.2.1.2. Dependence of observed neutrality on outdoor climate 104 3.2.1.3. Analysis of predicted neutralities with respect to mean outdoor temperature 106 3.2.2. Thermal acceptability and outdoor climate 108 3.2.3. Thermal preference and outdoor climate 110 3.2.4. Behavioral responses to outdoor climate 113 3.2.4.1. Indoor clothing and outdoor climate 114 3.2.4.2. Metabolic rate indoors related to outdoor climate 115 3.2.4.3. Indoor air speeds in relation to outdoor climate 116
3.3. Influence of building characteristics on thermal comfort 118 3.3.1. HVAC versus natural ventilation 118 3.3.1.1. Thermal sensation and sensitivity in HVAC versus naturally ventilated buildings 119 3.3.1.2. Thermal acceptability in HVAC versus naturally ventilated buildings 121 3.3.1.3. Thermal preferences in HVAC versus naturally ventilated buildings. 122 3.3.2. Personal environmental control 124 3.3.3. Building occupancy types - offices, residential and industrial 127
3.4. Summary of basic results 130 3.4.1. Summary of thermal sensation, acceptability and preference 131 3.4.2. Summary of thermal sensitivity and behavioral thermoregulation 133 3.4.3. Summary of the effects of outdoor climate on thermal perception indoors 134 3.4.4. Summary of the effects of contextual factors and perceived control 135
CHAPTER 4 - TOWARDS ADAPTIVE MODELS 139
4.1. The semantics of thermal comfort 139
4.2. Comparison of RP-884 models with earlier adaptive model publications 141
4.3. Comparison of RP-884 models with the PMV “static model” 145 4.3.1. Comparisons within the centrally conditioned building sample 146 4.3.2. Comparisons within the naturally ventilated building sample 150
4.4. Adaptive models for acceptable ranges of indoor temperatures 152
CHAPTER 5 - VARIABLE TEMPERATURE STANDARDS 155
5.1. A variable temperature standard for application in buildings with centrally controlled HVAC 155 5.1.1. Purpose 155 5.1.2. Scope 156 5.1.3. Definitions 156 5.1.4. Conditions for an acceptable thermal environment. 161 5.1.4.1. Analytic PMV method 161 5.1.4.2. Adaptive PMV method 161 5.1.4.3. Prescriptive method 163
5.2. A variable temperature standard for application in naturaly ventilated buildings 165
v 5.2.1. Purpose 165 5.2.2. Scope 165 5.2.3. Definitions 166 5.2.4. Conditions for an acceptable thermal environment. 168
BIBLIOGRAPHY 171
APPENDIX A - THERMAL SENSATION AND NEUTRALITY FOR EACH BUILDING IN THE RP-884 DATABASE 185
APPENDIX B - PREFERRED TEMPERATURE FOR EACH BUILDING IN THE RP-884 DATABASE 227
APPENDIX C - SUMMARY OF THE ORIGINAL FIELD EXPERIMENTS COMPRISING THE ASHRAE RP-884 DATABASE 235 C.1. Project Title - ASHRAE TC 2.1 sponsored RP-702 236 C.2. Project Title - Thermal comfort studies in modern industrial buildings. 239 C.3. Project Title - Doctoral dissertation. From comfort to kilowatts: An integrated assessment of electricity conservation in Thailand’s commercial sector. 242 C.4. Project Title - The CSAA, Antioch (1995) component of the Advanced Customer Technology Test (ACT2) project. 245 C.5. Project Title - Higher PMV causes higher energy consumption in air- conditioned buildings: a case study in Jakarta, Indonesia. 248 C.6. Project Title - Montreal ASHRAE RP-821. 250 C.7. Project Title - Richard de Dear’s PhD research project in Australia. 253 C.8. Project Title - A field study of thermal comfort using questionnaire software. 256 C.9. Project Title - “Thermal comfort in Pakistan.” 258 C.10. Project Title - Comfort criteria for passively cooled buildings. a PASCOOL task. 262 C.11. Project Title - Developing indoor temperatures for naturally ventilated buildings. 264 C.12. Project Title - Mixed mode climate control: some hands-on experience. 267 C.13. Project Title - ASHRAE sponsored RP-462. San Francisco area. 269 C.14. Project Title - A field investigation of thermal comfort environmental satisfaction and perceived control levels in UK office buildings, University of Liverpool. 272 C.15. Project Title - Thermal comfort in the humid tropics: field experiments in air conditioned and naturally ventilated buildings in Singapore. 275 C.16. Project Title - The Steelcase building. Grand Rapids Michigan, US 277 C.17. Project Title - Sunset building: a study of occupant thermal comfort in support of PG&E’s Advanced Customer Technology Test (ACT2) for maximum energy efficiency 279 C.18. Project Title - The Verifone building, a component of the Advanced Customer Technology Test (ACT2) Project. 282
APPENDIX D - CLIMATE CLASSIFICATION 285
APPENDIX E - CODEBOOK FOR RAW DATA IN RP-884 DATABASE 287
vi APPENDIX F - CODEBOOK FOR THE RP-884 META-ANALYSIS 291
APPENDIX G - AULICIEMS’ ADAPTIVE MODEL DATABASE 295
ACKNOWLEDGMENTS
The successful completion of this project depended very heavily on the willingness of field researchers to make available their raw data for re-analysis and incorporation into the RP- 884 database. In particular, we would like to thank the following contributors:
Dr. Jill Brown, formerly of University of Wales, Cardiff; Dr. John Busch Jr. Lawrence Berkeley Labs., California; Prof. Cris Benton, CEDR, University of California at Berkeley; Dr. Tri Karyono, Agency for the Assessment and Application of Technology (BPPT), Jakarta, Indonesia (formerly of the Department of Architecture, University of Sheffield, UK); Dr. Giovanna Donnini, formerly of Auger, Donnini and Nguyen Inc, Montreal, Canada; Dr. Guy Newsham, Institute for Research in Construction, National Research Council of Canada, Ottawa; Fergus Nicol, School of Architecture, Oxford-Brookes University, UK.; Iftikhar Raja, School of Architecture, Oxford-Brookes University, UK; Prof. Nick Baker, The Martin Centre for Architecture and Urban Studies, University of Cambridge, UK; David Rowe, Dept. of Architectural and Design Science, University of Sydney, Australia; Dr Ruth Williams, The Building Services Research and Information Association, UK (formerly Liverpool University, UK); Fred Bauman, CEDR, University of California at Berkeley.
RP-884 also depended on weather and climate data resources. Such data was required for the relevant sites and periods covered by field experiments within the database. Apart from resources available on the WWW and various CD-ROM publications, the following organisations provided data. The Australian Bureau of Meteorology’s National Climate Centre supplied meteorological data for the Melbourne, Brisbane and Darwin field experiments. The Oxford University Radcliffe Observatory supplied meteorological observations for some of the UK experiments. Macquarie University’s Meteorological Site supplied observations for the Sydney field data. The US National Climate Data Center (NCDC) supplied meteorological data for the Californian experiments. Meteorological
vii observations for Grand Rapids were supplied by the Michigan State Climatologist. Bangkok meteorological data were supplied by the Royal Thai Meteorological Department.
Special thanks are also due to Andris Auliciems of the University of Queensland, Fergus Nicol of Oxford-Brookes University and Michael Humphreys of Oxford University for their pioneering work in the area of adaptive models and also for their encouragement at various stages during the ASHRAE RP-884 project.
viii EXECUTIVE SUMMARY
One of the more contentious theoretical issues in the applied research area of thermal comfort has been the dialectic between “adaptive” and “static” models. Apart from having disparate methodological bases (the former laboratory-experimental, the latter field-based), the two approaches have yielded starkly differing prescriptions for how the indoor climate of buildings should be managed. These prescriptions carry implications for the types of permissible building designs, the means by which their thermal environments are controlled, and the amounts of energy they consume in the production of habitable indoor climates. Static models have led to indoor climate standards that have been universally applied across all building types, are characterised by minimal recognition of outdoor climatic context, and are contributing to an increased reliance on mechanical cooling. In contrast, proponents of adaptive models have advocated variable indoor temperature standards that more fully exercise the adaptive capabilities of building occupants. This approach potentially leads to more responsive environmental control algorithms, enhanced levels of occupant comfort, reduced energy consumption, and the encouragement of climatically responsive building design.
Despite these apparent differences, our review of the research literature emerging from both approaches indicated that this seemingly irreconcilable split was primarily the result of narrow definitions of the term “thermal adaptation”, and that there were opportunities to bridge some of the gap between the hypotheses. We suggest that human thermal adaptation is comprised of three distinct yet interrelated processes - behavioral, physiological, and psychological. The adoption of this tripartite definition goes some way towards reconciling the static and adaptive approaches and the indoor climate standards derived from them.
This project’s principal objective was the proposal of a variable temperature standard based on the adaptive approach. Where it differs from earlier attempts is in the quality control applied throughout its adaptive modelling method. About 21,000 sets of raw thermal comfort data from 160 buildings were collected from most of the thermal comfort field research groups around the world who are currently active. Data selection criteria
ix emphasized precision of indoor climatic instruments, while data assimilation involved a variety of questionnaire standardization processes. For example, each one of the over 21,000 building subjects’ clothing thermal insulation estimates was transformed into an equivalent clo value using consistent procedures specified in ASHRAE Standard 55-1992. The thermal effects of chairs for seated subjects was also included. For each set of raw data, outdoor meteorological and climatological data were appended to the RP-884 database. All indoor and outdoor thermal indices were recalculated using a standard software package (WinComf©) recently commissioned by ASHRAE’s TC 2.1. Since a significant component of this project’s effort was expended in the assembly of the database, and since that database has relevance to thermal comfort research problems extending well beyond the scope of RP-884, we have chosen to place this valuable data resource in the public domain (World Wide Web) where it can be used by the international thermal comfort research community.
After statistically analysing the raw data collected in each of the RP-884 database’s 160 buildings, we conducted a meta-analysis of human subjective response to indoor climate and how it interacted with indoor architectural, contextual and outdoor meteorological factors. The main subjective response variables were thermal neutrality (derived from thermal sensation votes) and preferred temperature. Eighty and 90% thermal acceptability criteria for general thermal comfort were estimated for each building as the range of operative temperatures falling between mean thermal sensations of ±0.85 and ±0.5 respectively. The list of independent variables in the meta-analysis included the following indoor climatic indices: operative temperature, effective temperature, PMV/PPD and standard effective temperature. Outdoor climate was operationalized as an independent variable in our meta-analysis as the mean of daily minimum and maximum outdoor effective temperatures prevailing during each building’s survey period. The most important contextual factor in our meta-analysis was a classification of buildings as having either central HVAC or being naturally ventilated. This distinction was a unique feature of the ASHRAE RP-884 project, and produced some of the most significant results.
The meta-analysis clearly indicated that the definition and prescription for thermal acceptability contained in ASHRAE Standard 55-92 bore little resemblance or relationship to the levels actually expressed by occupants within the building sample. Thermal sensation
x and thermal preference on the other hand, demonstrated statistically significant dependence on indoor thermal indices prevailing at the time of the questionnaire (these included operative, effective and standard effective temperatures, or PMV/PPD). Thermal neutrality, defined as the operative temperature most closely corresponding with a mean thermal sensation vote of zero (“neutral”) showed an adaptive relationship with mean indoor temperatures - warm buildings had warm neutralities and vice versa. However, this adaptive relationship was stronger in naturally ventilated buildings than in buildings with centralized HVAC systems. Similar adaptive relationships were established for neutrality and preference with outdoor climate, and again, the strength of the relationship was greater in the sample of naturally ventilated buildings. These observations support the notion that building occupants’ thermal ideals are influenced by their thermal experiences both indoors and outdoors.
Preferred temperature for a particular building did not necessarily coincide with thermal neutrality, and this semantic discrepancy was most evident in HVAC buildings where preference was depressed below neutrality in warm climates and elevated above neutrality in cold climates (i.e, people preferred to feel cooler than neutral in warm climates, and warmer than neutral in cold climates). This finding suggests that much of what has been regarded as climatic adaptation by previous proponents of the adaptive model was in fact a consequence of defining thermal optima in terms of neutrality instead of preference.
Clothing insulation worn by building occupants demonstrated a dependence on both mean indoor and outdoor temperatures. Thermal insulation levels worn indoors decreased as indoor and outdoor temperatures increased, while mean indoor air speed demonstrated a positive dependence on prevailing temperature levels. The close agreement between PMV model predictions of optimum indoor temperature and those actually observed within HVAC buildings suggests that the type of thermal adaptation found in such buildings was of the behavioral type, mainly driven by adjustments to clothing and indoor air speed. In contrast, the range of optimum indoor temperatures observed in naturally ventilated buildings was about twice as large as that predicted by the PMV model, suggesting that physiological (acclimatisation) and psychological (shifting expectations) adaptive processes were superimposed on the behavioral adaptations of clothing and air speed adjustment in the naturally ventilated context.
xi Based on these adaptive relationships between indoor comfort and outdoor climate, the RP- 884 project concluded with a pair of variable temperature standards. One standard was designed for use in HVAC buildings where occupants had little or no adaptive opportunity, while the other was designed for naturally ventilated buildings where occupants had access to operable windows and other adaptive opportunities. The HVAC standard was based on three alternative methods; a) the analytic PMV method for use whenever accurate estimates for all the heat-balance model’s inputs were feasible; b) the modified “adaptive PMV” method for use whenever an accurate estimate of mean outdoor effective temperature was possible (defined as the arithmetic average of 6am and 3pm outdoor effective temperatures), and c) the prescriptive method for use whenever the first two approaches were not feasible (presented as summer and winter comfort zones on the psychrometric chart). Acceptable ranges of operative temperature were applied symmetrically above and below predicted optimum operative temperatures. The average winter prescription for 90% general thermal acceptability (excluding local discomforts) was given as 22.5°C ± 1.2 K while the summer prescription was given as 23.5°C ± 1.2 K.
The variable temperature standard for use in naturally ventilated buildings was given as an adaptive linear regression model based on outdoor weather and climate:
optimum indoor temperature = 18.9 + 0.255 * (outdoor mean ET*)
Acceptable temperature ranges around the optimum in naturally ventilated buildings were specified as ±3.5 for 80% general acceptability and ±2.5 for 90% general acceptability.
The RP-884 project leads to the conclusion that the PMV model represents a useful adjunct to comfort standards intended for use exclusively within HVAC buildings where occupants have little or no opportunity to adapt themselves, nor their immediate occupied zone. However, application of this same model in naturally ventilated settings leads to significant errors since it overlooks an important adaptive response in the form of variable thermal expectations of building occupants in such buildings. In naturally ventilated settings we recommend the application of an adaptive model that predicts optimum indoor temperature from a knowledge of the building’s meteorologic or climatic setting.
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xv ASHRAE RP-884 Final Report
CHAPTER 1 - INTRODUCTION & BACKGROUND
1.1. Introduction
The way we design, construct, and operate buildings has profound implications for the quality of both the natural and built environments. All too often today’s buildings require massive resource inputs, create bleak or potentially unhealthy indoor environments, pollute both their local and global environments through increased greenhouse emissions, as well as contributing to the destruction of natural habitats (Barnett and Browning 1995). The energy required to heat and cool our buildings, and the very way we define the “comfortable” thermal conditions we are trying to maintain, play significant roles in this environmental impact. The use of energy for heating, ventilating and air- conditioning (HVAC) of the indoor environment is already the largest sector in energy consumption in most of the developed world (Griffiths et al 1988). As well we seeing a significant increase in HVAC energy use in developing and newly industrialized countries as well (Ang 1986, Abro 1994). This is particularly relevant to the rapidly developing tropical regions of the Asia-Pacific region, where traditional lifestyles in naturally ventilated buildings are giving way to an increased reliance on mechanical cooling. This in turn is changing both the way we design buildings and building occupants’ expectations and behavioral patterns related to air conditioning (Lovins 1992).
It is commonly estimated that persons in economically developed countries spend at least 80% of their time indoors. This suggests that the quality of the indoor environment can have a significant impact on comfort, health, and overall sense of well-being. In an effort to maintain the quality of the indoor environment, we mechanically condition our buildings to provide constant, uniform, “comfortable” environments. The current standards that define what those “comfortable” conditions should be were conducted primarily with university students and in mid-latitude climate regions (ASHRAE 1992, ISO 1994). Other than allowing for only a slight seasonal shift in the comfort zone based on clothing adjustments, it is often suggested that the standards are universally applicable across all building types, climates, and populations (Parsons 1994 and
Introduction & Background page 1 MRL Australia ASHRAE RP-884 Final Report discussion). A strict reliance on laboratory-based comfort standards also ignores important cultural and social differences in the need or desire for air conditioning. A special issue of Energy and Buildings (Kempton and Lutzenhiser 1992) focused on these non-thermal issues, with a variety of papers examining how individuals and cultures vary in their perceived need for and expectations of air conditioning.
But perhaps the single biggest issue in this debate remains the applicability of standards in buildings which aren’t air conditioned at all. For example, when recently asked by a union official whether or not Standard 55 (ASHRAE 1992) was applicable to un-air-conditioned premises, ASHRAE’s Technical Committee (TC 2.1) responsible for the standard openly declared that their comfort charts were intended for both HVAC and naturally ventilated premises. Many researchers, however, challenge this assumption of universal applicability, arguing that it ignores important contextual differences that can attenuate responses to a given set of thermal conditions. While the “comfort zone” might be viewed by the engineering community as a design goal for a deterministic HVAC control system, its relevance to naturally ventilated buildings where conditions are inherently much more variable is questionable (Forwood 1995). This was also acknowledged by Givoni (1992), who revised his already notable work on the building bioclimatic chart. He expanded the boundaries of the comfort zone based on the expected indoor temperatures achievable with different passive design strategies, applying a “common sense” notion that people living in unconditioned buildings become accustomed to, and grow to accept higher temperature or humidities. Strict and literal interpretation of the static “comfort zone” precludes application to anything other than full-blown HVAC designs across the world’s moderate to extreme climate zones.
An alternative to traditional comfort theory - termed the “adaptive model” of comfort - embraces the notion that people play an instrumental role in creating their own thermal preferences. This is achieved either through the way they interact with the environment, or modify their own behavior, or because contextual factors and past thermal history change their expectations and thermal preferences. Interest and research into this “adaptive” theory of thermal comfort first began in the mid-70’s in response to the oil- shocks, and has recently regained momentum due to increasing concerns over human impact on global climatic environment. There are numerous benefits to be gained from
Introduction & Background page 2 MRL Australia ASHRAE RP-884 Final Report an improved understanding of the influence of adaptation on thermal comfort in the built environment. These include improved predictive models and standards, more sophisticated and responsive environmental control algorithms, increased opportunities for personal control, enhanced levels of thermal comfort and acceptability among occupants, reduced energy consumption, and the encouragement of climatically responsive and environmentally responsible building design.
This research project, “ASHRAE RP-884 - Developing an Adaptive Model of Thermal Comfort and Preference”, is premised on the development and analysis of a quality- controlled, cumulative database compiled from previous thermal comfort field experiments worldwide. The aim is to use this database to refine our conceptual understanding of adaptive mechanisms, to develop an empirical model of the adaptive process, and to propose a variable temperature standard to supplement the current ASHRAE Standard 55 (1992).
1.2. Defining the adaptive process
1.2.1. The dialectic of contemporary thermal comfort theory
In contemporary thermal comfort research, there is a perceived irreconcilable split into “static” and “adaptive” schools of thought (Auliciems 1989; Nicol 1993). In the “static” camp are ASHRAE’s Standard 55 --Thermal Environmental Conditions for Human Occupancy (ASHRAE 1992) and the ISO Standard 7730 (ISO 1994). The static model essentially views the person as a passive recipient of thermal stimuli. It is premised on the assumption that the effects of a given thermal environment are mediated exclusively by the physics of heat and mass exchanges at the surface of the body, while the maintenance of a constant internal body temperature necessitates some physiological responses. It is generally assumed in the static school of thought that thermal sensations (hot-warm-cool-cold) are proportional to the magnitude of these physiological responses, as measured by mean skin temperature and latent heat loss or wettedness due to sweating (Benzinger 1979). The deterministic logic underpinning heat balance comfort models such as PMV, ET* and SET* is:
physics Þ physiology Þ subjective discomfort
Introduction & Background page 3 MRL Australia ASHRAE RP-884 Final Report
These models are based on extensive and rigorous laboratory experiments, and yield fairly consistent, reproducible results in climate chambers. However, researchers are increasingly exploring the extent to which we can directly apply these laboratory-derived models, without modification, to the task of predicting subjective responses to thermal conditions in real buildings, where the interactions between the occupants and indoor climate are exceedingly complex. Adherents to the adaptive school of thought regard the simplistic cause-and-effect approach embodied in the static models as inadequate to describe thermal perception in the real world. As such the static hypothesis has come to be regarded as a “single temperature” model of thermal comfort (Humphreys 1981, 1994a, Nicol 1993: Auliciems 1989). But a more conciliatory interpretation of the heat balance model depicts it as partially adaptive, since it does include the impact of thermal variables and clothing which can be adjusted by the occupant.
1.2.2. The “adaptive” hypothesis
With the static heat-balance models representing one side, on the other side of this dialectic is the “adaptive” school of thought in which factors beyond the fundamental physics and physiology all interact with thermal perception. These factors can include demographics (gender, age, economic status), context (building design, building function, season, climate, semantics, social conditioning), and cognition (attitude, preference, and expectations) (McIntyre 1982, Baker 1993, Baker and Standeven 1994, Oseland 1994a,b, Griffiths et al 1988). These factors have been demonstrated time and again to be irrelevant to the comfort responses of subjects in the contrived setting of the climate chamber (Fanger 1972b, de Dear et al 1991a). However, there remains a lingering suspicion in the minds of adaptive modellers and practitioners alike that such considerations cannot be dismissed so easily in the context of real buildings.
The generic term “adaptation” might broadly be interpreted as the gradual diminution of the organism’s response to repeated environmental stimulation. As used in RP-884, adaptation subsumes all physiological mechanisms of acclimatization, plus all behavioral and psychological processes which building occupants undergo in order to improve the “fit” of the indoor climate to their personal or collective requirements. Within
Introduction & Background page 4 MRL Australia ASHRAE RP-884 Final Report this broad definition it is possible to clearly distinguish three categories of adaptation (Folk 1974, 1981, Goldsmith 1974, Prosser 1958, Clark and Edholm 1985):
1. Behavioral Adjustment. This includes all modifications a person might consciously, or unconsciously make, which in turn modify heat and mass fluxes governing the body’s thermal balance. We define adjustment in terms of three subcategories: a) Personal adjustment: adjusting to the surroundings by changing personal variables, such as adjusting clothing, activity, posture, eating/drinking hot/ cold food or beverages, or moving to a different location; b) Technological or environmental adjustment: modifying the surroundings themselves, when control is available, such as opening/closing windows or shades, turning on fans or heating, blocking air diffusers, or operating other HVAC controls, etc.; and c) Cultural adjustments, including scheduling activities, siestas, dress codes
2. Physiological. The most comprehensive definition of physiological adaptation would include all of the changes in the physiological responses which result from exposure to thermal environmental factors, and which lead to a gradual diminution in the strain induced by such exposure. Physiological adaptation can be broken down into at least two subcategories: a) Genetic adaptation: alterations which have become part of the genetic heritage of an individual or group of people, but developing at time scales beyond that of an individual’s lifetime, and b) Acclimation or Acclimatization (used interchangeably here): changes in the settings of the physiological thermoregulation system over a period of days or weeks, in response to exposure to single or a combination of thermal environmental stressors.
3. Psychological. The psychological dimension of adaptation to indoor climate refers to an altered perception of, and reaction to, sensory information. Thermal perceptions are directly and significantly attenuated by one’s experiences and expectations of the indoor climate. This form of adaptation involves building occupants’ “comfort setpoints” which may vary across time and space. Relaxation of indoor climatic expectations can
Introduction & Background page 5 MRL Australia ASHRAE RP-884 Final Report be likened to the notion of habituation in psychophysics -- repeated or chronic exposure to an environmental stressor leading to a diminution of the evoked sensation’s intensity (Glaser 1966, Frisancho 1981).
Adaptation to Indoor Climate
adjustment habituation behavioral/technological psychological adaptation - changes to heat-balance changing expectations
acclimatization long-term physiological adaptation to climate
Figure 1.1: The three components of adaptation to indoor climate
1.3. A conceptual model of adaptation -- feedback loops
An important premise of the adaptive model is that the building occupant is no longer simply a passive recipient of the thermal environment as given, as in the case of a climate chamber experimental subject, but instead is an active agent interacting with all levels of the person-environment system via feedback loops. We continue to emphasize, however, our opinion that this perspective complements rather than contradicts the “static” heat-balance view as outlined above. The heat-balance model does partially account for adaptation by using as inputs those parameters affected by adjustment and environmental interventions, but it explicitly rules out any notions of physiological and psychological adaptation.
In contrast, the adaptive model draws upon a phenomenological perspective that emphasizes how people interact with and change their environment, and accounts for the ways in which a person’s past experience, future plans, and intentions influence one’s perception (Canter 1983, Wohlwill 1974, Helson 1964, Veitch and Arkkelin 1995, Kaplan and Kaplan 1982). The adaptive hypothesis indicates that one’s satisfaction with an indoor climate is achieved by a correct matching between the actual thermal
Introduction & Background page 6 MRL Australia ASHRAE RP-884 Final Report environmental conditions prevailing at that point in time and space, and one’s thermal expectations of what the indoor climate should be like. Thermal expectations result from a confluence of current and past thermal experiences, cultural and technical practices (Auliciems 1981, 1989, de Dear 1993, Nicol 1993). These relationships have been described in Figure 1.2, a schematic diagram developed by Auliciems (1981, 1989) showing that a given set of indoor climatic conditions can elicit varying levels of comfort and satisfaction from building occupants, depending on culture or climatic and HVAC/architectural expectations.
Figure 1.2: The "adaptive model" of thermal perception (after Auliciems, 1981)
By logical extension, the adaptive hypothesis also implies that the temperatures people expect indoors for comfort and satisfaction will move in the direction of the average conditions encountered in their day-to-day life, both indoors and out. So, in the systems schematic in Figure 1.2, outdoor climate acts as a negative feedback which attracts the thermal perceptual sub-system’s set point, thereby damping load error
Introduction & Background page 7 MRL Australia ASHRAE RP-884 Final Report
(dissatisfaction/discomfort) within the human behavioral thermoregulatory system. The net result is that adapted building occupants may be perfectly comfortable at temperatures beyond those recommended in standards such as ASHRAE 55 (1992) and ISO 7730 (1984, 1994).
We believe that the development of an adaptive predictive model of thermal comfort should combine features of both the static and adaptive theories, and that these various feedback loops should be described in terms of how they affect the more traditional linear relationships. As set out in the heat balance models (physics Þ physiology Þ subjective discomfort)
1.3.1. Behavioral feedback - adjustment
Behavioral adjustment of the body’s heat-balance probably offers the greatest opportunity for people to play an active role in maintaining their own comfort. The extent to which building occupants can, or do, behaviorally interact with their indoor climate depends a great deal on contextual factors. This is very important in both the development and application of an adaptive model, and deserves further elaboration. Context can be described in terms of adaptive opportunity, compared to the constraints or restrictions on thermoregulatory degrees of freedom (Nicol and Humphreys 1972). That is, “adaptive opportunity” refers to whether or not buildings afford their occupants scope for adaptive interventions (Baker and Standeven 1994). This may result from:
a) an attribute of the building itself (e.g. are windows operable? how far are occupants placed away from such windows? is the floor plan individual office cells or open-plan bureau landschaft?),
b) characteristics of the active, or energy consuming, climate services inside the structure (e.g. centralized HVAC services, or decentralized task conditioning controls at each workstation?), or
c) the organizational and social conditions prevailing within the building (e.g. is there a strict or casual dress code? are employees bound to a single workstation for the entire working day?).
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The flip-side of adaptive opportunity (i.e, the lack of...), is the analysis of constraints to thermal control. These constraints may be gathered under five main headings (Nicol and Humphreys 1972, Humphreys 1994a):
a) Constraints due to climate. Buildings in harsh or extreme climates might present a more exclusive barrier to the elements than buildings in milder climate, affording their occupants fewer adaptive opportunities.
b) Economic constraints. The costs of thermal environmental control, both initial and recurrent, often exceed the resources of many countries.
c) Constraints due to social custom or regulation. To what extent can an individual change his/her clothing? Are clothing patterns determined by climate, fashion or religion? To what extent do the various requirements put on us by other people, government energy guidelines, greenhouse gas emission quotas or targets limit our freedom to behaviorally thermoregulate?
d) Constraints due to task or occupation. Often the requirements of a particular job override those of thermal comfort, when there are formal dress codes of fixed work locations.
e) Constraints due to design. This refers to design of the building or HVAC system, availability of task-conditioning or personal environmental controls, design quality of awnings, climatic suitability of window placement and size.
The concept of adaptive opportunity helps to differentiate those buildings in which a deterministic relationship between the thermal environment and human response is applicable, and those in which an adaptive feedback loop is fully operational. Adaptive opportunity can be thought of as a continuum. At one extreme is the climate chamber in which subjects are instructed what to wear and what activities they are to perform while an external agent, the researcher, determines the temperature, humidity and air flow regime they are to experience for the duration of the experiment. At the other extreme we find the single-occupant room in which clothing and activity patterns are discretionary
Introduction & Background page 9 MRL Australia ASHRAE RP-884 Final Report and environmental controls cover the full range of possibilities from operable windows through to task-ambient air conditioning.
The ultimate efficacy of any form of adaptive control must be measured in terms of occupant satisfaction and ideally should be evaluated in terms of available control (adaptive opportunity) vs. exercised control (actual physical control that takes place) vs. perceived control (Paciuk 1989, 1990). But regardless of whether it is placebo or real control, there seems little dispute in the literature that the issue of personal and environmental control is central to thermal acceptability, and therefore should be a factor examined in the RP-884 data analysis.
Behavioral adjustment represents the most immediate feedback link to the thermal environment. Stated simply, if a person is uncomfortable, or expects to become so, they are to take corrective action. What might have previously been regarded as the final consequence in the static heat balance model (the conscious sensation of thermal discomfort), becomes the starting point for this feedback in the adaptive model.
indoor clothing body’s physiol. thermal discomfort climate + activity heat load regulation sensation dissatisfaction
Behavioral Adjustment
Figure 1.3: Behavioral feedback loop
1.3.2. Physiological Feedback -- acclimatization
Physiological acclimatization to cold stress is primarily associated with maintenance of warmer skin temperatures and increased heat production, although it is not clear to what extent the increased metabolic rate can occur without shivering (Frisancho, 1981). Otherwise, adaptation to the cold is primarily behavioral (Clark and Edholm 1985). The evidence for physiological acclimatization is more thoroughly documented for heat exposure, be it metabolically or environmentally induced (Folk 1974, 1981, Fox 1974,
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Bruce 1960, Berglund and McNall 1973, Givoni and Goldman 1973). The primary physiological response to prolonged heat stress induced by a regime of work in heat is an increased sweating capacity for a given heat load. Other changes related to thermoregulatory sweating include a fall in the setpoint body temperature at which sweating begins, triggering the onset of sweating earlier. A heat acclimatized person also achieves a better distribution of sweat over their skin compared to an unacclimatized person under the same heat load. Faced with comparable levels of heat challenge, the heat acclimatized person also demonstrates a variety of cardiovascular responses such as reduced heart rate, an increased blood volume and peripheral blood flow (Fox 1974, Bean and Eichna 1943, Hardy 1961, Wyndham 1970). Acclimatization to heat takes place mainly in the first week of exposure, while a longer period is required for cold acclimatization or for resting or sedentary activity (Bruce 1960).
This picture of acclimatization can be regarded as most appropriate to hot-dry climate zones. The pattern in hot-humid climates, however, differs significantly (Gonzalez et al 1974, Goldman et al 1965). In particular, the elevated capacity for sweating observed in hot-dry situations seems to be less important in the humid condition due to the reduced evaporative potential of the environment. Thus, while sweat secretion in the humid acclimatized subject is initiated at a core temperature lower than that for the unacclimatized subject, the shortfall in body heat dissipation in the humid condition appears to be taken up by increased dry heat losses from the skin which result from an increased peripheral blood flow and skin temperature.
Acclimatization is an unconscious feedback loop mediated by the autonomic nervous system, that directly affects our physiological thermoregulation setpoints. Like behavioral adjustment depicted earlier, the physiological feedback process of acclimatization can also be depicted schematically:
outdoor indoor physiol. strain discomfort & climate climate & regulation dissatisfaction
Acclimatization
Figure 1.4: Physiological feedback loop
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1.3.3 Psychological feedback -- habituation and expectation
Psychological adaptation encompasses the effects of cognitive and cultural variables, and describes the extent to which habituation and expectation alter thermal perceptions. This concept has been most clearly elaborated under the banner “adaptation-level theory” (A-LT). A-LT introduces the notion of optimal levels of stimulation, or adaptation levels, along with a view of environmental stress resulting from excessive deviations from such optimal levels. These optimal adaptation levels result from past exposure, and act as benchmarks for environmental evaluations (Wohlwill 1974, Helson 1964). Studies of the general nature of perception and its relationship to environmental stimuli, memory and cognition, and contextual factors such as building type or season, can also offer insights into understanding thermal comfort in buildings (de Dear et al 1991c, Helson 1971, Ittelson 1973, Auliciems 1981, Russell and Ward 1982).
The role of expectation in thermal comfort research was acknowledged in the earlier work of McIntyre (1980), who stated that “a person’s reaction to a temperature which is less than perfect will depend very much on his expectations, personality, and what else he is doing at the time.” Although the least studied of the three adaptive mechanisms, psychological adaptation might actually play the most significant role in explaining the differences between observed and predicted thermal responses. This applies particularly in light of different environmental contexts such as the laboratory vs. home vs. office, or when comparing responses in air-conditioned vs. naturally-ventilated buildings (Fishman and Pimbert 1982, Heijs and Stringer 1988, Bush 1990, de Dear et al 1991c, Rowe et al 1995, Oseland 1995,).
In terms of a feedback loop that can be incorporated into our conceptual model of adaptation, expectation and habituation are influences by one’s current thermal experience or one’s longer history of experiences with both the indoor and outdoor climate. This in turn directly affects our thermal sensation and cognitive assessments of thermal acceptability as described in Figure 1.5.
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outdoor indoor physiol. strain thermal discomfort climate climate (Tsk, wet) sensation dissatisfaction
Climatocultural practices & norms, Expectation HVAC & architecture & Habituation
Figure 1.5: Psychological feedback loop
1.4. Literature review
The relevant literature for this project is classified into two broad categories: 1) climate chamber evidence for adaptation to climate, and 2) field evidence for adaptation. Within the second category, we review some of the earliest studies of adaptation, as well as an analysis of more recent, rigorously conducted field studies in both air-conditioned and naturally ventilated buildings. The literature review of field studies will be further sub-classified in terms of specific evidence for both behavioral and psychological adaptation
1.4.1. Climate chamber evidence for adaptation to climate
A research design for experiments known as the “preferred temperature method” has been applied by various researchers over the years to the questions raised by the adaptive hypothesis. This method is very suitable for testing the adaptive feedback in a laboratory setting because the environmental temperature within the chamber is directly controlled by its single occupant, the subject. What follows is a summary of some of the more pertinent results.
Fanger et al (1977) investigated the effects of differing climatic experiences, and by implication, adaptive states, on thermal comfort responses by comparing the temperature preferences of climatically disparate samples. In one study, sixteen Danish subjects wore a standard 0.6 clo ensemble and sat quietly in a string chair (assumed to exert negligible effect on their clothing insulation), one-at-a-time in a climate chamber for 2.5 hr. Subjects were selected for the study because of their regular swimming in the ocean off Copenhagen during winter (lat. 56°N, mean February air temperature 0°C). The sample was found to have the same preferred temperature, about 25.5°C, as
Introduction & Background page 13 MRL Australia ASHRAE RP-884 Final Report regular Danish college students (not winter swimmers) under the same experimental conditions (Fanger and Langkilde 1975). Another Danish sample with cold exposures consisted of 16 meat-packers from a refrigerated storeroom (Fanger et al 1977). They too had the same preferred temperatures as the winter swimmers and college students.
If cold exposure fails to influence temperature preference, the next question is whether or not heat exposure has an effect. As noted earlier, physiologists have a clearer picture of heat, as opposed to cold, acclimatization, and much of that work refers specifically to heat stress conditions of the type induced by a regime of work in heat. Very little research has been done into the effects of acclimatization on thermal discomfort in the moderate heat stress range. In one such study, Fanger (1972a) recruited a sample of 16 long-term inhabitants of the tropics shortly after their arrival in Copenhagen. The same procedure as described above was followed, and the result, again, was that temperature preferences were not significantly different.
Acknowledging the limited “shelf-life” of physiological heat acclimatization, de Dear et al (1991b) replicated Fanger’s tropical experiment on location in Singapore (lat. 1°N) using a sample of 32 college students. Attention to detail in the replication went as far as borrowing the standard 0.6 clo KSU uniforms from Fanger's Danish laboratory, and a chair similar to the Danish string chair was also used. Again, temperature preferences turned out not to be significantly different from those of Fanger's benchmark Danish subjects ~ circa 25.5°C (de Dear et al 1991b).
Gonzalez (1979) studied the role of natural heat acclimatization (humid) during a five day heat wave in New Haven Connecticut during which day-time temperature maxima ranged between 32°C to 37°C and 88% to 90% rh. Twenty young male subjects participated. For lightly exercising subjects (116 W m-2), there was a discernible increase in preferred temperature (as assessed by a rating scale) after the heat wave (Gonzalez 1979). However, there were no statistically significant differences in thermal comfort or acceptability responses of resting subjects between the before-and-after heat wave tests.
The only significant departure from this picture of overall consistency in chamber research results has been a recent, but as yet unpublished, PhD thesis from the
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University of London (Abdulshukor, 1993). Three results from that study have been cited by Humphreys (1994a):
· Chinese subjects in a Malaysian climate chamber preferred a temperature of 28.0°C, · Malay subjects in a Malaysian climate chamber preferred an even warmer temperature at 28.7°C, while · Malay subjects in a London climate chamber study preferred only 25.7°C.
A clear implication of these results is that the hot and humid climatic context of the Malay peninsular was responsible for a three degree elevation of temperature preferences. These Malaysian climate chamber results are perplexing insofar as the same ethnic groups (Chinese and Malays) with exactly the same thermal histories and experiences (Singapore lies at the tip of the Malay peninsula) were represented in the de Dear et al. (1991b) chamber study. Using exactly the same clothing, metabolic rate and experimental protocol as used in Fanger’s Danish studies, the temperature preferences in Singapore’s climate chamber were three degrees cooler than these unpublished Malaysian results.
In conclusion, on the basis of the majority of experimental evidence published to date, subjective discomfort and thermal acceptability under conditions most typically encountered in residences and office buildings, by resting or lightly active building occupants, appear to be unaffected by the physiological processes of acclimatization.
1.4.2. Field evidence for adaptation
While chamber studies have the advantage of testing under carefully controlled conditions, field studies are best used for assessing the potential impacts of behavioral or psychological adaptations as they occur in realistic settings. If people feel thermally comfortable in conditions that fall outside of the ASHRAE comfort zone, it seems likely that adaptation has played a role. While the majority of published field studies collected the necessary data to determine whether people are comfortable when conditions are in or out of the comfort zone, only a subset of the data contains sufficient detail to disentangle the causal mechanisms behind those responses. In other words, exactly what kind of adaptation was taking place?
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1.4.2.1. The earlier field evidence for adaptation
Subjective assessments of thermal comfort typically use the rating scale method (McIntyre 1978), where comfort is operationalized as a vote coinciding with the central category of a thermal sensation, or comfort scale (“neutral”, or “comfortable”). The ambient temperature found by statistical analysis to most frequently coincide with this central rating is referred to as the sample's "neutrality" and is denoted here as Tn. The typical cross-sectional field study consists of a questionnaire with rating scales administered to building occupants while simultaneously recording indoor climatic variables. The most important of which is air temperature. The simplest of these studies are based on single-point measurements of temperature, and possible humidity. Numerous such studies have been published over the years, and Humphreys' (1975) review of 36 examples from various countries around the world uncovered a strong statistical dependence of thermal neutralities (Tn) on the mean levels of air or globe temperature (Ti) recorded within the buildings:
Tn = 2.56 + 0.83 Ti (r=+0.96) eq.1.1
It was noted that building occupants were able to find comfort, assumed to be a vote on the central category of rating scales, in indoor temperatures spanning more than 13 K. Humphreys (1975) attributed this to the adaptive processes, concluding that "... the range of recent experience is better regarded as one of the factors which will contribute to the acceptability of the environment to which the respondent is exposed."
Reasoning that indoor temperatures are dependent on outdoor temperatures to varying extents, Auliciems suggested that there might be a statistical relationship between indoor thermal neutralities and outdoor climate as well (Auliciems 1969). Parameterizing “outdoor climate” as mean monthly temperature (i.e. average of the average daily minima and average daily maximum for the month in question), Humphreys (1978) followed up Auliciems’ suggestion and found convincing evidence for adaptation to outdoor climate, as depicted in Figure 1.6. The influence of external climate on indoor neutralities is particularly evident in the results from the so called "free running" buildings which had neither centralized heating nor cooling plant (naturally
Introduction & Background page 16 MRL Australia ASHRAE RP-884 Final Report ventilated). In such buildings, the following linear regression model accounted for 94% of the variance in neutralities:
Tn = 11.9 + 0.534 Tm (r=+0.97) eq.1.2
Climate controlled (centralized HVAC) buildings, on the other hand, had a less pronounced but still highly significant correlation with outdoor mean monthly temperature
(Tm), but with a curve rather than a straight line achieving the best fit:
2 Tn = 23.9+.295(Tm-22) * exp(-((Tm-22)/(24*Ö2)) ) (r=+0.72) eq.1.3
Auliciems (1981) subsequently revised Humphreys’ regression database by deleting incompatible field studies, such as those based on asymmetric rating scales or children as subjects, and adding more recent studies that had been published after Humphreys’ (1976) paper. These revisions brought the database up to 53 separate field studies from various climatic zones in Australia, Asia, the Americas and Europe. After collapsing free running and climate controlled buildings together, the resulting equation was:
Tn = 0.48 Ti + 0.14 Tm + 9.22 (r=0.95) eq.1.4 where r is the multiple correlation coefficient. Even though the regression coefficients may be unstable in such a model due to intercorrelation between the two independent variables, equation 1.4 represents a widely cited statistical expression for the adaptive hypothesis of human thermal perception.
While the statistical association between neutralities and prevailing outdoor climate appears quite strong and convincing in Figure 1.6, the actual causal mechanism is left in doubt by such “black box” adaptive models. Apart from thermal habituation and acclimatization, there are several other plausible hypotheses, including the possibility that some unmeasured variables in the human body's heat balance were compensating for environmental temperature. For example, adjustments such as reduced clothing, metabolic and humidity levels may combine with higher air velocities in the warm climate studies (to the right-hand side of Figure 1.6) to cause subjects to experience thermal neutrality at considerably higher indoor temperatures than would otherwise have been
Introduction & Background page 17 MRL Australia ASHRAE RP-884 Final Report the case. Therefore, to more rigorously test the physiological and psychological bases of the adaptive hypothesis, these behavioral alternatives need to be eliminated, or at least accounted for. More recent field studies and experiments have done just that, by collecting simultaneous measurements of all of the input variables to Fanger’s PMV model (ISO, 1994). Such studies allow a closer look at the causal mechanisms driving thermal adaptation indoors.
32 30 28 26
24 climate controlled buildings 22 20
indoor neutrality (C) 18 free running buildings 16 14 -6 -1 4 9 14 19 24 29 34 mean monthly outdoor temperature (C)
FIGURE 1.6: The statistical dependence of indoor thermal neutralities on climate (after Humphreys, 1976)
1.4.2.2. Analysis of neutral temperatures using recent field experiments de Dear’s Ph.D. thesis, entitled “Perceptual and Adaptational Bases for the Management of Indoor Climate - A Study of Warm Climates” (1985) and subsequent ASHRAE Transactions paper (de Dear and Auliciems, 1985) reported on six thermal comfort experiments in office buildings scattered across various Australian climatic zones, ranging from equatorial (Darwin) through sub-tropical (Brisbane) to mid-latitude
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(Melbourne). The research design was premised on a consistent field method across the various climatic and building types, including instrumentation, questionnaire, protocols and analysis, thereby permitting climatic and contextual effects to be disentangled from the dozens of methodological artefacts that potentially confound earlier investigations. In both Melbourne and Brisbane, two experiments were conducted during their respective summer seasons, one in free running buildings and the other in climate controlled buildings. In total, these Australian samples included over 1100 office building occupants who cast questionnaire assessments of indoor climatic environments on 3290 separate occasions. Figure 1.7 contains the neutralities, estimated by probit analysis, for the Bedford scale in each of the six experiments, plotted against the corresponding mean monthly outdoor temperatures. Neutralities tend to increase from Melbourne's mild summer through to equatorial Darwin. This trend is most pronounced in the free running (FR) buildings (codes F for Brisbane, C for Melbourne). The Brisbane sample had the warmest neutrality in Australia at 25.6°C, while Melbourne's FR sample had the coolest at 21.8°C. The climate controlled buildings in Australia on the other hand all had neutralities clustered within the 23-24°C range.
Apart from the neutralities observed in the six Australian field experiments, neutralities predicted by Auliciems' thermal adaptive model (eq.1.4) are also shown in Figure 1.7, as are the predictions based on the PMV heat-balance model. It should be noted that these PMV predictions differ from those presented in the original publication (de Dear and Auliciems, 1985). Average clo values observed in the experiments have since been increased by 0.15 clo units to account for the insulation value of a typical office chair (Schiller 1990, Fanger and Wyon 1990, McCullough and Olesen 1994). This having the net effect of lowering the PMV model's neutrality predictions by over a full degree, which in turn halves the average prediction error down to 0.7°C (absolute value).
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Code Location & season Climate-controlled Author or Free Running P Montreal-Winter CC Donnini et al (1996) A San Francisco-winter CC Schiller et al (1988a) O Montreal-Summer CC Donnini et al (1996) B San Francisco-summer CC Schiller et al (1988a) M Townsville-Dry CC de Dear + Fountain (1994) C Melbourne-summer FR de Dear + Auliciems (1985) D Melbourne-summer CC de Dear + Auliciems (1985) E Brisbane-summer CC de Dear + Auliciems (1985) F Brisbane-summer FR de Dear + Auliciems (1985) G Darwin - Dry CC de Dear + Auliciems (1985) N Townsville-Wet CC de Dear + Fountain (1994) H Singapore FR de Dear et al (1991) I Singapore CC de Dear et al (1991) J Bangkok FR Busch (1990) K Darwin-Wet CC de Dear + Auliciems (1985) L Bangkok CC Busch (1990)
30 H J Observed 28 neutrality C) o F Adaptive 26 model I M 24 A O Static PMV P E K model D G N L 22 B Climate- C controlled
indoor neutrality ( 20 Free- running 18 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 mean outdoor temperature (oC)
Figure 1.7: Thermal comfort experiments in the field: Observed and predicted neutralities in relation to outdoor climate
Also depicted in Figure 1.7 are some results from six ASHRAE-sponsored Class I field experiments in climate-controlled buildings across a variety of climatic contexts. Two experiments are from San Francisco (Schiller et al 1988a; Schiller 1990). Another two
Introduction & Background page 20 MRL Australia ASHRAE RP-884 Final Report are from tropical Townsville (de Dear and Fountain 1994), and another pair from Montreal (Donnini et al, 1996).
Plotted along with the San Francisco observed neutralities are some predictions from Auliciems' (1983) adaptive model as well as Fanger's PMV (heat balance) model, after the effect of chair insulation (0.15 clo) has been added to Schiller et al's published clothing insulation estimates. Clearly in both seasons, the adaptive model comes very close to observation, but so too does the static heat balance model. This general pattern of consistency between neutralities observed in air-conditioned buildings and PMV predictions also extends to the more recent ASHRAE-sponsored studies in office buildings located in a hot-humid climate (de Dear and Fountain 1994) and cold climate (Donnini et al, 1996).
Busch's (1990) field experiments in office buildings in tropical Bangkok have also been included in Figure 1.7. Both climate controlled (air-conditioned) and free running buildings were studied, so a diverse range of thermal environments was covered by the sample size of 1146. For the climate controlled buildings, neutrality was established at 24.5°C (code L in Figure 1.7), within a degree of the PMV prediction based on Busch's mean clo value of 0.56 plus some chair insulation (0.15 clo). In Bangkok's free running buildings, Busch observed a neutrality of 28.5°C (code J in Figure 1.7), which appears to be over three degrees (K) warmer than predicted by Fanger's PMV. Auliciems' (1983) adaptive model, on the other hand, came within half a degree of the observed result. Busch suggested that the lighter clothing and higher local wind explain most of the disparity between observed thermal neutralities in the naturally ventilated and air- conditioned buildings, implying that behavioral adjustments were playing a strong adaptive role. But there are clearly other factors at play, as well. Noting that clothing and air velocity are used as input parameters to the heat balance models, the fact that PMV still underestimates neutrality suggests that occupants were influenced by other modes of adaptation unaccounted for by the heat balance inputs. In particular, PMV’s underestimation of thermal neutrality more significantly in the free running building sample than in the climate controlled building sample suggests that context and adaptive opportunity can influence expectations and thermal response to the indoor environment.
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Another example of this is found in a more recent field experiment, in which de Dear et al (1991c) examined climate controlled office buildings and free running residential apartment blocks in equatorial Singapore. As seen in Figure 1.7 (code I), the observed neutrality of 24.2°C in the air conditioned buildings was accurately predicted by both the adaptive and heat balance models after 0.15 clo chair insulation was added to clothing estimates. But as with Busch's Bangkok experiment, the 28.5°C neutrality observed in Singapore's naturally ventilated apartment buildings (code H) was most closely approximated by the adaptive model with a prediction of 27.2°C.
1.4.2.3. Evidence for behavioral adaptation - personal/environmental adjustment
There have been a few studies that examined direct evidence of exercised control, or adjustment. One of the earlier studies that looked closely at clothing patterns was by Fishman and Pimbert (1982), who studied 26 subjects in a UK office building for an entire year. The estimated clo values of the Watson House sample had a strong linear dependence on outdoor weather and season, especially in the case of women subjects, with a regression gradient of -0.02 clo units per degree of outdoor mean weekly temperature. This supports the hypothesis that the statistical dependence of indoor neutrality on outdoor climate may, in part, be due to behavioral adjustments that directly affect the heat balance, rather than acclimatization or habituation.
This hypothesis is also supported by the work of Humphreys (1994b) and Nicol et al (1994), in which a study of naturally ventilated buildings in North West Pakistan concluded that the office workers were comfortable across a wide range of seasonal temperatures (neutralities varying between 15.7°C in winter, and 26.4°C in summer). They also concluded that 1~B of the seasonal changes in comfort temperature could be attributed to the flexibility in the traditional Pakistani clothing worn.
Personal behavioral adjustments over time were looked at in an exploratory study by Nicol and Raja (1996) in the UK. They found that clothing changes were more strongly dependent on the succession of outdoor temperatures that occurred prior to the measurement, compared to the instantaneous or daily mean outdoor temperature, or the instantaneous indoor temperature. This suggests the importance of time-series measurements in future field studies designed to evaluate the effect of behavioral
Introduction & Background page 22 MRL Australia ASHRAE RP-884 Final Report adaptation on thermal comfort. Posture is another example of behavioral adaptation, and they found a correlation with temperature such that posture would change to increase the effective body surface area available for dry and latent heat exchange as it got warmer.
In addition to adjusting to the environment, one can directly manipulate the environment itself. Baker and Standeven (1994) used hourly questionnaires to ask whether subjects had made adjustments to their clothing or to furniture, doors, windows, shades, fans or any other part of the building to improve their comfort. Results indicated extensive occupant-environment interaction - for 23 subjects in 7 buildings, over a total of 864 hours - there were a total of 273 adjustments to controls or other environmental aspects of the room, and 62 adjustments to clothing.
The extent to which adjustments actually improve thermal comfort is as important as the frequency with which they’re made. Benton and Brager (1994) conducted a field experiment of thermal comfort in a centrally-conditioned office building in California, before and after energy-efficiency retrofit measures were installed. Adaptive opportunity was addressed by a series of questions on the availability, use, and effectiveness of coping mechanisms that either altered the physical environment or personal variables. While modification mechanisms were infrequently cited, when exercised, they consistently received high ratings for effectiveness. Behavioral mechanisms received the highest number of citations, and clothing adjustments in particular were given a relatively high effectiveness rating.
1.4.2.4. Evidence for psychological adaptation - expectation and context
While there is limited field data providing direct evidence for the effects of psychological adaptation on thermal comfort, the previous analysis of Figure 1.7 suggests that it can be implied through comparing comfort responses in different contexts. Paciuk (1990) provided a more direct analysis of the distinction between available control (adaptive opportunity), exercised control (behavioral adjustment) and perceived control (related to the psychological dimension and expectation). She found that, in addition to the traditional list of thermal inputs to the heat balance models, perceived degree of control was one of the strongest predictors of thermal comfort in office buildings, and had a
Introduction & Background page 23 MRL Australia ASHRAE RP-884 Final Report significant impact in shaping both thermal comfort and satisfaction. This finding was also supported by the work of Williams (1995), in her study in office buildings in the Northwest of England. The subjects in this study expressed higher levels of satisfaction when they perceived themselves to have more control over their environment.
Increasing levels of both perceived and available control have implications for the design of buildings, including their mechanical systems and interior layouts. A good example is shown in Figure 1.8, which comes from the English researchers Leaman and Bordass (1993). They administered a standardized indoor environmental quality questionnaire to thousands of office workers across the UK, and found a strong negative relationship between perceived control and occupant density in the workplace.
3.5
3.0 heating
2.5
ventilation 2.0
1.5
Perceived Control Rating 1.0
0.5
0.0 1 2-4 5-9 10-29 30+ Number of Occupants in Office
Figure 1.8: Relationship between the number of workers sharing an office and perceived level of control over room heating and ventilation systems. (Leaman & Bordass 1993).
This relationship also has implications for air-conditioned vs. naturally-ventilated buildings. Naturally ventilated buildings typically consist of small offices with single occupants or small groups of who are usually within reach of an operable window. This is clearly not the case in most modern air-conditioned office buildings which are
Introduction & Background page 24 MRL Australia ASHRAE RP-884 Final Report characterized by deep-space or open-plan floor layouts with dozens if not hundreds of employees being required to share the same space. The effects of this may be evident in Figure 1.7, where the naturally ventilated buildings had thermal neutralities significantly different from the predictions of heat-balance (static) models such as PMV. These same buildings probably had occupants who perceived a higher degree of personal environmental control by comparison to their counterparts in centrally air- conditioned office buildings. The poor predictive capabilities of PMV in naturally ventilated buildings suggests that adaptive processes other than behavioral adjustment (which would be accounted for in the heat balance models) must be occurring. Expectation seems the most likely explanation, since expectation has all but been eliminated by the climate-chamber method of comfort research. Within the adaptive hypothesis, such buildings would be expected by their occupants to provide variable indoor temperatures, and therefore be judged less critically than centrally air-conditioned buildings. The RP-884 data analysis will pay careful attention to the distinction between thermal perception in air-conditioned vs. naturally ventilated buildings.
Although naturally ventilated buildings might generally offer a higher level of adaptive opportunity than air-conditioned buildings, they could still differ in the actual degree of occupant control they offer. Rowe (1995a) looked at studies in 1) air conditioned buildings, 2) naturally ventilated buildings, and 3) naturally ventilated buildings with supplementary on-demand cooling and heating equipment. He found a significantly higher level of satisfaction in the naturally ventilated buildings with additional supplementary control. This led to the conclusion that people have a wider tolerance of variations in indoor thermal conditions if they can exert some control over them, and that a considerably higher level of satisfaction will be reached if occupants have means of controlling the upper and lower temperature limits. In Fishman and Pimberts’ (1982) year-long study in a UK office building, seven of the 26 subjects worked in air- conditioned areas. The rest were in naturally ventilated offices. While the sample size was not large, there was still a difference in the thermal responses of these two groups as temperatures rose above 24°C. People in the air-conditioned offices began voting much higher on the thermal sensation scale than their colleagues in the naturally
Introduction & Background page 25 MRL Australia ASHRAE RP-884 Final Report ventilated work areas, suggesting that they were less tolerant of higher temperatures and expected homogeneity in their thermal environment.
Several other researchers support this hypothesis regarding occupant expectations and their effects on thermal perception. In a study conducted by Black and Milroy (1966) in both air-conditioned and non-air-conditioned office buildings in London, occupants in the air-conditioned buildings expressed more complaints about temperature fluctuations, even though the free running building experienced much greater variability. The occupants were basing their evaluations on the benchmark of their own preconceptions of what air-conditioning should achieve, rather than on what it actually provided. In effect, this suggests that increasing levels of sophistication in environmental control systems and building services are on a treadmill of attempting to satisfy ever-increasing occupant expectations (de Dear and Auliciems 1986). Another study by Rohles et al (1977) found that Michigan subjects were more tolerant of high indoor summer temperatures (32°C ET*) than Texan subjects. Since other heat balance variables such as clothing or activity could not account for the difference, it was speculated that the Texans took summer air-conditioning for granted and came to expect or even demand cool temperatures, therefore becoming more critical of warmer indoor conditions than their northern counterparts.
1.5. Implications for RP-884
1.5.1. Lessons from static heat balance models
We believe that the split between “adaptive” and “static” heat balance models, or schools of thought, is not as irreconcilable as the protagonists have suggested. As mentioned previously, the terms "static” and “constancy" have given rise to a mistaken idea that models such as PMV and 2-node, plus the thermal comfort standards based on them, prescribe a single, constant temperature for thermal comfort the world over. But the PMV and 2-node models do, in fact, predict comfort temperatures moving in the direction of prevailing outdoor climate -- as seen in the offset of winter and summer comfort zones in the last few revisions to ASHRAE’s Standard 55. So the static model of comfort is in reality an “adaptive” model in its own right -- the fundamental distinction between the static and adaptive models is their underlying basis or postulated cause for
Introduction & Background page 26 MRL Australia ASHRAE RP-884 Final Report the shift in comfort temperatures. The former permits only behavioral adjustments (personal/technological) to heat balance variables such as clothing or air velocity, whereas the original adaptive models were premised on changing physiological (i.e. acclimatization) and psychological (i.e. expectations/habituation) setpoints. While this may seem to be a fine distinction, failure to appreciate it has, in the opinion of the authors, been responsible for unnecessary controversy between the two sides of this debate. An important contribution of the RP-884 adaptive model will be to go beyond the “black-box” approaches of the earlier adaptive models, so that we can better understanding the underlying processes of adaptive comfort.
Understanding the challenges of applying laboratory-based static models in the field can provide guidance on issues to consider when developing a new adaptive model that combines the best of both static and adaptive theories of thermal comfort. One place to start is to learn from some of the explanations that have been offered for the discrepancies between predicted and observed thermal sensations in real buildings:
1. Estimating insulation of clothing garments or ensembles. Brager et al. (1994) have demonstrated the significance of the clothing insulation estimation method on the actual clo value obtained. The ensemble insulation value differs by as much as 20% depending on whether one uses the tables and algorithms in the older or newer versions of ASHRAE Standard 55 (1981, 1992), or ISO 7730 (1994). It will therefore be important that rigorous statistical correction factors are used to create consistent ensemble clo values across the RP-884 database.
2. Accounting for the chair insulation. The tendency for PMV to overestimate thermal neutralities has been reported in several field studies (Schiller 1990), prompting Fanger and Wyon (1990) to suggest that the method of estimating clothing insulation might be systematically flawed by omission of the thermal effect that chairs have on their occupants. McCullough and Olesen (1994) responded by examining the effects of upholstered office furniture on the total thermal insulation of a heated manikin, and found that a typical office chair adds approximately 0.15 clo to the value that one gets by simple addition of individual garment values, as described in ASHRAE Standard 55-92 (ASHRAE 1992) or ISO-7730 (ISO 1994). Even if the original researchers
Introduction & Background page 27 MRL Australia ASHRAE RP-884 Final Report
supplying raw data to the RP-884 database omitted the effect of chair insulation, it will be included as part of the RP-884 analysis.
3. Non-uniformities of physical measurements. If field studies take spot- measurements of general ambient thermal parameters that are separated from the occupant’s location in space and/or time, then they might not be representative of what the occupant is actually experiencing at all (Baker 1993). This becomes particularly important in rooms with transient or spatially non-uniform thermal conditions, which are more likely to be the case in passive, or naturally ventilated buildings, or any situations where workers have high levels of personal or environmental control available to them. An analysis of adaptive comfort would best be served by using data taken close to the occupant’s location, and at the same time as the thermal questionnaire. This will be carefully considered when selecting data for inclusion in the RP-884 database.
4. Behavioral adjustments and perceived control. People adapt to the environment by adjusting their clothing or activity, modifying their posture or moving to another part of the room, opening/closing windows, operating fans or other environmental controls. But why would this cause a discrepancy between the observed and predicted conditions? In theory, static heat balance models account for clothing, activity, and thermal environmental parameters, and should therefore, be able to factor the consequences of the behavioral adjustments into their equations. Probably the most likely impact of thermal adjustments is the perception of control -- psychologists are quick to point out that adverse or noxious stimuli are less irritating if the subject perceives she/he has control over them (Paciuk 1990, Veitch and Arkkelin 1995, Kaplan and Kaplan 1982). Issues of behavioral adjustment and perceived control will be given a high priority in the RP-884 analysis, as this represents a potentially significant feedback loop between discomfort and purposive behavioral thermoregulation.
5. Thermal sensation, preference, and acceptability. Existing thermal comfort standards provide guidelines for “thermal acceptability”, while the static heat balance models on which they’re based only predict “thermal sensation”. As a result, the
Introduction & Background page 28 MRL Australia ASHRAE RP-884 Final Report
traditional approach has been to indirectly associate specific thermal sensations with “acceptability”, and to assume that thermal “preference” is synonymous with thermal “neutrality”. RP-884 will strive to include field experiments in its database that directly asked about sensation, acceptability and preference, so these assumptions can be tested.
1.5.2. Time scales of thermal adaptation
Since each class of adaptive response depends on repeated exposure to a given regime of thermal conditions, the questions of duration of exposure and lag in response seem relevant to adjustment, acclimatization and habituation adaptive processes. A review of the literature in this area will reveal, in part, which mechanisms are likely to play the most significant role in thermal response to the indoor environment and, therefore, which should receive the greatest attention in the RP-884 analysis.
The significance of the temporal dimension of thermal adaptation is realized when one considers applications of adaptive models to control algorithms for HVAC systems. Auliciems was the first to propose such an adaptive algorithm (Auliciems 1986) which he referred to as a thermobile (as opposed to a thermostat). It was premised on the adaptive model described in equation 1.4. The question of how long the averaging period for the algorithm’s temperature inputs should be was left open but, as an initial guess, Auliciems proposed that the running means, one for both indoor and outdoor temperatures, should comprise hourly observations across the preceding fortnight. More recently, Humphreys and Nicol (1995) proposed a similar adaptive algorithm for UK office temperatures. The gist of his proposed guideline is that a weighted, running mean of the preceding week’s outdoor temperature is combined with current outdoor temperature in a ratio of 3:7, thereby reflecting the overriding importance of today’s weather on clothing decisions and behavior. Humphreys proposed that this outdoor temperature index be used to specify the target indoor temperature.
Adjustment. Thermal adjustment and behavioral adaptation operate across several time scales. Cutaneous thermoreceptors provide almost instantaneous neural information about sudden changes in the thermal environment. For example, as experienced, when crossing the indoor/outdoor threshold, thus enabling clothing
Introduction & Background page 29 MRL Australia ASHRAE RP-884 Final Report adjustments and other behavioral adaptations to be effected well in advance of any significant alteration in the body’s heat balance. As for other behavioral adaptations, very little research has been published on adaptive time lags. A notable exception is a study by Humphreys (1979) on clothing adjustments at the seasonal and synoptic weather time-scales. He was able to statistically relate clothing insulation levels on any given day to an exponentially weighted moving average of outdoor temperatures on the days leading up to, and including, the day in question. It was suggested that the half-life for daytime clothing regulation was of the order of 20 hours.
Acclimatization. The literature on acclimatization reviewed earlier indicates that the physiological adaptations to heat exposure begin on the first day of exposure and progress rapidly to full development by the third or fourth day, providing the heat exposures are sufficiently severe to elevate core temperatures (Bean and Eichna, 1943; Fox, 1974). This has been achieved experimentally with daily work-in-heat regimes or hyperthermic suits. Passive exposures to heat in the course of normal day-to-day acclimatization cannot be expected to induce acclimatization responses as quickly nor as thoroughly, although Wyndham (1970) reports that passive exposures to the normal course of the seasons in South Africa induced definite signs of at least partial acclimatization. The time-scales of interest for office workers, therefore, may be of the order of weeks to months.
Habituation and expectations. Unfortunately this literature review was unable to find reference to any research on the time-scales of psychological adaptive responses, probably for the simple reason that no researchers have previously attempted to disentangle psychological from other thermal adaptive processes. However, anecdotal evidence suggest that building occupants become accustomed to levels of warmth prevailing within buildings on time scales of weeks to months. These scales translate into synoptic and seasonal processes operating in the outdoor atmospheric environment.
To summarize, the adaptive processes are operating on time scales ranging from seasonal, through synoptic to diurnal. Critics of the adaptive approach at various symposia or seminars have repeatedly asked the question: “... how long must your
Introduction & Background page 30 MRL Australia ASHRAE RP-884 Final Report people suffer in sub-optimal indoor climates before they become adapted?” Ignoring the emotive language in this question, we feel its answer, if there is one, depends on which of the adaptive processes is being relied upon. The consensus within what little has been written on the temporal dimension of adaptation is that meteorological conditions on the day in question, and to a lesser extent, the preceding week or two, exert an overriding influence on thermal adaptation in general, and clothing thermoregulation in particular. This has important implications for future field experimental protocols. While traditional research designs tend to look at responses at a given moment, experiments that intend to evaluate adaptive mechanisms need to take measurements over extended periods of time. Available evidence reviewed in this paper indicates that, in climate chamber experiments at least, the slower physiological adaptive process of acclimatization appears not to be relevant to this question of thermal neutrality and its fluctuations from day-to-day, week-to-week and season-to- season. As a result, the RP-884 data analysis and model development will focus more heavily on the adaptive mechanisms of adjustment, and habituation/expectation. This also suggests the need for field experiments in which data were rigorously obtained, including accurate measurements of air movement
1.6. Aims
The specific objectives of RP-884 can now be listed:
1. Elaborate and define adaptive processes in the context of indoor climatic perception.
2. Develop an internally consistent and quality controlled database of thermal comfort field experimental data from a variety of buildings and climates across the world. To then make this database as widely available to other thermal comfort researchers as possible.
3. Examine the semantics of thermal sensation, acceptability and preference scales within the context of an adaptive model of thermal comfort.
4. Develop statistical models of thermal comfort based explicitly on the various processes of adaptation, including adjustment, acclimatization and habituation.
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5. Explore the influence of contextual and non-thermal factors on thermal perception indoors. This investigation will include (but not be restricted to) season, building purpose (residential, office) and climatic setting, on thermal perception. This will inevitably include comparisons with the thermal comfort predictions of heat-balance models such as PMV/PPD.
6. Proposing a variable temperature standard that, in time, might eventually supplement and/or modify ASHRAE Standard 55.
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CHAPTER 2 - METHODS
2.1. Overview of the RP-884 approach
In view of the vital role played by perceptual and cognitive factors in the adaptive hypothesis, a consensus emerging from the literature is that observational data to test the hypothesis must be come from field rather than climate-chamber research. The reductionist, laboratory approach to comfort research runs the risk of stripping away those very aspects of thermal perception that are the focus of the adaptive hypothesis (McIntyre, 1982). The approach in RP-884 has, therefore, been to focus on research conducted in “real” buildings, occupied by “real” subjects going about their normal day- to-day activities rather than paid college-age subjects sitting in the highly contrived and controlled setting of the climate chamber.
In order to identify and disentangle various adaptive processes from the data, it became apparent in the research design stages of RP-884 that the field data needed to be of a high standard. The database underpinning RP-884’s adaptive models comprised field experiments where the standard of measurements, both physical and subjective, was as close as possible to laboratory-grade, and comprehensive enough to enable heat- balance indices (static model) to be calculated. Where possible, the RP-884 database comprised field experiments rather than field studies. Furthermore, the database needed to be built up from the raw data files generated by the original researchers instead of their processed or published findings. This approach allowed a variety of quality controls to be applied and enhanced the internal consistency of the entire database.
Considerable effort and resources from RP-884 and numerous field researchers around the world have been dedicated to the assembly of this database of thermal comfort field experiments. It therefore seems highly likely that the database will have numerous applications well beyond the scope and lifetime of RP-884. Therefore a decision was made to provide global and unrestricted access via the World Wide Web (WWW).
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The ultimate application of any thermal comfort model, adaptive or otherwise, is to predict the response of a given group of human subjects to a given set of input parameters (temperatures, humidity, air speeds etc). Typically this means either the occupants of an extant building, or the hypothetical occupants of a yet-to-be-built structure. Since RP-884 adaptive models are to be applied at the level of single buildings, the meta-analysis used to derive the models should be conducted at the same unit of analysis -- that of the single building. Therefore the 21,000 rows of raw data in the RP-884 database were subsequently sorted, aggregated and analyzed at the building level. Figure 2.1 is a schematic depiction of the database process, and how it evolved into the adaptive model meta-analysis. The remainder of this chapter describes the detailed steps underpinning this schematic flow chart.
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Figure 2.1: Schematic depiction of the RP-884 database process and its evolution into the adaptive model meta-analysis.
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2.2. Establishing the database for RP-884
The RP-884 database is the project’s fundamental research resource. This section describes where the raw data came from, how they were quality controlled, and what processes of data assimilation were developed to ensure internal consistency within the database.
2.2.1. Sourcing the raw data
The literature review in Chapter 1 uncovered numerous thermal field studies and experiments. Combined with the authors’ and ASHRAE TC 2.1’s knowledge of researchers currently or recently active in this area, we compiled a mailing list. An initial fax was broadcast to dozens of researchers around the world requesting information about field methods and soliciting contributions to the database (see Figure 2.3a and Figure 2.3b). On the basis of the returns to that questionnaire, a list of the contributors and their field methods was collated. Figure 2.2 depicts the geographic locations of the contributors to the RP-884 database. Data came from four continents and a broad spectrum of climatic zones.
Figure 2.2: Geographic origins of the raw data contributions to RP-884 world database of thermal comfort field research
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Table 2.1: Sources of raw data for the RP-884 world database of thermal comfort
Researcher File Experiment Location Building Research Sample No. of No. Type Design Size Blgds Jill Brown (U of Wales - UK) 1 South Wales, UK (summer) HVAC cross-sectional 80 4 Jill Brown (U of Wales - UK) 2 South Wales, UK (winter) HVAC cross-sectional 38 4 John Busch (LBL) 3 Bangkok, Thailand (Hot season) HVAC cross-sectional 776 2 John Busch (LBL) 4 Bangkok, Thailand (Hot season) NV cross-sectional 392 3 Benton + Brager (ACT2) 5 Antioch, California (winter) HVAC longitudinal 111 1 Tri Karyono (Sheffield, UK) 6 Jakarta, Indonesia (summer) HVAC cross-sectional 458 5 Tri Karyono (Sheffield, UK) 7 Jakarta, Indonesia (summer) NV cross-sectional 97 1 Tri Karyono (Sheffield, UK) 8 Jakarta, Indonesia (summer) Mixed cross-sectional 41 1 Donnini ASHRAE RP-821 9 Montreal, Canada (summer) HVAC cross-sectional 443 12 Donnini ASHRAE RP-821 10 Montreal, Canada (winter) HVAC cross-sectional 426 11 de Dear (PhD data) 11 Brisbane, Australia (summer) HVAC cross-sectional 564 5 de Dear (PhD data) 12 Brisbane, Australia (summer) NV cross-sectional 611 5 de Dear (PhD data) 13 Darwin, Australia (dry season) HVAC cross-sectional 493 8 de Dear (PhD data) 14 Darwin, Australia (Wet season) HVAC cross-sectional 555 7 de Dear (PhD data) 15 Melbourne, Australia (summer) HVAC cross-sectional 512 4 de Dear (PhD data) 16 Melbourne, Australia (summer) NV cross-sectional 555 3 Guy Newsham (Canada NRC) 17 Ottawa, Canada (winter) HVAC longitudinal 1859 4 Nicol, Fergus (Oxford-Brooks U) 18 Karachi, Pakistan (summer) NV longitudinal 190 1 Nicol, Fergus (Oxford-Brooks U) 19 Karachi, Pakistan (winter) NV longitudinal 470 1 Nicol, Fergus (Oxford-Brooks U) 20 Multan, Pakistan (summer) NV longitudinal 437 1 Nicol, Fergus (Oxford-Brooks U) 21 Peshawar, Pakistan (summer) NV longitudinal 556 1 Nicol, Fergus (Oxford-Brooks U) 22 Peshawar, Pakistan (winter) NV longitudinal 513 1 Nicol, Fergus (Oxford-Brooks U) 23 Quetta, Pakistan (summer) NV longitudinal 492 1 Nicol, Fergus (Oxford-Brooks U) 24 Quetta, Pakistan (winter) NV longitudinal 425 1 Nicol, Fergus (Oxford-Brooks U) 25 Saidu, Pakistan (summer) NV longitudinal 568 1 Nicol, Fergus (Oxford-Brooks U) 26 Saidu, Pakistan (winter) NV longitudinal 548 1 Nick Baker, Cambridge UK 27 Athens, Greece (summer) NV longitudinal 1626 6 Raja, Ifitkhar (Oxford-Brooks U) 28 Oxford, UK (summer) NV longitudinal 877 3 David Rowe (U Sydney) 29 Sydney, Australia (summer) mixed longitudinal 137 1 David Rowe (U Sydney) 30 Sydney, Australia (winter) mixed longitudinal 170 1 Dav id Rowe (U Sydney) 31 Sydney, Australia (winter) HVAC cross-sectional 83 1 Gail Brager ASHRAE RP462 32 Bay Area, California (summer) HVAC mixed 673 7 Gail Brager ASHRAE RP462 33 Bay Area, California (summer) NV mixed 360 3 Gail Brager ASHRAE RP462 34 Bay Area, California (winter) HVAC mixed 923 7 Gail Brager ASHRAE RP462 35 Bay Area, California (winter) NV mixed 393 3 de Dear & Fountain 702-RP 36 Townsville, Australia (Dry season) HVAC cross-sectional 628 12 de Dear & Fountain 702-RP 37 Townsville, Australia (Wet season) HVAC cross-sectional 606 11 Ruth Williams (BSRIA - UK) 38 Merseyside, UK (summer) NV cross-Sectional 167 3 Ruth Williams (BSRIA - UK) 39 Merseyside, UK (winter) NV cross-Sectional 209 5 Ruth Williams (BSRIA - UK) 40 Merseyside, UK (winter) Mixed cross-Sectional 121 1 de Dear, Foo and Leow 41 Singapore (summer) HVAC cross-sectional 333 1 de Dear, Foo and Leow 42 Singapore (summer) NV cross-sectional 583 1 Bauman et al (Steelcase) 43 Grand Rapids, Michigan (winter) HVAC mixed 85 1 Benton + Brager (ACT2) 44 San Ramon, CA (summer) HVAC longitudinal 96 1 Benton + Brager (ACT2) 45 San Ramon, CA (winter) HVAC longitudinal 285 2 Benton + Brager (ACT2) 46 Auburn, CA (winter) HVAC longitudinal 128 1 TOTAL TOTAL 20693 160
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Figure 2.3a: The RP-884 thermal comfort research methods questionnaire sent to active field researchers around the world
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Figure 2.3b: The RP-884 thermal comfort research methods questionnaire sent to active field researchers around the world
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2.2.2. Ratings of raw data submitted to RP-884
Field data were classified according to the standard of instrumentation and procedures used for indoor climatic measurements. Three broad classes of thermal comfort field investigation were defined as follows:
· Class III: Field studies based on simple measurements of indoor temperature and possibly humidity. One level of measurement above the floor. Possibly asynchronous and non-contiguous physical (temperature etc.) and subjective (questionnaire) measurements. The field studies used to derive the previously published adaptive models (Humphreys, 1976, 1978, 1981; Auliciems, 1981) were all Class III.
· Class II: Field experiments in which all indoor physical environmental variables (ta,
tr, v, rh, Icl, met) necessary for the calculation of SET* and PMV/PPD indices were collected at the same time and place as the thermal questionnaires were administered. Measurements may not have been made at the three heights above floor level as specified in ASHRAE (1992) and ISO (1994) standards (0.1, 0.6 and 1.2m). Humidity measurements were taken by aspirated psychrometer or solid state hygrometer sensors. Air speeds were measured by hot wire (or sphere) probes with thresholds above 0.1 ms-1, directional sensing elements and time constants larger than that necessary for turbulence intensity, Tu, assessments.
· Class I: Field experiments in which all sensors and procedures were in 100% compliance with the specifications set out in ASHRAE Standard 55 (1992) and ISO 7730 (1984). In particular, all of the shortcomings identified in Class II investigations were absent from Class I field experiments. Three heights of measurement with laboratory-grade instrumentation including omnidirectional anemometry capable of turbulence intensity assessments. The three ASHRAE-sponsored field experiments in the San Francisco Bay Area (RP-462), Townsville (RP-702) and Montreal (RP- 921) are examples of Class 1 investigations.
Also listed in Appendix C is a comprehensive summary of each field project adopted in the ASHRAE RP-884 database. Information listed includes: original researchers’ names, class of data (I, II or III); publications; field location, climate and season; description of sample buildings; indoor climatic instruments; questionnaire details, and
Methods page 40 MRL Australia ASHRAE RP-884 Final Report outdoor meteorological/climatological data sources. In addition there is a detailed section explaining the RP-884 standardization steps and procedures that were applied to each project’s raw data before they were assimilated into the cumulative database.
2.3. Raw data standardisation
Individual researchers each have their own detailed methods, but thankfully these idiosyncrasies are largely transparent to the readers of their final research publications. However, in an exercise involving the assembly of a database from raw data, the emphasis must be on standardization. In the present case this has not been easy since the decision to assemble a database occurred after the original data were collected (except in the case of the ASHRAE field RPs). This section describes some of the more important steps in this process of data assimilation.
2.3.1. Creation of a standard data template
A standard template of variables was developed, based on previous ASHRAE-funded research projects, particularly RP-702 (hot-humid), RP-462 (Mediterranean) and RP- 821 (cold climate). This template was applied to each and every row of data in the RP- 8884 database (n~21,000). The standard template consisting of units of measurement, codenames and coding conventions is presented in Appendix E. The template is broken down into the following groups of variables: · Basic Identifiers such as building code, subject information and date. · Thermal Questionnaire comprising sensation, acceptability and preference scales, as well as activity, metabolic rates, clothing and chair insulation. · Indoor Climate Physical Observations of air temperature, globe temperature, air velocity and turbulence at three heights, plus dewpoint, rh and plane radiant asymmetry temperature. · Calculated Indices, including averaged single height measurements of air temperature, mean radiant temperature, air velocity; operative temperature, turbulence intensity, vapour pressure and relative humidity; new effective temperature, new standard effective temperature, TSENS, DISC, predicted mean vote, predicted percentage dissatisfied and draft risk at three heights and maximum.
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· Personal Environmental Control covering questions of perceived control (including a composite index that could be applied to buildings where the questionnaire did not cover perceived control), and specific adaptive opportunities. Options include: windows, internal doors, external doors, thermostat, curtains/blinds, local heaters and fans. · Outdoor Meteorological Observations include raw data and derived indices. Daily temperatures and relative humidities at 600 hours and 1500 hours were collected, and daily effective temperatures (ET*) for these times calculated with WinComf© (Fountain and Huizenga, 1996). Daily averages for air temperature, relative humidity and effective temperature were also calculated.
2.3.2. Consistent mean radiant temperatures within the database.
Mean radiant temperature was recalculated from each row of data using the ASHRAE HoF formula (1993), based on raw globe and air temperatures plus air speed.
8 0.6 1 é 4 1.10 · 10 V ù 4 t = (tg + 273) + (tg - ta) - 273 r ëê e · D0.4 ûú
where e is emissivity (0.95 for a black globe), D is globe diameter (0.04 m for “ping-pong”), V is air speed in m s-1, o ta is air temperature in C, o tg is globe thermometer’s temperature in C. N.B. the globe thermometer has a lagged response and requires about 10 to 15 minutes to equilibrate. Larger diameter globes have longer lags.
2.3.3. Consistent comfort index calculations within the database
With models as complex as PMV and SET*, it is to be expected that several different algorithms and implementations exist in engineering and research circles around the world. ASHRAE TC 2.1 has recently acknowledged this potential source of “noise” in comfort research and engineering applications, and has sought to standardize the models into a single software package (ASHRAE RP-781) now known as WinComf©
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(developed by Fountain and Huizenga, 1996). From this software, new effective temperature (ET), new standard effective temperature (SET), the two-node temperature sensation index (TSENS), the two-node discomfort index (DISC), Fanger’s Predicted Mean Vote (PMV) and Fanger’s Predicted Percentage Dissatisfied (PPD) were obtained for each row of data in each field experiment within the database, provided the necessary input data were available. Requisite input data include: air temperature (average of three heights, TAAV), mean radiant temperature (average of three heights, TRAV), air speed (average of three heights, VELAV), relative humidity (RH), metabolic rate (MET) and insulation afforded by clothing and chair (INSUL). Where only one measurement height was available in the raw data files, it was treated as the average. If TRAV values were missing TAAV was often substituted in its place.
2.3.4. Predicted draft risk index (PD)
Predicted Draft Risk was not taken from the WinComf© software. The following formula was used:
0.62 PD = (34 - ta) * (v - 0.05) * (0.37 * v * Tu + 3.14) o ta = indoor air temperature ( C) v = indoor air velocity (m s-1) Tu = turbulence intensity (%)
ta, v and Tu were provided at three heights (0.1m, 0.6m and 1.1m above the floor). Where possible PD was calculated at the three heights and designated as PD_L, PD_M and PD_H. The maximum of these was then taken as the value of the index
(PD_MAX) for subsequent analyses. Where ta, v and Tu were only recorded at one height the resulting PD defaulted to PD_MAX. Where no turbulence was provided in the raw data files, the default of 40% was substituted in calculations and if the velocity was < 0.05 m s-1 the default value for PD was zero.
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2.3.5. Clothing insulation in the ASHRAE RP-884 database
Clothing insulation has remained one of the more troublesome parameters for field researchers. It is often singled out as an explanation whenever observed comfort responses of building occupants depart from the predictions of thermal comfort standards and indices. The insulation value of a particular ensemble of clothing can only be measured with any precision using a thermal manikin in a controlled climate chamber over several hours, and even then the answer varies considerably between manikins. Little wonder, therefore, that the estimation of clothing insulation has presented HVAC practitioners with confusion when applying comfort theory in the field, encouraging them to think of thermal comfort simplistically in terms of single set-point temperatures instead of multidimensional comfort zones.
But it is not only practitioners who have trouble with clothing insulation -- even researchers face difficulties due to a complicated variety of garment insulation databases and equations available to derive whole ensemble insulation values. Adding further confusion, even the standards themselves (ISO 7730 and ASHRAE 55) have recommended different techniques and equations between their various revisions. This leads to the surprising situation where a given set of clothing may get quite different clo estimates attached to it, depending on which standard and which edition is used.
From the RP-884 perspective, clothing represents one of the key thermal adaptive responses. Clothing is a behavioral adjustment that directly affects the heat-balance. Therefore this important parameter demanded careful treatment within the RP-884 database. The main approach here has been to establish statistical “conversion factors” between the various clo estimation techniques based on a large sample of clothing data from the ASHRAE RP-462 project in San Francisco (Schiller et al., 1988a,b), and to use these to convert all clo data within the RP-884 database into equivalent ASHRAE Standard 55-92 (ASHRAE, 1992) intrinsic ensemble clo estimates.
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2.3.5.1. Discrepancies between field estimation methods for clo.
Brager et al. (1994) indicated that conversions from ASHRAE Standard 55-81 to 55-92 clo estimates lifted their sample average ensemble insulation by 0.1 clo, alerting the RP-884 team to the possible difficulties in comparing field data from several different researchers around the world. Because its raw data file, as supplied to the RP-884 team, contained individual clothing garment values (CL1 through CL15) instead of the usual aggregate ensemble insulation estimates, the ASHRAE RP-462 project (Schiller et al 1988a,b) afforded a unique opportunity to quantitatively compare clothing ensemble insulation estimates based on several different techniques with each being benchmarked against the ASHRAE Standard 55-1992 method:
· Sprague and Munson (McIntyre 1980),
· ASHRAE Standard 55-1981,
· ISO7730 1984,
· ISO7730 1994,
For each subject in the RP-462 field experiment spreadsheets, we estimated the ensemble clothing insulation using each of the techniques listed above, and also the ASHRAE 55-1992 technique. Following are some regression models fitted to the relationships between the ASHRAE 55-92 estimates, and the other four techniques in turn. The two genders were analyzed separately and all regression equations were forced through the zero origin so that the fitted models could be applied throughout the RP-884 database as simple conversion coefficients.
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Male Clothing Estimate from RP-462 data Female Clothing Estimate from RP-462 data 2.0 2.0 1.8 1.8 1.6 1.6 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 ASHRAE 55-92 clo ASHRAE 55-92 clo 0.4 0.4 ASH55-92 = 1.0938 * ASH55-81 ASH55-92 = 1.2362 * ASH55-81 0.2 2 0.2 2 R = 0.8072 R = 0.6073 0.0 0.0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 ASHRAE 55-81 clo ASHRAE 55-81 clo
Figure 2.4: Relationships between ASHRAE 55-81 and ASHRAE 55-92 clo estimates in the RP-462 field experiment
Figure 2.4 depicts the ASHRAE 55-81 Þ ASHRAE 55-92 models for the RP-462 data files. The male samples’ model had 832 data points and was highly significant (F=3,478; p=0), explaining 81% of variance (r = 0.90). The actual model had a regression coefficient of 1.094 (95% confidence interval 1.086 - 1.102), indicating that clothing insulation estimates using the new method described in ASHRAE Standard 55- 92 were, on average, 9.4% higher than those obtained for the same male subjects using the ASHRAE 55-1981 methods.
The RP-462 female samples’ model had 1,508 data points in Figure 2.4 and was also highly significant (F=2,330, p=0) with 61% explained variance (r = 0.78). The regression coefficient was 1.236 (95% confidence interval 1.224 - 1.249), indicating that clothing insulation estimates using the ASHRAE Standard 55-92 estimation method were, on average, 23.6% higher than those obtained using the ASHRAE 55-1981 methods.
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Male Clothing Estimate from RP - 462 data Female Clothing Estimates from RP - 462 data 2.5 2.5 ASH55-92 = 1.0921 * McIntyre R2 = 0.7851 2.0 2.0
1.5 1.5
1.0 1.0
0.5 2 0.5 ASHRAE 55 - 92 CLO ASH55-92 = 1.1979 * McIntyre + 0.219 * McIntyre ASHRAE 55 - 92 CLO R2 = 0.9166 0.0 0.0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 McIntyre (Sprauge and Munson) CLO McIntyre (Sprauge and Munson) 1980 CLO
Figure 2.5: Relationships between Sprague & Munson (McIntyre 1980) and ASHRAE 55-92 clo estimates in the RP-462 field experiment
Figure 2.5 depicts the regression models fitted to the relationship between the Sprague and Munson clo method (reported in McIntyre, 1980) and the ASHRAE 55-92 method. The curvilinear relationship for male subjects was best approximated by a 2nd order polynomial regression model which managed to account for 92% of the variance in Standard 55-92 estimates (r=0.96). The female subjects in RP-462 had their ASHRAE 55-92 clothing ensemble insulation estimates were systematically larger than the Sprague and Munson estimates by a factor of 9.2% and the linear relationship between the two estimation methods had a correlation coefficient of r=0.89.
Male Clothing Estimates from RP-462 data Female Clothing estimates from RP-462 data 2.5 2.5 ASH55-92 = 1.057 * ISO-84 R2 = 0.8476 2.0 2.0
1.5 1.5
1.0 1.0
0.5 ASRAE 55-92 clo 0.5 ASHRAE 55 - 92 CLO ASH55-92 = 0.3839 * ISO-842 + 0.6579 * ISO-84 R2 = 0.9518 0.0 0.0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 ISO 7730 1984 CLO ISO 7730 (1984) clo
Figure 2.6: Relationships between ISO 7730 (ISO, 1984) and ASHRAE 55-92 clo estimates in the RP-462 field experiment
Figure 2.6 depicts the relationships between RP-462 clothing ensembles insulation estimates using the ISO 7730 (1984) and ASHRAE 55-92 methods. The male subjects’
Methods page 47 MRL Australia ASHRAE RP-884 Final Report clothing was described by a second order polynomial regression which explained 95.2% of the variance (r=0.98). The female subjects’ model was a simple linear regression with the ASHRAE 55-92 estimates being, on average, 5.7% higher than the ISO 7730 (1984) estimates, and the relationship accounting for 84.8% of variance (r=0.92).
Male Clothing Estimate from RP - 462 data Female Clothing Estimates from RP-462 data 2.5 2.5 ASH55-92 = 1.0049 * ISO-94 2 2.0 2.0 R = 0.8814
1.5 1.5
1.0 1.0
0.5
ASHRAE 55-92 CLO 0.5 ASHRAE 55-92 CLO ASH55-92 = 0.3954 * ISO-942 + 0.6954 * ISO-94 R2 = 0.9448 0.0 0.0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 ISO 7730 -1994 CLO ISO 7730 - 1994 CLO
Figure 2.7: Relationships between ISO 7730 (ISO, 1994) and ASHRAE 55-92 clo estimates in the RP-462 field experiment
Figure 2.7 above depicts the regression relationships between RP-462 clothing ensemble insulations values estimated by the ISO-7730 2nd edition (1994) and ASHRAE 55-92 methods. The male subjects’ clo estimates were approximated again with a second order polynomial which accounted for 94.5% of the variance (r=0.97). The females’ regression model was a simple linear one (r=0.94) with the ASHRAE 55- 92 clo estimates being about half a percent higher than the ISO 7730 (1994) counterparts.
Unlike the RP-462 raw data files, the remainder of the raw data contributions to ASHRAE RP-884’s database contained only total ensemble insulation estimates, therefore ruling out any systematic garment-by-garment conversions and trends of the type performed for RP-462 above. However, a preliminary questionnaire sent to each of the RP-884 database contributors enquired about their method of clo estimation (see Figure 2.3a and Figure 2.3b). Where the method used was pre-ASHRAE Standard 55- 1992, the original researchers’ clo estimates in their raw data file were simply scaled up or down to equivalent Standard 55-92 levels using the conversion factors (regression models) described above in Figures 2.4 through 2.7.
Methods page 48 MRL Australia ASHRAE RP-884 Final Report
2.3.5.2. The chair insulation effect
The preliminary questionnaire on field methods sent to all database contributors (Figure 2.3a and Figure 2.3b) also enquired into whether or not the incremental insulation effect of furniture was included in their clo estimates. If omitted from the original estimates, an additional 0.15 clo was added, after the regression correction to clothing insulation had been performed (McCullough and Olesen, 1994; de Dear, 1994). While it is recognised that all chairs, stools, sofas, and any other horizontal surface which might have acted as a chair at the time of questionnaire for the 21,000 subjects in the RP-884 database may not have provided exactly 0.15 clo insulation at the time of interview, we feel inclusion of this “best estimate” is preferable to omitting the effects of chairs altogether.
2.4. Developing an index for perceived thermal control
Adaptive opportunity and perceived control figured prominently throughout the literature review in Chapter 1, but unfortunately, only a handful of original field experiments supplied to the RP-884 database actually recorded these data in their survey buildings. Therefore the development of a method for estimating this parameter across all buildings within the RP-884 database was given a high priority. This section describes the assumptions and steps we made to achieve this goal.
· Step 1: Find a data base possessing both a global perceived control item (PCC) in the original questionnaire as well as individual items on specific adaptive opportunities (PCEC1 through PCEC7). The ASHRAE-sponsored RP-702 (hot- humid Townsville) and RP-821 (Cold climate Montreal) fulfilled these requirements.
· Step 2: Classification of the adaptive opportunities (PCEC variables) according to their relevance to season (See Table 2.2). For example, access to windows was not regarded as a relevant thermal control during winter months, whereas access to thermostats was deemed relevant all year round.
Methods page 49 MRL Australia ASHRAE RP-884 Final Report
Table 2.2: Thermal adaptive opportunities classified according to their relevance to perceived control in summer and winter seasons PCEC variables Can You Control Season PCEC1 windows summer PCEC2 external doors summer PCEC3 internal doors summer/winter PCEC4 thermostats summer/winter PCEC5 curtains/blinds summer/winter PCEC6 local heaters winter PCEC7 local fans summer
· Step 3: For the summer index, we found all cases in the database that had control over operable windows ( PCEC1= 1) but none of the other adaptive opportunities (PCEC2 through PCEC7). We then found the average of the overall perceived control variable (PCC) for this subset of the database with control over windows. Similar perceived control averages for each of the other adaptive opportunities (PCEC variables) were obtained
· Step 4: For each of the 21, 000 subjects in the RP-884 database, we summed the relevant perceived control scores (Step 3) for all adaptive opportunities they had at their disposal. The aggregate score resulting from this step was entered in the database as PCC_AG.
We can see from Table 2.3 that, in both summer and winter, the most efficacious adaptive opportunity is “thermostats,” with “internal doors,” “curtains/blinds,” “external doors,” “local heaters” and “local fans” all rating approximately equal for their designated season. In summer, windows also contribute significantly to building occupants’ perceived control.
Methods page 50 MRL Australia ASHRAE RP-884 Final Report
Table 2.3: Thermal adaptive opportunities scored according to their influence on perceived thermal control in summer and winter PCEC variables Can You Control PCC score in PCC score in Winter Summer season Season PCEC1 windows 1.6 PCEC2 external doors 1.3 PCEC3 internal doors 1.3 1.3 PCEC4 thermostats 1.8 2.0 PCEC5 curtains/blinds 1.3 1.4 PCEC6 local heaters 1.3 PCEC7 local fans 1.5
2.5. Thermal acceptability issues within the RP-884 database
2.5.1 Developing a proxy variable for thermal acceptability based on thermal sensation votes.
The thermal comfort standards such as ASHRAE’s Standard 55 and ISO 7730 are couched in terms of maintaining certain levels of thermal acceptability within a building. Unfortunately specific questionnaire items on thermal acceptability such as this (TSA):
“Is this environment thermally acceptable to you at this point in time?” were available in only a small subset of buildings in the RP-884 database, but a proxy could be inferred from thermal sensation votes (ASH), which were recorded in all studies. It has generally been assumed that a thermal sensation vote within the central three categories of the ASHRAE scale is acceptable. Translating to the real-number version (as opposed to the integer only version) of the scale, this criterion for acceptability was defined as a thermal sensation vote falling in the interval - 1.5 Methods page 51 MRL Australia ASHRAE RP-884 Final Report percentages can be treated in the meta-analysis as a thermal acceptability rating for the building in question. 2.5.2. Rating buildings in terms of their compliance with ASHRAE Standard 55 acceptable indoor climate guidelines If we are prepared to ignore the upper and lower humidity boundaries of the summer and winter comfort zones depicted in ASHRAE Standard 55-92 (which may be justified in view of the ongoing debate as to what they should actually be -- see Berglund, 1995), it was a relatively simple task to assess each RP-884 database building’s percentage of indoor climate measurements falling within either the summer or winter ASHRAE comfort zone (ASH55_92). This also depends on which season the building was surveyed in. It was done for buildings assessed during the cooling season (summer), with: 23°C <= indoor ET* <= 26°C and for the heating season (winter), with: 20°C <= indoor ET* <=23.5°C The resulting percentages for each building can be regarded in the RP-884 meta- analysis as an index of the compliance with the ASHRAE Standard 55 thermal acceptability prescriptions. 2.6. Outdoor meteorological/climatological data for the data base Obviously outdoor weather and climate represent key components of any conceivable adaptive model of thermal comfort since outdoor climate partly drives acclimatization, behavioral and psychological adaptive responses. 2.6.1. Appending outdoor weather observations to each row of data For those studies supplied to the RP-884 database without weather data, the first priority was to obtain meteorological data (weather data recorded on exactly the same dates as the indoor observations). If that was not possible, then climatological data were used (i.e. data from published sources covering long-term statistical averages for the months in question). The outdoor atmospheric parameters collected for the RP-884 database consisted of daily outdoor air temperature and coincident relative humidity at Methods page 52 MRL Australia ASHRAE RP-884 Final Report 6:00 am and 3:00 pm. These times were selected because they represent typical times of occurrence of daily minimum and maximum temperature. The times also typically correspond with daily maximum and minimum relative humidity. Various national weather and climate data resources searched and used included: · The US National Climatic Data Center (NCDC) which currently maintains an on-line US climatic data archive on the World Wide Web (INTERNET) from which it is possible to download data via the Hyper-Text Transfer Protocol (HTTP). · Commercially available CD ROMS such as the International Station Meteorological and Climate Summary (ISMCS, 1992) proved most useful in filling some of the gaps in the RP-884 meteorological/climatological data base. · Published Climatological data resources such as in academic journal Weather were used to obtain maximum and minimum temperatures (with relative humidity supplied by ISMCS) for many of the UK field experiments. · In two cases, meteorological data were supplied gratis by weather stations on university campuses. These were the Radcliffe Observatory at Oxford University, UK, and the Physical Geography Met Site at Macquarie University in Sydney Australia. · For those investigations in which the actual outdoor meteorological data were either unavailable from previously listed sources, or at the wrong temporal resolution, it was necessary approach the relevant State or National Climatologists (weather bureaux) for raw data. This was done for the Australian field experiments in Brisbane, Darwin and Melbourne, for all the Californian field experiments, and the Steelcase project in Michigan. 2.6.2. Climate classification applied to RP-884 raw data A relatively simple and descriptive climate classification developed for the Macquarie University undergraduate teaching program in climatology was applied to the RP-884 database. A map of the classification can be seen in Appendix D. Methods page 53 MRL Australia ASHRAE RP-884 Final Report 2.7. Subdivision of the standardized field experiments Once the field experiments supplied by original researchers had been quality controlled and standardized into the RP-884 database template, they were broken down according to season (summer/winter) and building type (centrally controlled HVAC buildings, naturally ventilated buildings NV, and mixed-mode buildings). Here the distinction between centrally-controlled HVAC buildings and naturally ventilated buildings is that in central HVAC buildings individual occupants have little or no control over their imediate thermal environment, while occupants in naturally ventilated buildings at least have control over operable windows. See Table 2.1 and the “sample buildings” sections for of each project summary in Appendix C. 2.8. The meta-analysis By aggregating the statistical unit of analysis up from the individual subject to whole buildings, the RP-884 was able to reduce the 21,000 cases in the database to 160 buildings. This section describes how the aggregation was performed and how the resulting meta-file was used as the basis for developing adaptive models. 2.8.1. The unit of analysis for the RP-884 meta-analysis Earlier attempts at defining adaptive models (Humphreys and Auliciems) typically aggregated data up to the unit of an entire field study, which often incorporated many different buildings. Therefore the early adaptive models may have glossed over considerable variety in contextual factors affecting subjective responses. While each record within the RP-884 database was structured as one individual subject’s thermal questionnaire, indoor climatic physical measurements, thermal index values and outdoor meteorological observations, the most appropriate unit of analysis for the statistical modelling part of the project is the single building. This level of aggregation masks some of the inherent noise involved in a single subject’s thermal comfort assessment, while still providing sufficient data points for statistical modelling purposes. Furthermore, several important parameters such as neutrality and preferred temperature can only be sensibly derived from a group’s response. Data analysis at the level of Methods page 54 MRL Australia ASHRAE RP-884 Final Report buildings rather than individuals also ensured a modicum of consistency across several contextual factors relevant to thermal adaptive processes, including : · type of HVAC system, · degree of personal environmental control, · job satisfaction and other managerial factors that might impinge upon thermal comfort, · temporal variability of internal temperatures in the days/weeks preceding the thermal comfort experiment, · mean levels of outdoor meteorological factors and their variability in the days/weeks preceding the comfort experiment. In total there are 160 individual buildings in the RP-884 database. 2.8.2. Meta-file’s structure and coding conventions The meta-file included country, city, and season in which the field experiment was conducted. Data quality and intensity of measurement were also recorded, as was building type (HVAC, NV, mixed-mode). Following these descriptors are means and standard deviations of questionnaire responses (e.g. ASHRAE sensation votes and thermal environmental measurements plus derived indices). In addition there are the derived products such as the building’s observed thermal neutrality, preferred temperature and thermal acceptability rating. The full listing of variables in the meta-file and their coding conventions can be found in Appendix F. 2.8.3. General assumptions within the statistical meta-analysis · For the purpose of statistical analysis in RP-884, field experiments with longitudinal research design (few subjects, sampled many times) were assumed to have independence between subjects. That is, longitudinal studies were treated the same way as cross-sectional research designs during the meta-analysis. We also accepted that all other statistical assumptions of linearity, normality and equality-of- variance applied across the data base. Methods page 55 MRL Australia ASHRAE RP-884 Final Report · For all statistical modelling conducted on the meta-file, each building “data point” was weighted according to the number of human subjects it represented (i.e. sample size within the building). The purpose of using a weighting factor was to minimise the impact of outlying data points that were based on relatively small number of observations. · Statistical products such as building neutrality or preferred temperature were appended as new variables in the meta file. However, if the statistical model or test in question failed to reach statistical significance at the p=0.05 level of or better, the building registered a missing value code for that particular variable in the meta file. · Test statistics based on small sample sizes were interpreted with care or eliminated (i.e. coded as “missing values”) due to their wide confidence interval estimates. 2.8.4. Statistical treatments on the various subjective thermal ratings There are some common features in the methods used in thermal comfort field work, particularly in relation to their assessmetns of subjective warmth within buildings. The most common approach has been the rating scale method in which comfort is operationalized as a vote of "neutral" or "comfortable" on scales such as those depicted in Table 2.4. Shading has been used in the table to indicate the commonly assumed mapping between rating scales and other thermal assessments. That is, “neutral” is generally assumed within the comfort research community to be synonymous with “comfortable”, “acceptable,” and “preferred.” Despite the apparent semantic differences between the ASHRAE scale of thermal sensation and the Bedford comfort scale, these two scales have been found to behave more-or-less the same in most practical situations (McIntyre, 1978a; de Dear, 1985). This encouraged direct comparisons in this project between studies using either scale. But recent analyses of questionnaire studies in which acceptability, preference and thermal sensation were recorded simultaneously reveal that the optimum temperature based on thermal sensation votes does not correspond exactly with that derived from thermal preference or acceptability (Brager, 1994). Therefore thermal acceptability and preference were analyzed separately wherever possible. Methods page 56 MRL Australia ASHRAE RP-884 Final Report Table 2.4: Common rating scales used in comfort research in the field ASHRAE scale Bedford scale Acceptability Preference (McIntyre) 3 hot much too warm 2 warm too warm unacceptable want cooler 1 slightly warm comfortably warm 0 neutral comfortable acceptable no change -1 slightly cool comfortably cool -2 cool too cool unacceptable want warmer -3 cold much too cool The ambient temperature found by statistical analysis to most frequently coincide with the central, usually “neutral” or “comfortable,” rating in a thermal comfort study is referred to as that sample's "neutrality". Neutrality was calculated in the meta-analysis by the following steps: · Binning a particular building’s observations into half-degree (K) increments, and working with the bins’ mean response, say thermal sensation vote, instead of individual subjects’ thermal votes. · Fitting a linear regression model between thermal sensations and whatever the x-axis thermal index may be (TOP, ET, SET, PMV). The regression models weighted each point according to the number of observations within each x-axis bin. The regression models had the general form: mean thermal sensation = a + b * (bin index value) The following statistical details of each building’s four regression models (TOP, ET, PMV, SET) were extracted for the meta-analysis: · gradient (b) of each regression model, a measure of thermal sensitivity, · the neutrality of the model, i.e. solution of the linear equation for a mean thermal sensation value of zero, or “neutral,” · the range of index values corresponding with 80% “acceptable” thermal sensations, i.e. the distance between solutions of the linear equation corresponding with mean Methods page 57 MRL Australia ASHRAE RP-884 Final Report thermal sensations of -0.85 (close to the mean vote of “slightly cool”) and +0.85 (close to “slightly warm”), · The range of index values corresponding with 90% “acceptable” thermal sensations between solutions of the linear equation corresponding with mean thermal sensations of -0.5 and +0.5. These boundaries were selected on the assumption that the normal distribution of thermal sensations recorded within each building resembled that of Fanger’s PPD function (1970). So a mean vote of ±0.85 was assumed to correspond with 80% general acceptability (20% dissatisfaction, excluding local discomfort), ie. 80% of votes falling inside the central three categories. A mean vote of ±0.5 was assumed to correspond with 90% general acceptability (10% dissatisfaction). Apart from statistically deriving observed neutralities for each building with the procedures above, the meta-file also contains predicted neutralities for each building on the basis of heat-balance models. The model used for this purpose was Fanger’s (1970) PMV index, and it was applied to the problem in the following way: · Each building’s mean values for each of the five PMV variables (TOP, RH, VEL, INSUL, MET) were input to the WinComf© software (Fountain and Huizenga, 1996). · The PMV model was solved iteratively by adjusting TOP (ta with tr linked) until the PMV output field equalled zero. The final operative temperature corresponding with PMV=0 is, by definition, predicted neutrality (PREDNEUT) for that particular building. · One additional variable named DELTNEUT was derived from the PMV model -- the difference between observed and predicted neutralities (NEUT_TOP and PREDNEUT respectively). PREDNEUT was subtracted from NEUT_TOP, so that if a particular buildings occupants were neutral in temperatures warmer than expected by the PMV model, their DELT_NEUT was positive in sign. 2.8.5. Preferred temperatures Preferred temperature was assessed directly (MCI) in a subset of the buildings in the RP-884 database. The typical questionnaire item was of the type: Methods page 58 MRL Australia ASHRAE RP-884 Final Report “At this point in time, would you prefer to feel warmer, cooler, or no change?” These categorical data require different statistical treatments to that applied to linear ASH scale of thermal sensation. In particular, probit analysis (Finney, 1971; Ballantyne et al, 1977) is applicable rather than linear regression. However, probit requires binary responses, whereas the questionnaire item described here has three possible answers. The solution was to split the “no change” responses 50:50 into the remaining two categories. Statistical software was applied to the task of tallying the number of observations with MCI=1 (“want cooler”), and MCI=3 (“want warmer”) for each half- degree temperature bin. Separate probit models were fitted to each of the “want warmer” and “want cooler” percentages with the SAS probit procedure. Our operational definition for the preferred temperature (or other index) within a particular building is that value of the independent variable (e.g. operative temperature) corresponding to the intersection of the “want cooler” and “want warmer” probit models. The fitted probit models and preferred temperatures are depicted in separate graphs in Appendix B for each building in which the MCI questionnaire item was available. Only those models in which the probit models achieved statistical significance at the p=0.05 level or better had their preferred temperatures registered in the RP-884 meta-file. The RP-884 work statement specified separate analyses of thermal comfort (assumed to equal sensation) and preference. Part of the logic underpinning this distinction is known as the “semantic artefact hypothesis” which suggests that the preferred temperature in cold climates may in fact be described as “slightly warm,” whereas residents of hot climates may use words like “slightly cool” to describe their preferred thermal state. While the actual temperatures preferred in both climatic extremes may in fact be identical (assuming similar clothing, air speed, metabolism etc), the semantics may differ to such an extent that the neutrality derived from thermal sensation scales in the manner described above could shift up in warm climates and down in cold climates. The RP-884 meta-file offers an opportunity to examine the semantic artefact in some detail, since there were 55 buildings in which both thermal sensations (ASH) and thermal preferences (MCI) were assessed. To this end, a new variable was defined in meta-analysis: Methods page 59 MRL Australia ASHRAE RP-884 Final Report semantic discrepancy = neutrality minus preferred temperature (°C) discrep = neut_top - preftemp (°C) 2.9. The RP-884 database in the public domain and disseminated via the World Wide Web The ASHRAE RP-884 project has its own homepage on the World Wide Web at the following URL: http://atmos.es.mq.edu.au/~rdedear/ashrae_rp884_home.html The homepage, depicted in Figure 2.9, serves the purpose of introducing the RP-884 project, in particular the main team members on the project as well as the overall concept of “adaptive models” in the context of thermal comfort research (Fig. 2.10). The website also describes the background to the RP-884 database, linking to a flow chart outlining the processes of data acquisition, quality control, standardization and assimilation (Figure 2.1 is hyper-linked in the homepage). The structure of the database and a copy of the codebook (Appendix E) are covered in another hyperlink to the homepage. Most importantly, the comfort research community is given access to the entire RP-884 database by means of an FTP server presented as a clickable “data downloader” on the RP-884 website (see Figure 2.11). The table enables a total of 46 separate data files, each in a variety of formats, to be downloaded from the RP-884 host machine in Sydney to any PC, Mac or UNIX machine elsewhere in the world, as long as it is connected to the internet. Several data formats are available in an effort to facilitate cross-platform transfers, but the most heavily used format is MS Excel® V.5 spreadsheets for use within the MS Windows ® 3.X or Windows 95 operating environments. These data files have been “zipped” into compact, self-extracting archives with *.exe filenames. The user will need to execute (run) the *.exe file after it has been transferred and it will automatically inflate back to the native Excel® 5 format, ready for use on the user’s machine with an *.XLS filename. The forenames of the 46 files within the “data downloader” correspond to the file numbers listed in Table 2.1 earlier in this chapter. Methods page 60 MRL Australia ASHRAE RP-884 Final Report Figure 2.9: The homepage for RP-884 on the World Wide Web http://atmos.es.mq.edu.au/~rdedear/ashrae_rp884_home.html Methods page 61 MRL Australia ASHRAE RP-884 Final Report Figure 2.10: One of the pages linked to the ASHRAE RP-884 homepage Methods page 62 MRL Australia ASHRAE RP-884 Final Report Figure 2.11: The entire RP-884 database (46 data files) is accessible to anyone who is interested via this “data downloader. The device is a “clickable form” interface and can be found on the RP-884 project’s website. Methods page 63 MRL Australia ASHRAE RP-884 Final Report 2.10. Summary of the methods used in RP-884 This chapter has described the RP-884 approach to developing adaptive models of thermal comfort and preference. Underpinning the method has been the creation of a large database of thermal comfort field research observations. The raw data for this database were assembled from a wide variety of climatic, geographic and architectural contexts, and at last count, the database had in excess of 21,000 rows of raw data. The database is made available to the thermal comfort R&D community via a homepage on the World Wide Web. The raw data supplied to the RP-884 database included basic characteristics of the building in which each subject was interviewed, demographic descriptors, the subject’s thermal sensation, preference and acceptability votes at the time of the indoor climate’s physical measurements (ta, tr, rh, v, Tu, clo, met). In view of the significance of clothing in terms of behavioural thermal adjustments and also various thermal index calculations, particular care was taken to ensure clothing and furniture insulation values were derived from, or converted to, a consistent estimation method throughout the entire RP-884 database -- the ASHRAE Standard 55-92 method was selected as the benchmark for this purpose. Once raw data had been standardized, cleaned and assimilated into the database, thermal indices such as ET*, SET, PMV, PPD, PD were calculated using a standard software tool (ASHRAE RP-781). In addition, outdoor meteorological and climatological observations were appended to each set of data in the database to enable an examination of the role played by outdoor atmospheric environmental factors in thermal adaptation. The meta-analysis for RP-884 was conducted on this large database by aggregating observations up to the level of individual buildings, of which there were 160 in total. Statistical results were derived at this level of aggregation, including the building’s thermal neutrality, preferred temperature, thermal acceptability rating, mean indoor Methods page 64 MRL Australia ASHRAE RP-884 Final Report thermal index values, as well as mean outdoor climatic indices at the time of the building’s survey. The next chapter (3) describes the basic results of this meta-analysis. These then provide the foundation upon which adaptive models of thermal comfort will be built in Chapter 4. Methods page 65 MRL Australia ASHRAE RP-884 Final Report Methods page 66 MRL Australia ASHRAE RP-884 Final Report CHAPTER 3 - BASIC RESULTS The generic term “thermal comfort” covers many aspects of subjective thermal experience, each of which has been operationalized with specific questionnaire items by researchers over the years. This chapter focuses on three specific dimensions of thermal perception: a) thermal sensation, b) thermal acceptability, and c) thermal preference. The chapter relates these subjective data to; a) indoor climate, b) outdoor climate, and c) various built environmental-contextual factors. 3.1. Interactions with Indoor Climate This section examines thermal perceptual data in relation to indoor climatic parameters. The first subsection deals with thermal sensation (ASH) and derived parameters such as thermal neutrality and their statistical associations with indoor climatic factors. The second subsection examines thermal acceptability, either directly measured or inferred from (ASH), while the third subsection examines thermal preferences. 3.1.1. Thermal sensation Thermal sensation (ASH) within each building was analyzed separately with respect to four indices of indoor warmth: · operative temperature (TOP) · new effective temperature (ET) · predicted mean vote (PMV) · standard effective temperature (SET) As described in the Methods Chapter (2), data were binned into half-degree steps for the thermal indices. Simple linear regression models within each building were fitted to these binned ASH data as follows: mean thermal sensation = a + b * (bin index value) The regression models generated within the SAS® software package weighted each data point (bin) according to the number of observations it represented; the purpose being to Basic Results page 67 MRL Australia ASHRAE RP-884 Final Report minimize the impact of outlying data points that were based on relatively small number of observations. The resulting models and their associated statistics have been plotted building-by-building in Appendix A. Of the 160 separate building analyzed, only results from models that achieved a statistical significance level (T test) of 95% or better were extracted for use in this chapter’s meta-analysis. 3.1.1.1. Dependence of thermal sensation on indoor operative temperature Regression of binned mean thermal sensation (ASH) on indoor operative temperature (TOP) was performed building-by-building. Individual graphs of the models can be found in Appendix A. Of the total 160 models fitted, 99 (out of 157 buildings, with 3 missing values) achieved statistical significance at the 95% confidence level. Table 3.1 below summarises the significant regression models. Table 3.1: Summary of the weighted linear regression of bin mean thermal sensation on indoor operative temperature (°C). centrally heated/air- naturally mixed-mode conditioned buildings ventilated buildings buildings number of buildings 109 44 4 (2 missing values) (1 missing value) (no missing values) number of buildings with 63 36 3 regression models achieving (57.8% of total) (81.8% of total) (75.0% of total) 95% significance mean (±stdev) model constant -11.96 -6.65 -8.65 (y-intercept)* (±5.839) (±3.572) (±2.982) mean (±stdev) model gradient* 0.51 0.27 0.39 (±0.248) (±0.134) (±0.105) * Based on those models (y=a + b*TOP) achieving 95% statistical significance or better The relatively small number of central HVAC buildings listed in Table 3.1 as producing a significant regression model is probably related to the relatively small number of temperature bins (independent variable), found within such buildings (i.e. tightly controlled temperatures). Basic Results page 68 MRL Australia ASHRAE RP-884 Final Report Naturally ventilated buildings, on the other hand, provided significant regression equations in four out of every five cases. The last row of Table 3.1 indicates that occupants of centrally air-conditioned buildings had thermal sensations that were approximately twice as sensitive to indoor operative temperatures as those of occupants of naturally ventilated buildings. On average, mean thermal sensations changed one unit every two degrees of operative temperature in centrally air-conditioned buildings, whereas in naturally ventilated buildings, a four degree change was needed to shift mean thermal sensations jump by one unit. Testing this difference in sensitivity with the T statistic indicated it was significant (T=5.37, df=97, p<0.001). 3.1.1.2. Dependence of thermal sensation on indoor ET Regression of binned mean thermal sensation (ASH) on indoor new effective temperature (ET) was performed building-by-building and individual graphs of the models are presented in Appendix A. Of the total 160 weighted models fitted, 98 (out of 157, minus 3 missing values) achieved statistical significance at the 95% confidence level. Table 3.2 below summarises the broad findings. Table 3.2: Summary of the weighted linear regression of bin mean thermal sensation on indoor effective temperature (°C). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings 109 44 4 (2 missing values) (1 missing value) number of buildings with regression models 64 34 3 achieving 95% signif. (58.7% of total) (77.3% of total) (75.0% of total) mean (±stdev) model -11.81 -6.76 -7.94 constant (y-intercept)* (±6.383) (±3.076) (±1.884) mean (±stdev) model 0.50 0.28 0.37 gradient * (±0.273) (±0.126) (±0.075) * Based on those models (y=a + b*ET) achieving 95% statistical significance or better As was found with the operative temperature index in Table 3.1, thermal sensations were approximately twice as sensitive to ET in centrally conditioned buildings compared to naturally ventilated buildings (T=4.45, df=96, p<0.001). This suggests that the difference Basic Results page 69 MRL Australia ASHRAE RP-884 Final Report between classes of building was not simply a result of humidity effects being ignored by the operative temperature index of indoor climate. 3.1.1.3. Dependence of thermal sensation on PMV Regression of binned mean thermal sensation (ASH) on indoor Predicted Mean Vote (PMV) was performed building-by-building and individual graphs of the models are presented in Appendix A. Of the total 160 weighted models fitted, 60 (out of 159, minus 1 missing value) achieved statistical significance at the 95% confidence level. Table 3.3 below summarises the broad findings. Table 3.3: Summary of the weighted linear regression of bin mean thermal sensation on indoor Predicted Mean Vote (7-pt scale). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings 111 44 4 (no missing values) (1 missing value) (no missing values) number of buildings with regression models 33 28 3 achieving 95% signif. (29.7% of total) (63.6% of total) (75.0% of total) mean (±stdev) model 0.06 -0.04 0.68 constant (y-intercept)* (±0.274) (±0.611) (±0.697) mean (±stdev) model 0.74 0.62 0.65 gradient* (±0.271) (±0.329) (±0.195) * Based on those models (y=a + b*PMV) achieving 95% statistical significance or better Since the units of the PMV index and ASHRAE thermal sensation scale are one and the same, one would expect the gradient of the regressions models in Table 3.3 above to have been unity, on average. As indicated by the mean gradients in Table 3.3, however, actual mean thermal sensations appear to be less sensitive than PMV predicted, although in the case of centrally conditioned buildings, the observations were about 75% of expectation. This ratio dropped to about 60% in naturally ventilated buildings, but the difference between HVAC and NV was not statistically significant (T=1.56, df=59, p>0.1). The fact that the difference in thermal sensitivity between air-conditioned and naturally ventilated buildings dropped from the 2:1 ratio that was observed with the simpler thermal indices of operative and effective temperature suggests that other physical and behavioral factors were affecting the human body heat balance equation, especially in the naturally Basic Results page 70 MRL Australia ASHRAE RP-884 Final Report ventilated context. Factors such as clothing insulation and air speed, that are excluded from the simpler indices but incorporated in the PMV calculations, might explain some of the relationship between thermal sensations and indoor temperatures. That is, occupants within naturally ventilated buildings were more thermally adaptable at manipulating their heat balance than their counterparts in centrally conditioned buildings. 3.1.1.4. Dependence of thermal sensation on indoor SET Regression of binned mean thermal sensation (ASH) on indoor standard effective temperature (SET) was conducted building-by-building across the RP-884 database and individual graphs of the models are presented in Appendix A. Of the total 160 weighted models fitted, 56 (out of 152, 8 missing values) achieved statistical significance at the 95% confidence level. Table 3.4 below summarises the broad findings. Table 3.4: Summary of the weighted linear regression of bin mean thermal sensation on indoor standard effective temperature (°C). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings 106 43 3 (5 missing values) (2 missing values) (1 missing value) number of buildings with regression models 32 27 3 achieving 95% signif (30.2% of total) (62.8% of total) (100% of total) mean (±stdev) model -5.06 -4.40 -4.66 constant (y-intercept)* (±3.42) (±2.04) (±2.23) mean (±stdev) 0.21 0.18 0.21 model gradient* (±0.14) (±0.08) (±0.06) * Based on those models (y=a + b*SET) achieving 95% statistical significance or better Table 3.4 indicates that regression gradients were relatively constant across all classes of building at about one thermal sensation unit for each five degrees of environmental temperature. The same interpretation that we applied to the PMV index (preceding section) is relevant here as well -- namely that heat balance factors included in these more complex indices such as PMV and SET can account for the greater degree of thermal adaptability in naturally ventilated buildings compared to centrally conditioned buildings. Basic Results page 71 MRL Australia ASHRAE RP-884 Final Report 3.1.2. Thermal neutrality The term “thermal neutrality” refers to a specific value of the indoor thermal environmental index (e.g. operative temperature) corresponding to a mean thermal sensation vote of zero on the seven-pt scale (i.e.“neutral”). Neutrality is readily obtained by solving each building’s regression equation for y=0. 3.1.2.1. Neutral operative temperatures (neut_top) Solution of the regression equations for the “neutral” sensation in relation to the indoor operative temperature (top) was performed building-by-building. Table 3.5 below summarises the neut_top statistics from 160 buildings. Table 3.5: Summary of the neutral operative temperatures (neut_top) from 160 buildings in the database (°C). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of signif results 47 out of 78 27 out of 32 1 out of 2 in summer building (I missing value) (1 missing value) (no missing values) sample* mean neut_top (±stdev) 24.1 24.6 23.9 in summer sample °C (±1.31) (±2.42) (±0) number of signif results 14 out of 30 7 out of 11 2 out of 2 in winter building (2 missing values) (1 missing value) (no missing values) sample* mean neut_top (±stdev) 22.5 22.4 20.7 in the winter sample °C (±0.35) (±2.78) (±0.50) * only results from buildings with statistically significant regression models used Table 3.5 indicates that thermal neutrality was observed across the winter experiments in RP-884 database at an average indoor operative temperature of about 22.5°C, regardless of whether the buildings were centrally conditioned or naturally ventilated. However, the standard deviation of winter neutralities in the sample of naturally ventilated buildings was about eight times that observed in the centrally conditioned buildings. In the summer field experiments there was a tendency for neutrality to be half a degree warmer in naturally ventilated buildings compared to centrally conditioned, but the difference failed to reach Basic Results page 72 MRL Australia ASHRAE RP-884 Final Report significance (T = 1.16, df = 72, p > 0.1). The standard deviation of neutralities was again found to be larger in the summer sample of naturally ventilated buildings. All Buildings 29 27 C) o 25 23 21 19 neutral operative temperature ( 17 neut_top = 15.34 + 0.35 * top R2 = 0.38, p = 0.0001 15 18 20 22 24 26 28 30 32 o mean indoor operative temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 29 29 27 27 C) 25 C) 25 o o 23 23 21 21 19 19 temperature ( temperature ( neutral operative neutral operative 17 neut_top = 8.92 + 0.62 * top 17 neut_top = 15.47 + 0.35 * top R2 = 0.27, p = 0.0001 R2 = 0.32, p = 0.0013 15 15 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 o o mean indoor operative temperature ( C) mean indoor operative temperature ( C) Figure 3.1: Dependence of neutral indoor temperature on buildings’ mean temperature The adaptive hypothesis predicts that the temperatures regarded as “neutral” within any particular building will depend, in part, on the level of warmth typically encountered and expected within that building. Figure 3.1 expresses this idea statistically by regressing the neut_top observations against mean indoor operative temperatures. Figure 3.1 lends Basic Results page 73 MRL Australia ASHRAE RP-884 Final Report support to this adaptive hypothesis insofar as the models show a moderate linear correlation (r=0.5~0.6). The main regression equation (“all buildings” in Figure 3.1) indicates that indoor neutrality increases by about one degree (°C) for every three degrees increase of indoor temperature. Despite the apparent difference in gradients between the HVAC and NV building samples, their regression gradients were not significantly different (T=0.08, df=96, p>0.5). 3.1.2.2. Neutral effective temperatures (neut_et) Solution of the regression equations for the “neutral” sensation in relation to ET was performed building-by-building across the RP-884 database. Table 3.6 below summarises the neut_et findings from 160 buildings. Effective temperature neutralities within the naturally ventilated buildings were, on average, about half a degree warmer than their centrally conditioned counterparts in both seasons, but the difference was insignificant (summer T = 1.12, df = 70, p > 0.2 and winter T = 0.99, df = 21, p > 0.2). Table 3.6: Summary of the neutral effective temperatures (neut_et) from 160 buildings in the database (°C). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings in 45 out of 78 27 out of 31 1 out of 2 summer sample* (1 missing value) (2 missing values) (no missing values) mean neut_et (±stdev) in 24.0 24.6 23.4 summer sample °C (±1.63) (±2.91) (±0) number of buildings in 16 out of 30 7 out of 11 2 out of 2 winter sample* (2 missing values) (1 missing value) (no missing values) mean neut_et (±stdev) in 22.4 22.9 20.7 the winter sample °C (±0.77) (±1.68) (±0.55) * only results from buildings with statistically significant regression models used 3.1.2.3. Neutral predicted mean votes (neut_pmv) Solution of the regression equations for the “neutral” sensation in relation to the PMV index was done building-by-building. Since both dependent and independent variables are based Basic Results page 74 MRL Australia ASHRAE RP-884 Final Report on the same scales, the static heat-balance model theory predicts that subjects would vote zero (neutral) when they were in heat-balance conditions corresponding to PMV=0. However, results of these regression analyses indicate significant departures from this expectation. Table 3.7 below summarizes the neut_pmv findings from 160 buildings in the RP-884 meta-analysis. Many buildings in the database failed to produce statistically significant models when using the PMV index, but those that did indicated that occupants of HVAC buildings in summer found neutrality in indoor climatic conditions that corresponded with PMV=0; i.e. model matched observation quite closely. On the other hand, occupants of naturally ventilated buildings during summer found themselves feeling neutral in indoor conditions that the PMV model indicated as cooler-than-neutral. This HVAC v NV difference however, proved to be statistically insignificant (T = 1.48, df = 47, p > 0.1) due to the small number of buildings being compared. Table 3.7: Summary of the neutral Predicted Mean Votes neut_pmv from 160 buildings in the database (7-pt scale). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings in 23 out of 79 26 out of 32 1out of 2 summer sample* (no missing values) (1 missing) (no missing values) mean neut_pmv (±stdev) in the summer sample 0.01 -0.43 -0.24±0) (±0.32) (±1.40) number of buildings in winter sample* 7 out of 32 1 out of 12 1 out of 2 (no missing values) (1 missing value) (no missing values) mean neut_pmv (±stdev) in the winter sample -0.55 1.11 -0.53 (±0.301) (±0) (±0) * only results from buildings with statistically significant regression models used 3.1.2.4. Predicted neutralities with the PMV heat balance model As indicated in the preceding table, neutrality in several buildings occurred in thermal environmental conditions that departed significantly from the predictions of the static heat- balance models of thermal comfort. Another way of examining this issue is to use the PMV Basic Results page 75 MRL Australia ASHRAE RP-884 Final Report model to actually predict the neutral operative temperature for each building, ceteris paribus. This new variable in the meta-analysis was code-named PREDNEUT. All buildings 30 28 26 24 C) 22 o 20 18 predneut ( 16 14 12 predneut = 15.43 + 0.33 * top R2 = 0.54, p = 0.0001 10 12 14 16 18 20 22 24 26 28 30 32 mean indoor operative temperature (oC) Central HVAC and Mixed Mode buildings Naturally Ventilated buildings 30 30 predneut = 13.16 + 0.43 * top 28 28 R2 = 0.15, p = 0.0001 26 26 24 C) 24 C) o o 22 22 20 20 18 18 16 16 predneut ( predneut ( 14 14 predneut = 15.07 + 0.34 * top 12 12 R2 = 0.70, p = 0.0001 10 10 12 14 16 18 20 22 24 26 28 30 32 12 14 16 18 20 22 24 26 28 30 32 mean indoor operative temperature (oC) mean indoor operative temperature (oC) Figure 3.2: Dependence of neutrality predicted by the PMV heat-balance model (PREDNEUT) on mean indoor temperatures (TOP). The clear dependence of predneut on mean indoor top in Figure 3.2 suggests that the other heat balance variables that change in response to indoor temperature -- such as clothing insulation and air speeds, were driving the PMV predicted neutralities. The correlation appears to be strongest in the case of the naturally ventilated buildings (r=0.84). In the case Basic Results page 76 MRL Australia ASHRAE RP-884 Final Report of the HVAC and mixed mode buildings in Figure 3.2, the seven outlying buildings below the fitted regression line were from the Brown (1992/3) study in industrial settings where metabolic rates were elevated well above those encountered in the remaining office and residential buildings, causing the predicted neutrality to be depressed by as much as 10°C below the trendline. These outliers account for the reduction in correlation coefficients. 3.1.2.5. Neutral standard effective temperatures (neut_set) Solution of the regression equations for the “neutral” sensation in relation to SET was performed building-by-building. Table 3.8 below summarises the neut_set findings from 160 buildings. Table 3.8: Summary of the neutral standard effective temperatures (neut_set) (°C). centrally heated/air- naturally ventilated mixed-mode conditioned buildings buildings buildings number of buildings in 24 out of 73 25 out of 31 1 out of 1 summer sample* (6 missing values) (2 missing values) (1 missing value) mean neut_set (±stdev) in 24.5 24.1 23.8 the summer sample (±1.51) (±2.85) (±0) number of buildings in 8 out of 30 2 out of 11 2 out of 2 winter sample* (2 missing values) (1 missing value) (no missing values) mean neut_set (±stdev) in 25.2 31.3 19.6 the winter sample (±3.51) (±1.89) (±6.73) * only results from buildings with statistically significant regression models used Less than one third of the centrally heated/air-conditioned buildings in the sample yielded significant regression models, probably because of the restricted range of thermal environmental conditions in such buildings. Naturally ventilated buildings, with their greater internal climatic variety produced a significant regression equation against the SET index in the majority of cases. In those buildings sampled during the summer season, there was an average neutral SET in the 24~24.5°C region and the difference of 0.4 K between the Basic Results page 77 MRL Australia ASHRAE RP-884 Final Report means of naturally ventilated and centrally heated/air-conditioned buildings was not statistically significant (T = 0.61, df = 47, p > 0.5). The anomalously high average neutral SET of 31.3°C observed in naturally ventilated buildings in winter was based on only two buildings. The seasonal difference in neutrality for HVAC buildings was not statistically significant (T = 0.8, df = 30, p > 0.2). 3.1.3. Thermal acceptability and indoor climate Thermal acceptability was directly assessed in a small subset of studies in the RP-884 database, but it could be inferred from thermal sensation votes, which were recorded in all studies. This section analyzes both direct and inferred versions of thermal acceptability response. 3.1.3.1. Relationship between direct and inferred thermal acceptability Testing the assumption that a thermal sensation vote falling in the interval -1.5 The result have be depicted in Figure 3.3. The graphs represent weighted regression models of prxy_tsa versus f_tsa_2. Each point in the graphs represents a specific building in the database. The solid line plotted through the data points represents the expected relationship (gradient 1:1) whereas the dotted line represents the line of best fit (with model equation and statistics annotated on each graph). Basic Results page 78 MRL Australia ASHRAE RP-884 Final Report proxy acceptability versus actual acceptability Townsville Australia RP-702 for all available buildings (tropical dry season), HVAC buildings. 100 100 90 80 90 prxy_tsa = 30.74 + 0.61 * tsa prxy_tsa = 0.55 * tsa + 38.849 2 70 R2 = 0.48, p = 0.0001 R = 0.4685 80 60 Proxy TSA Proxy TSA 50 70 40 (% "satisfactory" ASH votes) (% "satisfactory" ASH votes) 30 60 30 40 50 60 70 80 90 100 60 70 80 90 100 TSA (% acceptable) TSA (% aceptable) Townsville Australia RP-702 Montreal Canada RP-821 (summer), (tropical wet season), HVAC buildings. HVAC buildings. 100 100 90 90 80 prxy_tsa = 0.4288 * tsa + 46.302 80 R2 = 0.2038 70 Proxy TSA prxy_tsa = 0.5007 * tsa + 32.536 Proxy TSA R2 = 0.4752 70 60 (% "satisfactory" ASH votes) (% "satisfactory" ASH votes) 60 50 60 70 80 90 100 50 60 70 80 90 100 TSA (% acceptable) TSA (% acceptable) Montreal Canada RP-821 (winter), Sydney Australia (summer and winter) HVAC buildings. HVAC and Mixed buildings. 100 100 90 90 80 80 70 Proxy TSA prxy_tsa = 1.3448 * tsa - 35.093 Proxy TSA R2 = 0.7155 prxy_tsa = -0.2419 * tsa + 92.27 60 70 R2 = 0.9872 (% "satisfactory" ASH votes) (% "satisfactory" ASH votes) 50 60 50 60 70 80 90 100 60 70 80 90 100 TSA (% acceptable) TSA (% acceptable) Figure 3.3: Comparison of directly determined and inferred thermal acceptability. Each data point represents an individual building from the RP-884 database. Figure 3.3 indicates that the strength of association between direct and inferred thermal acceptability varied considerably across field experiments. For the pooled analysis of all seven experiments’ buildings (top left panel in Figure 3.3), about half of the variance in acceptable thermal sensations (prxy_tsa) could be accounted for by the direct thermal acceptability ratings. Discounting Rowe’s (1996) Sydney experiment due to its small sample size (three building data points), the highest correlation was found in Donnini’s (1996) Montreal winter experiment (r = 0.85). For the remaining experiments, the correlations can be described as moderate. In five out of the seven individual project graphs Basic Results page 79 MRL Australia ASHRAE RP-884 Final Report depicted in Figure 3.3, the gradient of the observed dependence of inferred acceptability on directly stated acceptability was significantly lower than the unity we expected. Subjects apparently were voting that thermal sensations outside the central three categories of the ASHRAE 7-pt scale were still acceptable. Expressing that a different way, buildings that had a high rating of thermal acceptability on the direct scale (>80%), typically scored lower acceptability ratings on the basis of percentage of thermal sensations falling within the central three categories of the 7-pt scale. 3.1.3.2. Directly determined thermal acceptability. As noted in the preceding section, “acceptable” TSA votes (“At the present time, is this thermal environment acceptable to you or not?”) were tallied for each building and expressed as a percentage of all responses in the building. This percentage was coded as f_tsa_2 for each building in the RP-884 meta-analysis. Ignoring the upper and lower humidity boundaries of the summer and winter comfort zones depicted in ASHRAE Standard 55-92, the percentage of indoor climate measurements within each RP-884 database building complying with the relevant summer or winter ASHRAE comfort zone ET boundaries was coded as ASH55_92 in the meta-analysis. A simple assessment of the practical utility of the ASHRAE comfort zones can be performed by comparing these acceptability levels predicted from indoor climatic measurements (ASH55_92) with the corresponding thermal acceptability ratings for each building. These comparisons have been performed in Figures 3.4 and 3.5 respectively -- each data point in the graphs represents a single building. Basic Results page 80 MRL Australia ASHRAE RP-884 Final Report Thermal Acceptability (all buildings) 100 90 80 70 60 50 40 30 TSA (% acceptable) 20 tsa = 75.05 + 0.03 * ASH55_92 10 R2 = 0.01, p = 0.5258 0 0 10 20 30 40 50 60 70 80 90 100 % indoor climates falling within ASHRAE 55-92 comfort zones Figure 3.4: Relationship between direct thermal acceptability ratings of buildings (f_tsa_2) and buildings’ compliance with ASHRAE Standard 55-1992 comfort zone ET boundaries (ASH55_92). Thermal Acceptability (all buildings) 100 90 80 70 60 50 40 Proxy TSA 30 20 prxy_tsa = 70.96 + 0.16*ASH55_92 10 R2 = 0.14, p = 0.0001 (% votes -1.5 < ashrae 1.5) 0 0 10 20 30 40 50 60 70 80 90 100 % indoor climates falling within ASHRAE 55-92 comfort zones Figure 3.5: Relationship between acceptability thermal sensation ratings of buildings (prxy-tsa) and buildings’ compliance with ASHRAE Standard 55-1992 comfort zone ET boundaries (ASH55_92). Regardless of which thermal acceptability measure was adopted, Figures 3.4 and 3.5 indicate that compliance with the ET prescriptions of ASHRAE Standard 55-1992 had little or no bearing on the buildings’ acceptability ratings by occupants. This is indicated Basic Results page 81 MRL Australia ASHRAE RP-884 Final Report clearly by the complete lack of statistical significance in the regression models plotted on the graphs in Figures 3.4 and 3.5. A logical extension of this null result is that most of the buildings which had very low levels of compliance with ASHRAE 55-92 (say, ASH55_92 < 30%) still had occupant ratings of thermal acceptability better than 60 to 70% TSA versus TOP for all buildings TSA versus ET for all buildings 100 100 80 80 60 60 40 40 2 TSA (% acceptable) 20 20 tsa = -287.56 + 27.75 * top - 0.52 * top2 TSA (% acceptable) tsa = -253.80 + 25.64 * et - 0.49 * et 2 R2 = 0.08, p = 0.0883 R = 0.06, p = 0.1554 0 0 18 20 22 24 26 28 30 32 34 18 20 22 24 26 28 30 32 34 o o mean indoor operative temperature ( C) mean indoor effective temperature ( C) TSA versus PMV for all buildings TSA versus SET for all buildings 100 100 80 80 60 60 40 40 20 TSA (% acceptable) 20 TSA (% acceptable) tsa = 77.23 + 16.00 * pmv - 11.63 * pmv2 tsa = -724.32 + 61.02 * set - 1.16 * set 2 R2 = 0.18, p = 0.0030 R 2 = 0.22, p = 0.0005 0 0 -3 -2 -1 0 1 2 3 18 20 22 24 26 28 30 mean indoor predicted mean vote o mean indoor standard effective temperature ( C) Figure 3.6: Dependence of direct thermal acceptability ratings on mean thermal index values. Each data point represents a building. Figure 3.6 shows the percentage of occupants within each building voting “acceptable” (f_tsa_2) as a function of the mean indoor climatic index values recorded for each building. The indices selected for this analysis covered the spectrum from relatively simple operative temperature up to fully developed heat balance indices such as PMV and SET. The expected relationship between percentage satisfied and indoor warmth is hyperbolic, peaking around the database’s mean neutrality or preferred temperature. Unfortunately the majority of buildings available for the analysis were clustered within a fairly narrow band of indoor temperatures, centred on 23°C, and so the data are not well suited to regression analysis. As a result, the weighted 2nd order polynomial models fitted to the TOP and ET Basic Results page 82 MRL Australia ASHRAE RP-884 Final Report indices in Figure 3.6 were not statistically significant. While the models fitted for the more sophisticated heat balance models such as PMV and SET did achieve statistical significance, the explained variance was about 20% in both cases. The underlying concept of Fanger’s Predicted Percentage Dissatisfied index (1970) is simply that as mean indoor climatic conditions depart from the optimum (assumed to be PMV=0), the percentage of persons experiencing unacceptable thermal sensations increases. Despite the obvious lack of normality in statistical distributions for TSA % across the RP-884 building database, the 2nd order polynomial regression equation fitted to mean building PMV values in Figure 3.6 is of the hyperbolic form suggested by the PPD concept. The fact that the PMV index produced a statistically significant relationship (R2= 0.18) where the simpler indices of TOP and ET failed suggests that the inclusion of other heat balance factors such as air speed, metabolic rate and clothing actually does what it’s supposed to do -- improve predictions. The same interpretation can be applied to the SET index, since it too incorporates the full array of heat balance variables, and as seen in Figure 3.6, its 2nd order polynomial model was also statistically significant (p=0.0005). 3.1.3.3. Thermal acceptability inferred from thermal sensation. Fanger’s Predicted Percentage Dissatisfied (PPD) model is premised on the assumption that a thermal sensation vote within the central three categories of the ASHRAE 7-point scale (slightly cool + neutral + slightly warm) is acceptable and satisfactory. Since it is derived from the core thermal response item of ASH, this proxy for thermal acceptability was obtained for every respondent in the cumulative ASHRAE RP-884 database (n>21,000) and coded as prxy_tsa. Basic Results page 83 MRL Australia ASHRAE RP-884 Final Report Proxy TSA versus TOP for all buildings Proxy TSA versus ET for all buildings 100 100 80 80 60 60 40 40 20 prxy_tsa = -176.48 +20.56 * top - 0.41 * top2 20 2 2 prxy_tsa = -171.19 + 20.21 * et - 0.40 * et R = 0.49, p = 0.0001 2 Proxy TSA (% acceptable) Proxy TSA (% acceptable) R = 0.51, p = 0.0001 0 0 10 15 20 25 30 35 10 15 20 25 30 35 o o mean indoor operative temperature ( C) mean indoor effective temperature ( C) Proxy TSA versus PMV for all buildings Proxy TSA versus SET for all buildings 100 100 80 80 60 60 40 40 20 prxy_tsa = 83.72 + 10.75 * pmv - 10.52 * pmv2 20 prxy_tsa = -679.20 + 56.70 * set - 1.05 * set 2 R2 = 0.42, p = 0.0001 R 2 = 0.41, p = 0.0001 Proxy TSA (% acceptable) Proxy TSA (% acceptable) 0 0 -3 -2 -1 0 1 2 3 14 16 18 20 22 24 26 28 30 32 34 mean indoor predicted mean vote o mean indoor standard effective temperature ( C) Figure 3.7: Dependence of building acceptability ratings (derived from thermal sensation) on mean thermal index values. Each data point represents a building. As noted in the preceding section, the majority of buildings in the RP-884 database were clustered within a narrow range of mean indoor temperatures, severely limiting the scope for regression analyses. However in Figure 3.7, because of the larger sample size compared with the preceding section, all four indoor climatic indices showed statistically significant relationships with this proxy building thermal acceptability index. 3.1.3.4. Thermal sensitivity and the range of thermally acceptable temperatures. Given the relatively weak correlations for thermal acceptability in the preceding sections, the use of the associated regression models to define acceptable ranges of thermal indices would be not very reliable. A more feasible alternative, based on Fanger’s Predicted Percentage Dissatisfied (PPD) concept (1970), can be applied to this question of acceptable ranges. As noted earlier, PPD is a function of mean thermal sensation (PMV in Fanger’s terminology) and a PMV of ±0.85 is assumed to correspond with 80% acceptability. Logically therefore, assuming that actual thermal sensation votes (ASH) are distributed around their mean with a similar variance as predicted votes are (PMV/PPD), the values of a particular indoor thermal index (e.g. TOP, ET, PMV or SET) corresponding Basic Results page 84 MRL Australia ASHRAE RP-884 Final Report with mean ASH votes of ±0.85 can be interpreted as the limits of acceptable thermal environments (for 80% acceptability). This derivation of acceptable ranges was operationalized by solving the ASH linear regression models (Appendix A) that we defined for each of the main indoor thermal indices (TOP, ET, PMV, SET) for each of the buildings in the RP-884 database, using ASH=-0.85 and again using ASH=+0.85. Subtraction of the index value, say TOP, corresponding with -0.85 from the corresponding +0.85 value defines the width of 80% acceptable TOP values for that particular building and the variable thus defined was codenamed RANG_TOP in the meta-analysis. Note that acceptable temperature ranges using these techniques were only feasible in buildings whose ASH regression models (Appendix A) achieved statistical significance at the 95% confidence level. Table 3.9: Range of acceptable operative temperatures (Kelvin). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings 108 41 4 (3 missing values) (4 missing values) (no missing values) number of buildings with regression models 62 33 3 achieving 95% (57% of total) (75% of total) (75% of total) significance* 80% acceptability criterion (RANG_TOP) 4.1 6.9 4.5 Mean (±stdev) (±1.91) (±2.79) (±1.24) 90% acceptability criterion (RANTOP10) 2.4 4.9 2.7 Mean (±stdev) (±1.12) (±3.27) (±0.73) * Based on those thermal sensation models in Appendix A (y=a + b*TOP) achieving 95% statistical significance or better The 80% acceptable range of operative temperatures was, on average, 6.9 K wide in naturally ventilated buildings, which was about 70% wider than in centrally heated/air- conditioned buildings. This difference was statistically significant (T = 5.69, df = 93, p<0.001). The acceptable range of operative temperatures for mixed mode buildings was, Basic Results page 85 MRL Australia ASHRAE RP-884 Final Report on average, between that of HVAC and NV buildings, but the small number of cases precludes any statistical tests. Also included in Table 3.9 are the acceptable operative temperature ranges for a more stringent criterion of 90% acceptability (labelled RANTOP10 in the meta-anlysis). These were derived from each building’s thermal sensation v operative temperature regression equation, but instead of solving for neutrality ± 0.85 sensation units (as was the case for the 80% criterion used for RANG_TOP), we applied the PPD=10% assumption, namely neutrality ± 0.5 sensation units. The acceptable ranges in Table 3.9 reduced from 4.1 K for 80% acceptability in HVAC buildings to 2.4 K using the 90% acceptability criterion (not dissimilar to the prescriptive ranges found in ASHRAE Standard 55-92 for the same acceptability criterion for general thermal comfort, excluding local discomforts). However, the average 90% acceptability range observed for RP-884’s naturally ventilated sample was twice as wide as observed in the HVAC sample in Table 3.9 (and also prescribed in ASHRAE Standard 55-92). Basic Results page 86 MRL Australia ASHRAE RP-884 Final Report All Buildings 14 rang_top = 61.87 - 4.58 * top + 0.09 * top2 R2 = 0.18, p = 0.0001 12 10 8 6 rang_top (K) 4 2 0 17 19 21 23 25 27 29 31 33 mean indoor operative temperature (oC) Central HVAC and Mixed Mode buildings Naturally Ventilated buildings 14 14 rang_top = 145.35 - 11.85 * top + 0.25 * top2 2 12 R = 0.14, p = 0.0099 12 10 10 8 8 6 6 rang_top (K) 4 rang_top (K) 4 2 2 rang_top = 30.51 - 1.82 * top + 0.04 * top2 R2 = 0.02, p = 0.7189 0 0 17 19 21 23 25 27 29 31 33 17 19 21 23 25 27 29 31 33 mean indoor operative temperature (oC) mean indoor operative temperature (oC) Figure 3.8: Dependence of the acceptable range of operative temperature within buildings on mean operative temperature indoors Figure 3.8 indicates a loose relationship between acceptable ranges and mean indoor operative temperatures (RANG_TOP) -- as a building’s mean indoor temperature deviates from moderate levels in the vicinity of 24~25°C, the acceptable range tends to increase. Conducting this analysis separately on the HVAC and NV buildings indicates a lack of any statistical relationship for the sample of naturally ventilated buildings. Basic Results page 87 MRL Australia ASHRAE RP-884 Final Report All Buildings 14 12 10 8 6 rang_top (K) 4 2 rang_top = 3.19 + 1.82 * stdev. top R2 = 0.35, p = 0.0001 0 0 1 2 3 4 5 stdev. mean indoor operative temperature (oC) Central HVAC and Mixed Mode buildings Naturally Ventilated buildings 14 14 rang_top = 3.79 + 0.57 * stdev_ top 2 12 R = 0.06, p =0.0580 12 10 10 8 8 6 6 rang_top (K) 4 rang_top (K) 4 2 2 rang_top = 4.16 + 1.65 * stdev_ top R2 = 0.26, p = 0.0034 0 0 0 1 2 3 4 5 0 1 2 3 4 5 stdev. indoor operative temperature (oC) stdev. indoor operative temperature (oC) Figure 3.9: Dependence of the acceptable range of operative temperatures (TOP) within buildings on their standard deviation of operative temperature indoors The adaptive hypothesis emphasises the effects of expectation on thermal acceptability. If a particular building’s indoor climate is characterized by large variations in temperature, both temporally and spatially, the adaptive hypothesis predicts a corresponding widening in the range of indoor temperatures considered acceptable by its occupants. Figure 3.9 depicts the linear relationship between the range of acceptable operative temperatures and the standard deviation of indoor operative temperature. The model was statistically significant with a correlation coefficient r = +0.59, and the regression equation indicates that the acceptable range (-0.85< ASH < +0.85) increases by about two degrees for a single degree increase in standard deviation of operative temperature. So, in tightly controlled HVAC Basic Results page 88 MRL Australia ASHRAE RP-884 Final Report buildings depicted in Figure 3.9 where we find relatively small standard deviations of operative temperature, there is a clear trend for the gradient of ASH v TOP regression models to increase (see Appendix A). Consequently the range of acceptable temperatures appears to be much greater in naturally ventilated buildings where thermal variability is the norm compared to HVAC buildings. 3.1.4. Thermal preferences and indoor climate One hundred and sixteen of the 160 buildings in ASHRAE RP-884’s database assessed thermal preferences with a questionnaire item along these lines: “At this point in time, would you prefer to feel warmer, cooler, or no change?” Probit regression analysis (Finney, 1971; Ballantyne, 1977) rather than linear regression has been separately applied to the votes for warmer and cooler conditions for each building. Preferred temperature (of whatever index) was defined as that value of the independent variable (thermal index) corresponding to the intersection of the “want cooler” and “want warmer” probit models (see Appendix B). Table 3.10 below summarises the main statistics for preferred operative temperatures for the 116 buildings in which the questionnaire item was available. Table 3.10: Summary of the preferred operative temperatures (preftemp) (°C). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings in 62 24 1 summer sample* (17 missing values) (9 missing values) (1 missing value) mean preftemp (±stdev) 23.1 24.3 24 in the summer sample (±1.26) (±2.13) (±0) number of buildings in 22 6 1 winter sample* (10 missing values) (6 missing values) (1 missing value) mean preftemp (±stdev) 22.9 23.1 21.7 in the winter sample (±1.19) (±1.61) (±0) * results not based on statistically significant regression models. Basic Results page 89 MRL Australia ASHRAE RP-884 Final Report The results in Table 3.10 indicate a fairly constant temperature preference of about 23°C in centrally controlled HVAC buildings. Winter temperature preferences in naturally ventilated buildings were on average about a degree cooler than summer preferences, but this failed to meet statistical significance, due to the small sizes and large standard deviations. The summer temperature preferences in HVAC buildings were on average about one degree cooler than those in naturally ventilated buildings and because of the more substantial sample sizes in this season, the difference was significant (T = 3.23, df = 84, p < 0.002). The trivial difference in temperature preferences between HVAC and NV buildings in winter at less than a third of a degree was statistically insignificant (T = 0.34, df = 26, p > 0.5). All Buildings All Buildings 29 29 27 27 C) C) o 25 o 25 23 23 21 21 temperature ( temperature ( preferred operative 19 preferred operative 19 preftemp = 16.30 + 0.29 * top preftemp = 16.21 + 0.30 * et 2 R2 = 0.28, p = 0.0001 R = 0.30, p 0.0001 17 17 18 20 22 24 26 28 30 32 34 17 19 21 23 25 27 29 31 33 35 mean indoor operative temperature (oC) mean indoor effective temperature (oC) All Buildings All Buildings 29 29 27 27 C) C) o 25 o 25 23 23 21 21 temperature ( temperature ( preferred operative preferred operative 19 preftemp = 23.15 + 1.30 * pmv 19 preftemp = 12.93 + 0.41* set R2 = 0.30, p = 0.0001 R2 = 0.29, p 0.0001 17 17 -1 -0.5 0 0.5 1 1.5 2 2.5 22 24 26 28 30 32 34 o Predicted Mean Vote mean indoor standard effective temperature ( C) Figure 3.10 Thermal preferences as a function of mean indoor thermal index values (TOP, ET, PMV, SET). Each data point represents a single building. Figure 3.10 indicates that the operative temperature preferred by building occupants was moderately correlated with mean levels of warmth prevailing within their buildings at the time of the field survey. The strength of correlation was reasonably consistent at about r=+0.55 across all four indoor climatic indices (TOP, ET, PMV and SET). Basic Results page 90 MRL Australia ASHRAE RP-884 Final Report 3.1.5. Comparisons between neutral and preferred temperatures indoors. With various aspects of perceived indoor climates being assessed with different questionnaire items, there is a possibility that the indoor temperatures defined as optimal for a particular building and climatic context may in fact vary, depending on whether one is talking in terms of thermal sensation (neutrality), thermal acceptability (satisfaction) or thermal preference (preferred temperatures). Indeed, some authors (McIntyre, 1978; de Dear, 1991c) have suggested that at least some of the statistical dependence of neutrality on prevailing outdoor climates observed by the pioneers of adaptive models (Auliciems and Humphreys) may in fact be due to a semantic artefact in the ASHRAE (or Bedford) 7-pt scale of thermal sensation. Persons living in cold climates may in fact describe their preferred thermal environment with words like “warm and cosy” while for persons in hot climates, words like “cool and fresh” may connote their thermal ideal. The RP-884 database contains 55 buildings in which both thermal sensations (ASH) and thermal preferences were registered, and so each of these buildings had both a neutrality and a preferred temperature available in the meta-analysis. A new variable called “semantic discrepancy” (discrep) was calculated as neutrality minus preferred temperature and expressed in degrees (°C). Table 3.11: Statistics for the semantic discrepancy (discrep) between observed neutrality (neut_top) and observed temperature preference (preftemp) (°C). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings in 43 out of 62 23 out of 24 1 out of 1 summer sample* (17 missing values) (9 missing values) (1 missing value) mean discrep (±stdev) in the summer sample 0.7 0.2 -0.14 (±0.78) (±1.38) (±0) number of buildings in 13 out of 22 6 out of 6 1 out of 1 winter sample* (10 missing values) (no missing values) (1 missing values) mean discrep (±stdev) in the winter sample 0.0 0.3 -0.7 (±0.45) (±1.00) (±0) * only results from buildings with statistically significant regression models (neut_top) and probit analyses (preftemp) were used to define discrep Basic Results page 91 MRL Australia ASHRAE RP-884 Final Report Table 3.11 indicates that in both seasons and in HVAC and NV buildings alike, the average semantic discrepancy between neutrality and preference was greater than or equal to zero degrees. Although the seasonal difference between mean DISCREP in NV buildings was negligible (T = 0.15, df = 27, p > 0.5), the seasonal difference in HVAC buildings was statistically significant (T = 2.93, df = 54, p < 0.01). Neither the summer nor the winter differences between DISCREP in HVAC and NV buildings were significant (T = 1.96, df = 64, p > 0.05 and T = 0.73, df = 17, p > 0.2 respectively). Figure 3.11 below was designed to test the hypothesis that warm environments promote positive semantic discrepancies between thermal sensations and preferences, while cool environments promote negative discrepancies. The seemingly random distribution of data points in the graph and statistically insignificant correlation and regression in the “all buildings” panel of Figure 3.11 suggest that mean indoor climatic warmth (top) appears to exert no systematic influence on the semantics of thermal sensation scales. Pursuing the semantic artefact hypothesis a little further, the database was disaggregated into HVAC and naturally ventilated buildings. The lower panels of Figure 3.11 indicates again that, for the naturally ventilated buildings at least, the mean levels of warmth indoors had no systematic effect on DISCREP. However, there was a positive, albeit modest, relationship between the DISCREP variable and mean indoor operative temperature in HVAC buildings. The gradient on that regression model indicates that, on average, neutrality inside a centrally air-conditioned building becomes elevated above preferred temperature by about one degree for every two degrees the mean indoor operative temperature increases between 21 and 26°C. That is, persons living and/or working in generally warm centrally air-conditioned buildings seem to be describing their preferred indoor climate with terms like “slightly cool” while persons in generally cool centrally air- conditioned buildings seem inclined to describe their preferred indoor climate as “slightly warm.” Basic Results page 92 MRL Australia ASHRAE RP-884 Final Report All Buildings 3 2 1 C) o 0 -1 discrep ( -2 -3 discrep = 0.12 + 0.01 * top R2 = 0.002, p = 0.7034 -4 18 20 22 24 26 28 30 32 34 o mean indoor operative temperature ( C) Central HVAC and Mixed Mode buildings Naturally Ventilated Buildings 3 3 2 2 1 1 C) C) o o 0 0 -1 -1 discrep ( -2 discrep ( -2 discrep = -12.12 + 0.54 * top -3 -3 discrep = 0.08 + 0.01 * top R2 0.25, p = 0.0001 R2 = 0.001, p = 0.8450 -4 -4 18 20 22 24 26 28 30 32 34 18 20 22 24 26 28 30 32 34 mean indoor operative temperature (oC) mean indoor operative temperature (oC) Figure 3.11: Dependence of discrep on mean indoor operative temperatures 3.1.6. Behavioural adjustments to indoor climate As noted in the introductory chapter to this monograph, behavioral thermoregulation involves a variety of purposive actions that modify the heat and mass exchanges that define the body’s heat balance with its thermal environment. The most obvious behavioural response for which we have quantitative data in the RP-884 database is that of clothing insulation. The other “personal” or behavioral parameter governing the human body’s heat balance for which we have quantitative estimates in the RP-884 database is metabolic heat. Thirdly, indoor air speeds which were measured throughout the RP-884 database, is another parameter over which building occupants exert some behavioral control, either by Basic Results page 93 MRL Australia ASHRAE RP-884 Final Report opening/closing windows, or turning on/off fans and similar devices. The following sections examine these data and their relationships with various indices of indoor climate. 3.1.6.1. Thermal insulation adjustments indoors Clothing insulation and also the incremental insulation of the chairs upon which the subjects were sitting at the time of their questionnaire response were converted into clo units according to the ASHRAE Standard 55 1992 methods. Table 3.12 summarises the main statistics. Table 3.12: Statistics for the thermal insulation variable (clothes + furniture) (clo). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings in 79 33 2 summer sample* mean INSUL (±stdev) in 0.70 0.66 0.71 the summer sample (± 0.077) (± 0.125) (± 0.008) number of buildings in 32 12 2 winter sample* mean INSUL (±stdev) in 0.92 0.93 0.83 the winter sample (± 0.126) (± 0.331) (± 0.259) Table 3.12 indicates significant seasonal differences in thermal insulation, with average winter values exceeding 0.9 clo and average summer values around 0.7 clo (T=11.2, df=109, p<0.001 for HVAC buildings; T=4.0, df=43, p<0.001 for NV buildings). While seasonal differences were significant, the trivial differences between HVAC and NV sample means failed to reach statistical significance in either season. However, building mean insulation values showed greater variability in the naturally ventilated building sample compared to the HVAC sample. Figure 3.12 indicates a statistically significant relationship between the mean level of thermal insulation worn inside a building and its mean indoor temperature. The scattergrams in Figure 3.12 suggest that an exponential decay model might fit better than the straight line printed in the graphs. However, due to the particular weighting Basic Results page 94 MRL Australia ASHRAE RP-884 Final Report All Buildings 1.8 insul = 1.87 - 0.04 * top 1.6 R2 = 0.51, p = 0.0001 1.4 1.2 1.0 0.8 0.6 clothing + chair (clo) 0.4 0.2 0.0 10 15 20 25 30 35 mean indoor operative temperature (oC) Central HVAC and Mixed Mode Naturally Ventilated buildings Buildings 1.8 1.8 1.6 insul = 1.73 - 0.04 * top 1.6 R2 = 0.18, p = 0.0001 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 insul = 2.08 - 0.05 * top clothing +chair (clo) 2 chair +clothing (clo) 0.2 0.2 R = 0.66, p = 0.0001 0.0 0.0 10 15 20 25 30 35 10 15 20 25 30 35 o mean indoor operative temperature (oC) mean indoor operative temperature ( C) Figure 3.12: Thermal insulation inside buildings (mean ± stdev) as a function of mean indoor operative temperatures factors (i.e. sample sizes) applying to each point (building) in the graphs, the R2 statistic was greatest for the simple linear fits shown in Figure 3.12. The correlation can be described as “moderate” in the case of the “all buildings” graph of Figure 3.12. The lower panels indicate that a much stronger relationship in the naturally ventilated buildings. This finding is possibly due to the greater range of temperatures (independent variable) encountered in the naturally ventilated building sample compared to the central HVAC/mixed mode building sample. Basic Results page 95 MRL Australia ASHRAE RP-884 Final Report The error bars either side of the plotted points in Figure 3.12 represent ± one standard deviation around the within-building mean. All three panels of Figure 3.12 indicate a general tendency for the standard deviation bars to contract in towards the mean, i.e. the variability of clothing insulation to decrease, as indoor temperature increased. This possibly reflects a diminution of degrees of freedom to adjust clothing as the number of individual garments being worn reduced towards the socially acceptable minimum dress standards. All Buildings 1.8 insul = 1.66 - 0.04 * neut_top 2 1.6 R = 0.13, p = 0.0002 1.4 1.2 1 0.8 0.6 clothing + chair (clo) 0.4 0.2 0 16 18 20 22 24 26 28 30 neutral indoor operative temperature (oC) Central HVAC and Mixed Mode Naturally Ventilated Buildings Buildings 1.8 1.8 insul = 1.98 - 0.05 * neut_top 1.6 1.6 R2 = 0.33, p = 0.0001 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 insul = 1.66 - 0.04 * neut_top clothing + chair (clo) clothing + chair (clo) 0.2 0.2 R2 = 0.10, p = 0.0607 0.0 0.0 16 18 20 22 24 26 28 30 16 18 20 22 24 26 28 30 neutral indoor operative temperature (oC) neutral indoor operative temperature (oC) Figure 3.13: Thermal insulation inside buildings (mean ± stdev) as a function of neutral operative temperature. Basic Results page 96 MRL Australia ASHRAE RP-884 Final Report Figure 3.13 plots mean thermal insulation inside each building in relation to neutral operative temperature. While the linear regression model is significant for the “all buildings” panel, the lower panels of Figure 3.13 suggest that this is primarily attributable to the central HVAC and mixed-mode buildings. 3.1.6.2. Metabolic rate adjustments indoors Figure 3.14 depicts mean metabolic heat production estimates within each building in relation to the mean indoor temperatures prevailing within the building. The error bars represent ± one standard deviation. Apart from Brown’s eight industrial buildings which had mean metabolic rates in the 2~3 met unit range, seven of which appear as outliers, the remaining buildings in the RP-884 database had mean metabolic rates tightly clustered in the 1.1~1.4 met unit range. Figure 3.14 indicates no discernible relationship between mean metabolic rates, their within-building standard deviations, or mean temperatures within buildings. Figure 3.15 repeats the analysis of mean metabolic rates, this time in relation to the neutrality observed inside each of the RP-884 database buildings. It seems reasonably clear that there was no systematic relationship between metabolic rates and the temperatures which building occupants described as “neutral.” Basic Results page 97 MRL Australia ASHRAE RP-884 Final Report All Buildings 3.5 met = 1.53 - 0.01 * top R2 = 0.01, p = 0.4419 3.0 2.5 2.0 1.5 1.0 mean metabolic rate (met) 0.5 0.0 10 15 20 25 30 35 mean indoor operative temperature (oC) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 3.5 met = 1.53 - 0.01 * top 3.5 met = 1.09 + 0.003 * top R2 = 0.01, p = 0.4419 3.0 3.0 R2 = 0.05, p = 0.1656 2.5 2.5 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 mean metabolic rate (met) mean metabolic rate (met) 0.0 0.0 10 15 20 25 30 35 10 15 20 25 30 35 o o mean indoor operative temperature ( C) mean indoor operative temperature ( C) Figure 3.14: Mean metabolic rates (mean ± stdev) within buildings plotted in relation to mean operative temperature indoors. Basic Results page 98 MRL Australia ASHRAE RP-884 Final Report All Buildings 3.0 met = 1.32 - 0.01 * neut_top R2 = 0.02, p = 0.2134 2.5 2.0 1.5 1.0 mean metabolic rate (met) 0.5 0.0 16 18 20 22 24 26 28 30 o neutral indoor operative temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 3.0 3.0 met = 1.32 - 0.01 * neut_top met = 1.29 - 0.005 * neut_top R2 = 0.003, p = 0.6707 R2 = 0.03, p = 0.3454 2.5 2.5 2.0 2.0 1.5 1.5 1.0 1.0 0.5 0.5 mean metabolic rate (met) mean metabolic rate (met) 0.0 0.0 16 18 20 22 24 26 28 30 16 18 20 22 24 26 28 30 o o neutral indoor operative temperature ( C) neutral indoor operative temperature ( C) Figure 3.15: Mean metabolic rates (mean ± stdev) within buildings plotted in relation to neutral operative temperature 3.1.6.3. Air speed adjustments indoors Table 3.13 presents the means and standard deviations of the within-building mean air speeds. In all three types of building - HVAC, NV, and mixed-mode, there was a decrement in mean indoor speeds from summer to winter. The seasonal difference reached statistical significance in the case of HVAC and NV buildings (T=3.98, df=100, p<0.001 for HVAC; T=3.62, df=41, p<0.001 for NV). The mean HVAC building air speeds in both summer and winter samples fell below the draft limit of 0.2 m s-1 specified in ASHRAE Standard 55-92 for situations in which the occupant has no control. The summer sample in Table 3.13 indicates mean air speeds within the naturally ventilated buildings were twice as fast as in the HVAC sample -- this difference was statistically significant (T=7.8, df=103, p<0.001). Basic Results page 99 MRL Australia ASHRAE RP-884 Final Report Table 3.13: Statistics for mean indoor air speeds (m s-1). centrally heated/air- naturally ventilated mixed-mode buildings conditioned buildings buildings number of buildings in 74 31 1 summer sample* 5 missing values 2 missing values 1 missing value mean VELAV (±stdev) in 0.11 0.23 0.16 the summer sample (± 0.037) (± 0.120) (± 0) number of buildings in 28 12 2 winter sample* 4 missing values no missing values no missing values mean VELAV (±stdev) in 0.08 0.10 0.12 the winter sample (± 0.024) (± 0.047) (± 0.033) Figure 3.16 plots the within-building means and standard deviations for indoor air speeds in relation to mean temperatures indoors. The top panel indicates that almost half of the between-building variance in the mean indoor air speeds could be accounted for by variations in mean temperatures. The relationship was best approximated by a model that expressed indoor air speed as an exponential function of indoor temperature. The lower panels in Figure 3.16 indicate that this statistically significant relationship extended to both central HVAC and naturally ventilated buildings, although the relationship was a simple linear one in the case of HVAC buildings. This was probably because the range of mean temperatures and mean air speeds observed in the HVAC building sample was relatively restricted. But even across this restricted range of temperatures, the variability of indoor air speeds, as indicated by the error bars (mean ±stdev) in Figure 3.16, tended to increase as mean indoor temperatures increased. A stronger dependence of mean air speeds on mean temperatures was observed in the sample of naturally ventilated buildings (lower right-hand panel of Figure 3.16). The relationship for these buildings was clearly exponential and the model was capable of explaining 53% of the between-building variance in mean air speeds. Basic Results page 100 MRL Australia ASHRAE RP-884 Final Report All Buildings 1.0 vel = 0.0048e 0.1314 * top 2 R = 0.4664, p < 0.05 0.8 0.6 0.4 mean indoor velocity (m/s) 0.2 0.0 10 15 20 25 30 35 o mean indoor operative temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 1.0 1.0 vel = -0.56 + 0.03 * top vel = 0.0077e 0.1174 * top R2 = 0.34, p = 0.0001 R2 = 0.5312, p < 0.05 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 mean indoor velocity (m/s) mean indoor velocity (m/s) 0.0 0.0 10 15 20 25 30 35 o 10 15 20 25 30 o 35 mean indoor operative tempetature ( C) mean indoor operative temperature ( C) Figure 3.16: Indoor air speeds (building mean ± stdev) plotted in relation to mean operative indoor temperature. Figure 3.17 examines the relationship between mean indoor air speed and the temperature judged as “neutral” by each building’s occupants. As with the regressions on mean temperature, the air speed observations were best approximated by a simple linear model in the HVAC building sample. But the relationship across the extended range of dependent and independent variables in naturally ventilated buildings was best approximated with an exponential model, accounting for 35% of the between-building variance in mean speeds Basic Results page 101 MRL Australia ASHRAE RP-884 Final Report All Buildings 1.0 vel = 0.005e0.137 * neut_top R2 = 0.2407, p < 0.05 0.8 0.6 0.4 mean indoor velocity (m/s) 0.2 0.0 16 18 20 22 24 26 28 30 o neutral indoor operative temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 1.0 1.0 vel = -0.16 + 0.01 * neut_top vel = 0.0068e 0.1358 * neut_top R2 = 0.08, p = 0.0288 R2 = 0.354, p < 0.05 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 mean indoor velocity (m/s) mean indoor velocity (m/s) 0.0 0.0 16 18 20 22 24 26 28 30 16 18 20 22 24 26 28 30 o o neutral indoor operative temperature ( C) neutral indoor operative temperature ( C) Figure 3.17: Indoor air speeds (building mean ± stdev) plotted in relation to neutral operative temperature. 3.2. Interactions with outdoor weather and climate Much of what has been published to date on the subject of adaptive models of comfort has emphasised the role of external climatic environment in forcing behavioral adjustments, physiological acclimatization and thermal expectations. This section presents comfort data from the RP-884 database in relation to the weather and climatic conditions prevailing outside the study buildings. 3.2.1. Thermal neutrality and outdoor climate Outdoor climate can be represented in these meta-analyses at two levels of detail: a) simple seasonal comparisons (summer v winter), and Basic Results page 102 MRL Australia ASHRAE RP-884 Final Report b) mean outdoor average ET* (dayav_et) during the period of each building’s survey 3.2.1.1. Seasonal comparisons The raw data files obtained from original field researchers were classified by the RP-884 team into summer or winter, depending on month and location. Tropical locations were all regarded as summer regardless of month. Table 3.14: Seasonal comparisons of thermal neutralities* defined in terms of the four major indoor thermal indices (TOP, ET, PMV and SET) centrally heated/air- naturally ventilated conditioned buildings buildings t-test of the difference between the summer and t = 4.50 t = 2.09 winter samples for neut_top df = 59 df = 32 p < 0.001 p < 0.05 t-test of the difference between the summer and t = 3.76 t = 1.47 winter samples for neut_et df = 59 df = 32 p < 0.001 p > 0.1 t-test of the difference between the summer and t = 4.14 t = 1.08 winter samples for neut_pmv df = 28 df = 25 p < 0.001 p > 0.2 t-test of the difference between the summer and t = 0.8 t = 3.47 winter samples for neut_set df = 30 df = 25 p > 0.2 p < 0.002 * only results from buildings with statistically significant regression models in Appendix A were used in this table Thermal neutralities defined in terms of the simpler indices of operative temperature and new effective temperature were significantly differentiated between seasons in centrally heated or air-conditioned buildings, with the average neutrality in summer being about one and a half degrees (K) warmer than in winter. However, their seasonal differences for neutralities on the more sophisticated thermal indices of PMV and SET reached statistical significance in Table 3.14 only for the PMV index. The smaller sample size of naturally ventilated buildings in the RP-884 meant that the seasonal comparisons in neutralities were less clear cut than they were for HVAC buildings -- e.g. only two of the naturally ventilated buildings in winter achieved significant SET Basic Results page 103 MRL Australia ASHRAE RP-884 Final Report regression models. As a result, only neutral operative temperatures and neutral Standard Effective Temperatures were significantly differentiated between the summer and winter seasons in Table 3.14. While the seasonal difference in neutral operative temperature was over two degrees (K) and in the direction one might expect from the adaptive model’s perspective, it should be pointed out that SET index neutrality difference between seasons had a counterintuitive sign -- the winter neutrality was warmer than the summer one. Not listed in Table 3.14 due to small sample sizes, but worth mentioning is the seasonal difference in neutrality for buildings classified as “mixed mode”. Only one such building managed a significant regression model with operative temperature in summer, but its neutrality in that season was 23.9°C compared to the average winter neutrality of 20.7°C recorded in such buildings. 3.2.1.2. Dependence of observed neutrality on outdoor climate Linear regression models were constructed for the relationship between indoor thermal neutrality and mean outdoor warmth. The former was assessed in terms of the operative temperature index (neut_top) while outdoor warmth was parameterized in terms of mean daily effective temperature (dayavet). Figure 3.18 shows the resulting regression models, and the “all buildings” panel indicates a reasonably strong correlation for this relationship, with r = +0.65. The regression coefficient in that model suggests that operative temperature neutrality indoors changes by one degree (K) for about six degrees change in mean daily outdoor effective temperature. Basic Results page 104 MRL Australia ASHRAE RP-884 Final Report all buildings from the RP-884 database 29 27 25 C) o 23 21 temperature ( 19 neutral indoor operative 17 neutrality = 20.9 + 0.16 (outdoor ET*) R2 = 0.42, p = 0.0001 15 -5 0 5 10 15 20 25 30 35 mean outdoor effective temperature (oC) Central HVAC and Mixed Mode buildings, from naturally ventilated Buildings from the the RP-884 database RP-884 database 29 29 27 27 C) C) o 25 o 25 23 23 21 21 19 19 temperature ( temperature ( neut_top = 21.5 + 0.11 * dayavet neut_top = 18.9 + 0.255 * dayavet neutral indoor operative 17 neutral indoor operative 17 R2 = 0.53, p = 0.0001 R2 = 0.42, p = 0.0001 15 15 -5 0 5 10 15 20 25 30 35 -5 0 5 10 15 20 25 30 35 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Figure 3.18: Dependence of indoor neutrality on outdoor climate The slope of the model for naturally ventilated buildings in Figure 3.18 indicates that indoor thermal neutrality increased by approximately one degree (K) for every four degree (K) increase in mean daily outdoor effective temperature (dayavet). The gradient for centralized HVAC buildings was less than half the naturally ventilated buildings’ result. Using the T-test method for comparing two straight lines using separate regression fits, as described in Kleinbaum et al. (1988), we obtained a T statistic of 3.25 (d.f.=101), which was statistically significant (p < 0.01). Basic Results page 105 MRL Australia ASHRAE RP-884 Final Report 3.2.1.3. Analysis of predicted neutralities with respect to mean outdoor temperature The PMV model was used to predict thermal neutralities for each of the buildings in the RP- 884 database simply by using building mean values for each of the heat balance model’s input parameters, and then iterating the operative temperature input until PMV=0. The predicted neutralities, codenamed predneut, have been plotted in Figure 3.19 in relation to mean daily average outdoor effective temperatures. With the exception of Brown’s seven HVAC light industrial buildings, predicted neutralities demonstrate a moderate linear dependence on outdoor climate. The anomalous industrial buildings probably result from the significantly higher metabolic rates of their occupants. Metabolic rate is one of the six input parameters to the heat balance model which was used to predict these neutralities. Figure 3.20 shows the regression model for HVAC buildings with these outliers excluded from the analysis. While the regression coefficient changed little after these exclusions, the amount of variance in predneut accounted for by the model increased to 25% Basic Results page 106 MRL Australia ASHRAE RP-884 Final Report All Buildings 30 25 C) o 20 predicted neutrality ( 15 predneut = 21.6 + 0.08 * dayavet R 2 = 0.14, p = 0.0001 10 -30 -20 -10 0 10 20 30 40 o mean outdoor effective temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 30 30 predneut = 21.5 + 0.07 * dayavet R2 = 0.10, p < 0.001 C) C) o o 25 25 20 20 15 15 predicted neutrality ( predicted neutrality ( predneut = 21.6 + 0.12 * dayavet R 2 = 0.30, p < 0.001 10 10 -30 -20 -10 0 10 20 30 40 -30 -20 -10 0 10 20 30 40 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Figure 3.19: Dependence of PMV-based neutrality predictions on outdoor climate Basic Results page 107 MRL Australia ASHRAE RP-884 Final Report Central HVAC and Mixed Mode Buildings 30 C) o 25 20 15 predneut = 22.6 + 0.04 * dayavet predicted neutrality ( 2 R = 0.25, p <0.001 10 -30 -20 -10 0 10 20 30 40 mean outdoor effective temperature (oC) Figure 3.20: Dependence of PMV-based neutrality predictions on outdoor climate for HVAC buildings with sedentary occupants The statistical significance of regression models for PMV-predicted neutrality, as plotted in Figures 3.19 and 3.20, probably reflects the dependence of some of the basic heat-balance variables such as clothing insulation and indoor air speed on outdoor climate. These mediating variables will be subjected to further detailed analysis in relation to outdoor climate in a subsequent section dealing with behavioral responses (Section 3.2.4). The predicted neutralities in naturally ventilated buildings (Figure 3.19) were almost twice as sensitive to outdoor temperature than was the case for HVAC buildings (Figure 3.19), and this was confirmed with Kleinbaum’s (1988) statistical test for the difference between independent regression coefficients (T=3.64, df=148, p<0.01). 3.2.2. Thermal acceptability and outdoor climate Section 3.1.3 indicated that thermal satisfaction ratings within buildings bore little relationship with mean indoor climatic indices (Figure 3.6). This section examines whether or not thermal acceptability is related to outdoor climate in any way. Figure 3.21 presents polynomial regression models separately for a) directly assessed thermal acceptability (tsa), and b) thermal acceptability inferred from thermal sensation votes (prxy_tsa). The y-axis Basic Results page 108 MRL Australia ASHRAE RP-884 Final Report variables in both graphs represent the number of occupants within each building expressing thermal acceptability as a percentage of the whole building sample. Both direct and inferred versions of building thermal acceptability ratings failed to show any signs of a statistically significant relationship with outdoor climate in Figure 3.21. Directly assessed thermal acceptability, all buildings Thermal acceptability inferred from thermal sensation 100 votes, all buildings 100 80 80 60 60 40 40 acceptable) Proxy TSA (% TSA (% acceptable) 20 tsa = 72.78 - 0.30 * dayavet + 0.02 * dayavet2 20 prxy_tsa = 79.66 - 0.25 * dayavet + 0.01 * dayavet 2 R2 = 0.06, p = 0.1683 R2 = 0.003, p = 0.7682 0 0 -30 -20 -10 0 10 20 30 40 -30 -20 -10 0 10 20 30 40 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Figure 3.21: Thermal acceptability and outdoor climate. TSA represents directly assessed thermal acceptability levels within each building and PRXY_TSA represents thermal acceptability inferred from thermal sensation votes. A building’s thermal acceptability rating, as inferred from thermal sensation votes, is logically related to the gradient of that building’s thermal sensation regression models with respect to indoor temperature (Appendix A). The range of acceptable operative temperatures for each building was defined as rang_top in the RP-884 meta-analysis and presented earlier in Section 3.1.3.4 simply by solving the mean thermal sensation versus mean indoor operative temperature regression model (Appendix A) for mean ASHRAE thermal sensation votes of ±0.85. These values were chosen on the basis of Fanger’s PMV/PPD model (Fanger, 1970) which suggests they correspond to 80% acceptability levels (PPD=20%). The complete lack of any statistical relationship between the range of acceptable indoor operative temperatures (rang_top) and mean daily outdoor effective temperature (dayavet) is evident in Figure 3.22. Basic Results page 109 MRL Australia ASHRAE RP-884 Final Report Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 14 14 rang_top = 4.07 + 0.02 * dayavet rang_top = 8.63 - 0.04 * dayavet 2 12 R = 0.01, p = 0.8460 12 R2 = 0.01, p = 0.6752 10 10 8 8 6 6 rang_top (K) 4 rang_top (K) 4 2 2 0 0 -5 0 5 10 15 20 25 30 35 -5 0 5 10 15 20 25 30 35 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Figure 3.22: The range of acceptable operative temperatures indoors plotted in relation to the mean outdoor effective temperature. 3.2.3. Thermal preference and outdoor climate Section 2.8.5 described how preferred temperature was derived for each building with the MCI questionnaire item by locating the intersection of the “want cooler” probit model with the “want warmer” model (see Appendix B). The indoor operative temperature corresponding with the intersection of “cooler” and “warmer” probit models was incorporated into the RP- 884 meta-analysis as preftemp. Conceptually, preftemp is directly analogous to the neutrality (neut_top), only it was derived from thermal preference votes instead of thermal sensations. Regression models of the dependence of preftemp on mean outdoor effective temperature (dayavet) are presented in Figure 3.23. Basic Results page 110 MRL Australia ASHRAE RP-884 Final Report All Buildings 30 preftemp = 22.49 - 0.01 * dayavet + 0.003 * dayavet2 R2 = 0.13, p = 0.0004 28 C) o 26 24 22 preferred temperature ( 20 18 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 o mean outdoor effective temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 30 30 preftemp = 22.82 - 0.001*dayavet + 0.001 * dayavet2 2 preftemp = 19.3 + 0.22 dayavet C) R = 0.05, p = 0.3871 C) o 28 2 o 28 R = 0.43 p<0.00001 26 26 24 24 22 22 20 20 preferred temperature ( preferred temperature ( 18 18 -25 -15 -5 5 15 25 35 -25 -15 -5 5 15 25 35 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Figure 3.23: Dependence of indoor preferred temperatures on outdoor climate The regression models show a relatively weak relationship between indoor temperature preferences and outdoor climate in the “all buildings” panel of Figure 3.23. A slightly parabolic trend is discernible in the plot, but there is insufficient data from climates with sub- zero mean daily outdoor effective temperatures to be confident about this trend. The temperature preference data become clearer when they are plotted separately for the HVAC and NV building sub-samples in Figure 3.23. Basically there is no discernible relationship in the case of HVAC and mixed-mode buildings, with the second order polynomial model failing to reach statistical significance at the 0.05 level. However, the naturally ventilated sub-sample of preferred temperatures, albeit small (n=30), demonstrates a clear Basic Results page 111 MRL Australia ASHRAE RP-884 Final Report relationship with outdoor climate, with the linear regression model explaining 43% of the variance (r= +0.66). The NV subsample’s scatter plot in Figure 3.23 suggest a slightly curvilinear relationship, and although the addition of a second-order polynomial term in the regression model lifted the R2 statistic to 46%, neither the first-order nor second-order regression coefficients reached statistical significance (T=0.55, p=0.54; T=1.12, p=0.25 for X1 and X2 terms respectively), so the simple linear regression model has been retained for Figure 3.23. Since preferred temperatures represent an alternative definition to thermal neutrality for optimal indoor temperature, it becomes interesting to compare them. Traditionally, thermal comfort researchers have regarded thermal neutrality and preference as synonymous, but some published evidence suggests that there may in fact be a semantic discrepancy in the way the two scales are actually interpreted by building occupants (McIntyre, 1978; de Dear et al, 1991c). This semantic artefact hypothesis predicts that thermal neutralities will shift to warmer temperatures than actually preferred when the climatic context is warm, while the offset will be cooler-than-preferred in cold climatic contexts. Figure 3.24 tests this hypothesis by plotting the discrepancy between neutrality and preference (discrep) in relation to mean outdoor effective temperature. The main panel (“all buildings”) depicts a statistically significant weighted regression model with the slope as predicted by the semantic artefact hypothesis. However, the linear fit is very poor, with the model’s gradient indicating that thermal neutrality drifts apart from preference at the rate of only one degree (K) for every 25 K shift in mean outdoor effective temperature. The model fitted to the small sample of naturally ventilated buildings failed to achieve any statistical significance, but the same was not true for the HVAC and mixed-mode sample of buildings. The lower left panel of Figure 3.24 indicates the linear model accounted for about 38% of the variance in discrep and that thermal neutrality diverged from preference at the rate of about one degree (K) for every 14 K change in outdoor temperature. Basic Results page 112 MRL Australia ASHRAE RP-884 Final Report All Buildings 3 2 1 C) o 0 -1 discrep ( -2 -3 discrep = -0.42 + 0.04 * dayavet R 2 = 0.06, p = 0.0195 -4 -5 0 5 10 15 20 25 30 35 o mean outdoor effective temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 3 3 discrep =-0.95 + 0.07 * dayavet R 2 = 0.38, p = 0.0001 2 2 1 1 C) C) o 0 o 0 -1 -1 discrep ( -2 discrep ( -2 -3 -3 discrep = 0.23 + 0.01 * dayavet R2 = 0.001, p = 0.8501 -4 -4 -5 0 5 10 15 20 25 30 35 o -5 0 5 10 15 20 25 30 o 35 mean outdoor effective temperature ( C) mean daily outdoor effective temperature ( C) Figure 3.24: The discrepancy (discrep) between thermal neutrality (neut_top) and preference (preftemp) plotted against mean outdoor climate (dayavet). 3.2.4. Behavioural responses to outdoor climate Since all of the input variables to the heat balance model of thermal comfort are available within the RP-884 database, it is possible to explore their variations and relationships with respect to outdoor climate. This subsection focuses on the main adaptive adjustments involved in the human body’s heat balance with indoor climate -- in particular, clothing insulation, metabolic rate and indoor air speed. Basic Results page 113 MRL Australia ASHRAE RP-884 Final Report 3.2.4.1. Indoor clothing and outdoor climate The amount of clothing insulation worn indoors was shown in Section 3.1.6.1. to be related to indoor climatic indices such as mean indoor operative temperature. It seems reasonable to expect that clothing decisions and behavior are also influenced by outdoor weather and climatic differences. To examine this possibility Figure 3.25 plots mean thermal insulation values for the occupants of each building (insul), comprised of both clothing and chair components, against mean outdoor effective temperatures prevailing at the time of the building samples survey. For the HVAC and naturally ventilated building samples combined, 40% of the variance in clo values was explained by variations in the outdoor climatic index. An exponential decay curve fitted the data significantly better than a straight line model, probably reflecting the effects of a minimum socially acceptable level of thermal insulation at about 0.4 clo units (after subtracting 0.15 clo units for chair effects). The error bars (standard deviations) around each building point plotted in Figure 3.25 suggest a trend towards increasing homogeneity in thermal insulation for those buildings located in warmer climates. This presumably also reflects the fact that clothing decisions and behavior have fewer degrees of freedom as the level of clothing approaches the minimum socially acceptable threshold. When the clothing database was disaggregated by building type (HVAC v NV), thermal insulation was also found to decay exponentially with outdoor temperature in the HVAC buildings where the regression model was found to account for about 64% of the variance in insul. However, in the case of naturally ventilated buildings, a straight line regression model produced the best fit to the data, with only 44% of variance being explained (r= -0.66). The rate of insulation change with respect to outdoor temperature within the naturally ventilated buildings was almost one tenth of a clo unit for every three degrees (K) of outdoor effective temperature change, and this gradient appears in Figure 3.25 to be significantly steeper than in the HVAC and mixed-mode buildings. Basic Results page 114 MRL Australia ASHRAE RP-884 Final Report All Buildings 1.8 1.6 1.4 1.2 1.0 0.8 0.6 clothing +chair (clo) 0.4 -0.0133 * dayavet 0.2 insul = 0.9346e R2 = 0.40, p < 0.05 0.0 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 o mean outdoor effective temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 1.8 1.8 1.6 1.6 1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 chair +clothing (clo) 0.4 clothing +chair (clo) insul = 0.9343e-0.0127 * dayavet insul = 1.44 - 0.03 * dayavet 0.2 0.2 R 2 = 0.64, p < 0.05 R 2 = 0.44, p = 0.0001 0.0 0.0 -25 -15 -5 5 15 25 35 -25 -15 -5 5 15 25 35 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Figure 3.25: Building occupants’ thermal insulation (clothing plus chair) as a function of outdoor temperature 3.2.4.2. Metabolic rate indoors related to outdoor climate Figure 3.26 indicates a complete absence of any systematic relationship between mean metabolic rates registered within buildings and the mean outdoor temperature prevailing at the time of the survey. This generalization applies to both HVAC and naturally ventilated buildings. Basic Results page 115 MRL Australia ASHRAE RP-884 Final Report All Buildings 3.5 met = 1.18 - 0.000004 * dayavet R 2 = 0.00, p = 0.9977 3.0 2.5 2.0 1.5 1.0 mean metabolic rate (met) 0.5 0.0 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 mean daily outdoor effective temperature (oC) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 3.5 3.5 3.0 3.0 2.5 2.5 2.0 2.0 1.5 1.5 1.0 1.0 0.5 met = 1.21 - 0.0005 * dayavet 0.5 met = 1.13 + 0.002 * dayavet mean metabolic rate (met) R2 = 0.001, p = 0.7527 mean metabolic rate (met) R2 = 0.03, p = 0.2851 0.0 0.0 -25 -15 -5 5 15 25 35 -25 -15 -5 5 15 25 35 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Figure 3.26: Metabolic rates of building occupants plotted in relation to mean outdoor climate 3.2.4.3. Indoor air speeds in relation to outdoor climate Occupants of buildings, both with and without centralized HVAC, tend to increase indoor air movement when or where temperatures increase. In the case of naturally ventilated buildings in humid climates, this is typically achieved by opening windows and turning on fans. In very hot and dry climates, windows are often kept shut, leaving just indoor fans to accelerate air movement within the occupied zone. The same is often the case in buildings with centralized HVAC services -- because windows are typically sealed, occupants resort to using localised fans to supplement the typically low levels of air movement generated Basic Results page 116 MRL Australia ASHRAE RP-884 Final Report within the occupied zone by conventional HVAC diffusers. All of this applies to situations of elevated indoor temperature. The present section explores the relationship between indoor air speeds and outdoor climate. Obviously indoor temperatures correlate with those outdoors for naturally ventilated buildings, but this should not be the case in HVAC buildings if the current (static) thermal comfort standards (ISO and ASHRAE) are being strictly applied. All Buildings 1.0 velav = 0.07 e0.0258 * dayavet R 2 = 0.25, p < 0.05 0.8 0.6 0.4 mean indoor velocity (m/s) 0.2 0.0 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 o mean outdoor effective temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings 1.0 1.0 0.0135 * dayavet 0.0758 * dayavet velav = 0.08e velav = 0.03 e 2 0.8 2 0.8 R = 0.19, p<0.05 R = 0.64, p < 0.05 0.6 0.6 0.4 0.4 0.2 0.2 mean indoor velocity (m/s) mean indoor velocity (m/s) 0.0 0.0 -25 -15 -5 5 15 25 35 -25 -15 -5 5 15 25 35 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Figure 3.27: Relationship between indoor air speeds and outdoor climate Basic Results page 117 MRL Australia ASHRAE RP-884 Final Report Figure 3.27 plots mean building air speeds against the mean outdoor effective temperature at the time of the building’s survey. The error bars around each data point represents ± one standard deviation. The main panel of Figure 3.27 indicates a moderate correlation between mean indoor air speeds and mean outdoor temperatures (r= +0.50). An exponential function rather than straight line achieved the best fit to the data, reflecting the effects of a minimum mean air speed of about 0.05 m s-1. The graph demonstrates clearly that not only the mean speed within a building increases as outdoor temperature increases, but also the variability in speeds around the mean increases. These generalizations extend to the separate HVAC and NV samples’ analyses in Figure 3.27. The model fitted to the HVAC and mixed-mode sample indicates a modest increase in air speeds from an average of below 0.10 m s-1 in cold climates to about 0.2 m s-1 in the hot climates. Whether this increase is due to increased air speeds from HVAC diffusers or local air movement generated by desk, ceiling or floor fans is unsure, but the relationship accounted for 23% of variance in mean buildings air speeds (r=0.48). The strongest correlation in Figure 3.27 was found in the naturally ventilated buildings where an exponential regression model accounted for almost 64% of the variance in the dependent variable (r= +0.80). Mean air speeds in cold-climate naturally ventilated buildings were similar to those in HVAC buildings, below 0.10 m s-1, but increased to values in excess of 0.4 m s-1 in the warmer climates represented in the RP-884 database. 3.3. Influence of building characteristics on thermal comfort Comparisons between thermal comfort experiences of HVAC buildings and naturally ventilated buildings have been made throughout the preceding sections of this chapter and several significant differences have been discussed. The present section goes further into the analyses of these contextual factors (as opposed to indoor or outdoor climatic features), including the index of perceived control which we introduced in Section 2.4. 3.3.1. HVAC versus natural ventilation The regression gradients depicted for each building in Appendix A suggest that thermal sensations were about twice as sensitive to changes in indoor operative temperature in centrally heated and air-conditioned buildings than in naturally ventilated buildings. Basic Results page 118 MRL Australia ASHRAE RP-884 Final Report Unfortunately the sample size of mixed mode buildings was too small to draw any conclusive comparisons, but the mean gradient fell, as might be expected of such buildings, about midway between that for HVAC and naturally ventilated buildings. This heightened sensitivity in HVAC buildings extended to the ET index as well, but did not persist when the fully developed heat-balance indices such as PMV and SET were subjected to the regression analyses, suggesting that factors such as clothing insulation and air speed were responsible for any differences observed when using the simpler indoor climatic indices. 3.3.1.1. Thermal sensation and sensitivity in HVAC versus naturally ventilated buildings Comparisons between thermal neutralities in centrally heated/air-conditioned building sample with those in the naturally ventilated buildings have been summarised in Table 3.15. Table 3.15: Comparisons of thermal neutrality observed in the HVAC and naturally ventilated building samples Neutrality defined in terms of four indoor summer winter climatic indices sample sample t-test of the difference between HVAC and NV t = 1.16 t = 0.14 buildings for neut_top df = 72 df = 19 p > 0.1 p > 0.5 t-test of the difference between HVAC and NV t = 1.12 t = 1.00 buildings for neut_et df = 70 df = 21 p > 0.2 p > 0.2 t-test of the difference between HVAC and NV t = 1.48 not valid due buildings for neut_pmv df = 47 to low p > 0.1 sample size t-test of the difference between HVAC and NV t = 0.61 not valid due buildings for neut_set df = 47 to low p > 0.5 sample size * only results from buildings with statistically significant regression models used in these comparisons None of the HVAC - NV comparisons in Table 3.15 reached statistical significance. That is, in summer or in winter, there was no significant difference in neutrality between centrally conditioned and naturally ventilated buildings, regardless of which thermal index was used to define it. As noted earlier in Table 3.1, Section 3.1.1.1, thermal sensitivity (i.e. the rate of change in sensation votes, ASH, with respect to indoor temperature) was greater in the centrally Basic Results page 119 MRL Australia ASHRAE RP-884 Final Report heated/cooled buildings than in naturally ventilated buildings within the database. This was particularly so when indoor warmth was defined simply in terms of operative or effective temperature indices. But again, when the fully-developed heat balance indices of indoor warmth, namely PMV and SET, were substituted into the analysis, these differences became less significant, suggesting that heightened thermal sensitivity in HVAC buildings was at least partly the result of other heat balance factors such as clothing and air speed remaining relatively static. Conversely, the relative thermal insensitivity of occupants of naturally ventilated buildings appears to be largely the result of the ability to manipulate physical variables affecting their body’s heat balance. Table 3.16 below summarises the comparisons between classes of buildings in the database. Table 3.16: Comparison of thermal sensitivity for centrally controlled buildings (HVAC) and naturally ventilated buildings (NV) ASH v TOP ASH v ET ASH v PMV ASH v SET model mean gradient for HVAC buildings 0.51 0.50 0.74 0.21 model mean gradient for NV buildings 0.27 0.28 0.62 0.18 t-test of the difference between HVAC and t = 5.37 t = 4.45 t = 1.56 t = 1.00 NV model gradient means df = 97 df = 96 df = 56 df = 57 p < 0.001 p < 0.001 p > 0.1 p > 0.2 * only results from buildings with statistically significant regression models used The question left unanswered in this table is “why are occupants of naturally ventilated buildings inclined to behaviorally regulate their heat balance to a greater extent than their counterparts within HVAC buildings?” Is it the result of greater adaptive opportunities in naturally ventilated buildings, particularly with respect to air speed, or is it the result of a reluctance to thermoregulate with clothing adjustments on the part of HVAC building occupants? Questions of adaptive opportunity and perceived control will be examined later in Section 3.3.2. 3.3.1.2. Thermal acceptability in HVAC versus naturally ventilated buildings Earlier sections have demonstrated little systematic relationship, if any, between directly assessed thermal acceptability ratings and physical measurements of indoor climate. The Basic Results page 120 MRL Australia ASHRAE RP-884 Final Report percentage of building occupants voting “acceptable” was, on average, 81.6% in centrally air-conditioned buildings with a standard deviation of acceptability ratings at 10.1%. The mean rating is just at the minimum acceptability threshold suggested in the thermal comfort standards such as ASHRAE 55 and ISO 7730. The corresponding mean for naturally ventilated buildings was 66.8%, and the between-buildings standard deviation of 20.1% was twice that of HVAC sample. The difference of less than 15% acceptability, whilst statistically significant (T=3.74; df=59, p<0.001), suggests that centralized air-conditioning enhances perceived quality of internal environments only moderately. This interpretation, however, ignores questions about where the two samples were drawn from -- were the naturally ventilated buildings selected for the RP-884 database drawn from mild climate zones? Did the HVAC building sample cover a much broader spectrum of climates, including some which may have rendered passive architectural alternatives infeasible? Since directly assessed thermal acceptability ratings were not universally available throughout the database, attention turns to the indirect assessments derived from thermal sensation votes. An earlier section of the current chapter gave the definition of “range of acceptable temperatures” as those coinciding with mean thermal sensations of ±0.85 on the linear regression models of Appendix A. This temperature range for each building, codenamed rang_top in the meta-analysis, is inversely related to thermal sensitivity, as noted in the preceding section. Therefore it is not surprising to find that the mean rang_top in naturally ventilated buildings was about 70% wider than in centrally air-conditioned buildings (see Table 3.9). The significance of the difference in acceptable temperature ranges between HVAC and NV buildings was retained when the thermal index switched to SET (i.e. the RANG_SET variable T=2.49, df=55, p<0.02). This implies that the extended range of acceptability within naturally ventilated buildings could not be accounted for purely in terms of physical heat-balance adjustments (clothing and air speed), and that other types of adaptive response such as acclimatisation and shifting expectations may indeed influence thermal acceptability. 3.3.1.3. Thermal preferences in HVAC versus naturally ventilated buildings. Appendix B established the preferred operative temperatures for each building in which some variant of the so-called “McIntyre scale” (MCI) was presented on the questionnaire. Basic Results page 121 MRL Australia ASHRAE RP-884 Final Report The present section examines the dependence of preferred temperatures on indoor and outdoor warmth, separately, for HVAC and naturally ventilated buildings during winter and summer seasons. Figure 3.28 below presents the relationship between thermal preferences and indoor temperature. Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings in Summer in Summer 30 30 C) 28 o 28 26 26 C) 24 24 o ( 22 22 20 preftemp = 13.28 + 0.39 * top preferred temperature 20 preftemp = 30.38 - 0.31 * top 2 2 R = 0.34, p = 0.0029 R = 0.03, p = 0.1618 preferred temperature ( 18 18 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 o mean indoor operative temperature (oC) mean indoor operative temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings in Winter in Winter 30 30 preftemp = 13.52 + 0.47 * top C) 2 o R = 0.50, p = 0.1142 28 28 26 26 24 C) o 24 ( 22 22 20 preftemp = 16.22 + 0.29 * top preferred temperature 20 2 R = 0.02, p = 0.5165 preferred temperature ( 18 18 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 mean indoor operative temperature (oC) mean indoor operative temperature (oC) Figure 3.28: Dependence of thermal preferences on mean indoor warmth for HVAC and naturally ventilated buildings The relatively tight temperature control within HVAC (and mixed-mode buildings) is reflected as a restricted range on abscissa for Figure 3.28 and the statistically insignificant regression models fitted across this narrow band of the independent variable suggest thermal preferences were unrelated to mean temperatures inside HVAC buildings. However, the naturally ventilated buildings in Figure 3.28 present a significantly different picture, particularly those buildings sampled during summer, in which we found that the preferred temperature increased by one degree (K) for every 2.5 K increase in mean indoor Basic Results page 122 MRL Australia ASHRAE RP-884 Final Report temperature. The same trend was apparent in the winter sample as well, but due to the limited number of NV buildings sampled in that season, the regression model failed to reach statistical significance in the lower-right panel of Figure 3.28. Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings in Summer in Summer 30 30 preftemp = 17.29 + 0.29 * dayavet preftemp = 23.19 - 0.01 * dayavet 2 2 C) R =0.41, p = 0.0008 28 R = 0.0004, p = 0.8738 o 28 26 26 C) 24 24 o ( 22 22 20 preferred temperature 20 preferred temperature ( 18 18 -25 -15 -5 5 15 25 35 -25 -15 -5 5 15 25 35 o o mean outdoor effective temperature ( C) mean outdoor effective temperature ( C) Central HVAC and Mixed Mode Buildings Naturally Ventilated Buildings in Winter in Winter 30 30 preftemp = 19.78 + 0.26 * dayavet C) 2 o 28 R = 0.47, p = 0.1352 28 26 26 24C) 24 o ( 22 22 20 preftemp = 23.09 - 0.05 * dayavet 20 preferred temperature 2 R = 0.11, p = 0.1139 preferred temperature ( 18 18 -25 -15 -5 5 15 25 35 -25 -15 -5 5 15 25 35 mean outdoor effective temperature (oC) mean outdoor effective temperature (oC) Figure 3.29: Dependence of thermal preferences on mean outdoor warmth for HVAC and naturally ventilated buildings Figure 3.29 examines temperature preferences within HVAC and naturally ventilated buildings in relation to mean outdoor temperatures for summer and winter seasons. As was the case for the indoor analyses just presented in Figure 3.28, the insignificant models suggest that thermal preferences within HVAC buildings apparently have no systematic relationship with the temperatures prevailing outdoors. This generalization does not extend to naturally ventilated buildings, especially in the summer season where the RP-884 database contains a reasonable sample size. Figure 3.29 indicates that the operative Basic Results page 123 MRL Australia ASHRAE RP-884 Final Report temperature preferred inside such buildings increased by about one degree (K) for every three degrees (K) increase in mean daily outdoor effective temperature. 3.3.2. Personal environmental control The literature review of the adaptive thermal comfort hypothesis in Chapter 1 indicated that concepts of adaptive opportunity and perceived control play an important part in the processes of thermal perception. Unfortunately only a handful of field research projects within the RP-884 database explicitly included questionnaire items on these issues, and those that did may not have used directly comparable versions of questionnaire. The simple, albeit crude, solution proposed in the RP-884 database was to infer levels of perceived control for each building in the sample from the following items of information: · a knowledge of which adaptive opportunities were available within the building (operable windows, doors, thermostats, fans, blinds etc), and · how much each of these adaptive opportunities contributed to overall levels of perceived control. This second step was quantified on the basis of a sub-sample of RP-884 buildings where the relevant questionnaire items were available for detailed analysis (see Section 2.4). A synthesis of these details led to an index of perceived control (PCC_AG) for about two thirds of the buildings within the RP-884 database. This section of the RP-884 final report presents some basic descriptive statistics for this index and some preliminary analyses of its relationship with thermal perception (sensation, acceptability and preference). Table 3.17: Summary of the perceived control index (pcc_ag). centrally heated/air- naturally ventilated mixed-mode conditioned buildings buildings buildings number of buildings 76 30 2 (35 missing values) (15 missing values) (2 missing values) mean (±stdev) mpcc_ag 1.5 2.9 6.2 (±0.73) (±1.84) (±0.24) * based on those buildings in which adaptive opportunities were recorded. As might be expected, mean levels of the perceived control index were lowest in those buildings with centralized HVAC systems in place and highest in those buildings classified Basic Results page 124 MRL Australia ASHRAE RP-884 Final Report as “mixed mode” where benefits of both natural ventilation and air-conditioning were available for the occupants to use, as and when they saw fit. Naturally ventilated buildings typically had middle-ranking values on the perceived control index. The difference in mean pcc_ag between centralized HVAC and NV buildings was statistically significant (t = 5.52, df = 104, p < 0.001). Admittedly we are uncertain that the index of perceived control (PCC_AG) developed in this study conforms to all the assumptions necessary for linear regression. Bearing this caveat in mind, preliminary investigations of its relationships with thermal perceptual variables were performed. Several of the variables from earlier sections of this chapter represented logical candidates for these exploratory analysis. All Buildings All Buildings 1.7 1.7 grad_top = 0.36 - 0.02 * pcc_ag grad_set = 0.17 + 0.004 * pcc_ag 1.5 R2 = 0.03, p = 0.1574 1.5 R 2 = 0.01, p = 0.5392 1.3 1.3 1.1 1.1 0.9 0.9 0.7 0.7 0.5 0.5 0.3 0.3 (mean grad_set) (mean grad_top) thermal sensitivity thermal sensitivity 0.1 0.1 -0.1 -0.1 -0.3 -0.3 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 mean percieved control index (pcc_ag) mean percieved control index (pcc_ag) Figure 3.30: Regression analysis between thermal sensitivity and mean perceived control index (pcc_ag) The adaptive model predicts that occupants of buildings in which there is a high level of perceived control over thermal conditions will be less critical of indoor climatic conditions than those in tightly regulated environments. Translating this hypothesis to the RP-884 meta- analysis, Figure 3.30 plots each building’s thermal sensitivity statistic (dependence of thermal sensation votes on either operative or standard effective temperature indices) in relation to the building’s perceived control index score. The failure to reach statistical significance in both the operative temperature and standard effective temperature index graphs of Figure 3.30 lends no support to the adaptive hypothesis. Basic Results page 125 MRL Australia ASHRAE RP-884 Final Report Another extension of this perceived control hypothesis predicts that buildings with high degrees of occupant control would score higher ratings on thermal acceptability than those with low levels of control. Figure 3.31 fails to support this hypothesis since there is a complete absence of any relationship between buildings’ direct thermal acceptability ratings and their perceived control index score. All Buildings 100 90 80 70 60 50 40 30 TSA (% acceptable) 20 tsa = 82.16 - 0.90 * pcc_ag 10 R2 = 0.03, p = 0.1904 0 0 1 2 3 4 5 6 7 mean pcc_ag Figure 3.31: Regression analysis between direct thermal acceptability rating of buildings (f_tsa_2) and their mean level of perceived control (pcc_ag) Another corollary of the adaptive thermal control hypothesis is that occupants of buildings in which there is high thermal controllability should be less likely to request a change of temperature when presented with the thermal preference questionnaire item (MCI). Testing this prediction with the RP-884 database can be done by tallying the percentage of each building’s occupant sample who voted for either warmer or cooler temperatures (100 - F_MCI_2). The thermal control hypothesis predicts that this percentage should decrease in buildings where the degree of perceived control increases, but as seen in Figure 3.32, the RP-884 database offers no empirical support for this hypothesis. Basic Results page 126 MRL Australia ASHRAE RP-884 Final Report All Buildings 100 80 60 40 (100 - f_mci_2) % wanting change 20 (100 - f_mci_2) = 47.33 + 2.02 * pcc_ag R2 = 0.05, p = 0.0237 0 0 1 2 3 4 5 6 7 mean pcc_ag Figure 3.32: Regression analysis between the percentage of building occupants requesting a change in temperature (100- f_mci_2) and the mean level of perceived control (pcc_ag) for the building. 3.3.3. Building occupancy types - offices, residential and industrial Another corollary of the adaptive hypothesis is that the thermal perception of a particular set of thermal environmental factors is determined, in part, by the physics of the body’s heat balance, but also by the functional context of the building setting. That is, perception of a given state of body heat balance may differ, depending on the setting, because the occupants’ expectations are context specific and as such, not directly transferable from, say, the office setting to residential. In order to explore these issues in the RP-884 database, building function was classified within the database using the information supplied by the original researchers (Appendix C). A simple three-fold classification consisted of 1) residential, 2) office, and 3) industrial. Table 3.18 presents the summary statistics for each of the main thermal environmental parameters across all three functional classes of building in the RP-884 database. Obviously the overwhelming majority of buildings in the database were offices and so the analyses and conclusions developed in earlier sections of this chapter apply primarily to this Basic Results page 127 MRL Australia ASHRAE RP-884 Final Report class of building. However, the small sample of residential buildings summarized in Table 3.18 permit some comparisons to be drawn with office buildings. Firstly, the percentage of physical measurements of indoor climates actually meeting the ET* recommendations of ASHRAE Standard 55-1992 was remarkably low for the 16 residential buildings in the sample, ranging from an average of 6% in summer to 21% in winter. These low compliance levels mainly resulted from the high mean indoor summer temperature of 30°C and low indoor temperature means of 19°C in winter. Table 3.18 also indicates that mean indoor air speeds were generally higher in residential buildings compared with office and industrial settings, and they also showed a much larger seasonal variation in the residential cases. While mean metabolic rate estimates indoors remained relatively constant across residential and office settings at about 1.2 met units, they were noticeably higher in the small number of industrial buildings included in the RP-884 sample, with means ranging between 2 and 2.5 met units. The seasonal swing in mean building occupant thermal insulation levels was relatively small in the case of office and industrial buildings, amounting to less than 0.2 clo units. However, there was a much larger seasonal adjustment of insulation means across the residential buildings in the sample, suggesting that clothing adjustment represents a more powerful adaptive response in the home than in the workplace. Basic Results page 128 MRL Australia ASHRAE RP-884 Final Report Table 3.18: Summary of the thermal environmental conditions in three classes of building included in the RP- 884 database. residential offices industrial summer winter summer winter summer winter number of buildings in 12 4 98 38 4 4 sample$ mean (±stdev) * 30.2 18.8 24.3 22.6 22.6 20.4 operative temp (°C) ±0.932 ±4.86 ±2.07 ±0.74 ±1.40 ±1.64 mean (±stdev) 43.9 45.9 53.2 32.8 48.9 43.7 relative humidity (%) ±17.0 ±13.3 ±9.1 ±10.5 ±3.4 ±8.3 mean (±stdev) 6.4 20.9 55.8 86.5 0 0 % compliance with ±7.4 ±13.0 ±30.6 ±16.6 ±0 ±0 ASHRAE Standard 55@ mean (±stdev) 0.31 0.15 0.13 0.08 0.06 0.06 air speed (m s-1) ±0.10 ±0.04 ±0.07 ±0.03 ±0.00 ±0.002 mean (±stdev) insulation 0.58 1.34 0.70 0.89 0.66 0.82 (clothes+chair) (clo) ±0.16 ±0.16 ±0.075 ±0.16 ±0.06 ±0.08 mean (±stdev) 1.20 1.12 1.20 1.17 2.54 2.14 metabolic rate (met) ±0.08 ±0.04 ±0.10 ±0.05 ±0.08 ±0.573 * Mean and stdev figures quoted are averages across the n buildings in the table’s cell $ Note that in some studies large samples across several buildings have been treated as one building due to the way the data were originally supplied to the RP-884 database @ Percentage of physical observations within each building falling between ASHRAE Standard 55-92 ET* limits for the relevant season Having summarized the physical environmental and behavioral factors in three classes of building in Table 3.18, the main task of Table 3.19 is to summarize the subjective thermal responses to those indoor climatic conditions, again for residential, office and industrial settings. It appears that the samples of residential building occupants were, on average, less than half as sensitive to indoor temperature as the office building samples, since the gradient of their thermal sensation votes with respect to indoor operative temperature was about one vote per every 3~5 K change in temperature. In comparison the statistic from the sample of office buildings was closer to one sensation unit to every two degrees. Another noteworthy comparison between building function in Table 3.19 concerns acceptability ratings of buildings. Despite the very low level of ASHRAE Standard 55 compliance in the residential buildings in the database (Table 3.18), their acceptability ratings, at least in summer, were not appreciably lower than those registered in office buildings where the Standard 55 compliance levels were a good deal higher. Even in winter the acceptability ratings in residential sample buildings dropped only about 10% below the office buildings’ average rating, whereas the ASHRAE Standard compliance levels dropped by over 60% Basic Results page 129 MRL Australia ASHRAE RP-884 Final Report from office to residential settings. The implication of these comparisons is clearly that contextual factors have a strong bearing on how a given set of indoor thermal environmental parameters will be perceived by the occupants. Table 3.19: Summary of the subjective thermal responses across the three classes of building included in the RP-884 database. residential offices industrial summer winter summer winter summer winter number of buildings in 11 3 66 21 1 0 sample$ mean ±stdev thermal sensitivity 0.20 0.13 0.47 0.44 0.35 n.a. (i.e. regression gradient ±0.130 ±0.066 ±0.242 ±0.197 n.a. n.a. (sensation vote/deg K top) mean ±stdev thermal 25.66 24.41 24.11 22.03 19.23 n.a. neutrality (°C top) ±2.176 ±1.356 ±1.627 ±1.335 n.a. n.a. mean ±stdev % casting acceptable thermal sensation 81.21 66.70 79.17 78.00 28.78 55.20 votes ±8.756 ±38.012 ±11.239 ±16.046 ±17.494 ±11.827 (i.e. -1.5 < ASH < +1.5) n = 12 n = 4 n = 98 n = 38 n = 4 n = 4 * Mean and stdev figures quoted are averages across the n buildings in the table’s cell $ Note that in some studies large samples across several buildings have been treated as one building due to the way the data were originally supplied to the RP-884 database. Also, sample size is based only on statistically significant regression models, except where otherwise indicated (i.e. n=...) n.a. “not applicable” 3.4. Summary of basic results This chapter has presented a complex array of findings, exploring different thermal indices, different dimensions of subjective comfort, the effects of different seasons and climates, different modes of indoor climate control, and different patterns of building occupancy. This final section summarizes and interprets the key findings in relation to the adaptive hypothesis of thermal perception. This synthesis provides the starting point for developing more complex adaptive models in the next chapter. 3.4.1. Summary of thermal sensation, acceptability and preference Subjective thermal comfort research has been unfortunately complicated over the last thirty or forty years with the adoption of several different constructs of thermal perception. This Basic Results page 130 MRL Australia ASHRAE RP-884 Final Report chapter dealt with three of these -- thermal sensation, thermal acceptability and thermal preference. Sensation appears to be the most universally used version of questionnaire scale, and certainly the most ubiquitous within the RP-884 database. Consequently the more complex and abstract statistical analyses in this project were necessarily confined to this expression of thermal comfort. However, there was a useable quantity of data on thermal acceptability and preference within database as well, permitting several observations to be made about the semantic similarities and differences between all three constructs and the implications for practical applications. A very clear observation that emerges from the RP-884 analyses of direct assessments of thermal acceptability is that building occupants’ responses to direct questions such as this: “Is the thermal environment in this building at the moment acceptable to you or not?” bear virtually no relationship to the objective, physical conditions prevailing within the building at the time of the questionnaire. Evaluations of RP-884 database buildings’ indoor climatic quality in terms of its compliance with the relevant summer or winter temperature prescriptions of ASHRAE Standard 55 were completely dissociated from the direct acceptability ratings of those same buildings by their occupants. We therefore regard questionnaire items on direct thermal acceptability as being too ambiguous and vague to be of any practical value in thermal comfort research or practice. While direct ratings of thermal acceptability for indoor climates may not be particularly useful, there remains a practical need for information about the range of temperatures which can be regarded as acceptable for a given building in a specific climatic context. Accepting Fanger’s (1970) assumption that a mean sensation vote of ±0.85 corresponds with 80% thermal acceptability (or ±0.50 corresponds with 90%), it was possible in Section 3.1.3.4 to extract from the database ranges of acceptable temperatures within each of the sample buildings. ASHRAE Standard 55 suggests operative temperature ranges between 3K and 3.5 K. The RP-884 database, on the other hand, indicated that only a 2.5 K range was acceptable, on average, within HVAC buildings. In NV buildings, however, the 90% acceptable range extended significantly further, with an average of 5 K. This stretched to 7 K for the less stringent 80% acceptability criterion. Basic Results page 131 MRL Australia ASHRAE RP-884 Final Report The actual range of acceptable operative temperatures within any particular building was found to depend to a large extent on the degree of indoor climatic variability measured within that building (r=+0.66). This relationship suggests that if, through prior experience, occupants of a building come to expect considerable thermal variability, the range of temperatures regarded as acceptable will extend accordingly. Compared to direct thermal acceptability ratings, thermal sensation rating scales showed a much more consistent pattern of association with indoor thermal environmental indices. Correlations within each building in the database were statistically significant in the majority of cases in Appendix A, and this permitted the derivation of thermal neutralities wherever the sample of building occupants was large enough. Thermal neutrality is defined as that value of a thermal index (TOP, ET, SET or PMV) corresponding with a mean thermal sensation rating of “neutral” by the building’s sample of occupants. Assuming that neutrality is synonymous with the “optimum thermal condition” for a particular building, it should be more useful than direct acceptability ratings as a basis for application and practice. The temperatures which building occupants felt to be neutral were broadly similar in both HVAC and NV buildings, coming in at about 24°C in summer and 22.5°C in winter (TOP or ET). These figures approximate the centre of ASHRAE Standard 55’s summer and winter comfort zones. Neutrality defined in terms of the fully developed heat balance index such as SET also fell within the same range. Thermal neutrality depended on mean temperatures within both HVAC and NV buildings, but the rate of change of neutrality with respect to mean building operative temperature was twice as steep in NV buildings as it was in HVAC buildings. This finding suggests that occupants of NV buildings were twice as adaptable in terms of making themselves feel neutral than their counterparts in HVAC buildings. Fanger’s PMV model seemed to be reasonably accurate at predicting building neutralities across the whole sample of buildings, with an average prediction error less than half a degree (compared to observed neutralities). However, the standard deviation of the prediction error between buildings was quite high at 3.8 K. This suggests that, while the model worked well across a large sample of buildings, its predictions within any single building could be significantly wrong. Assuming that the quality of input data in the RP-884 database is of a uniformly high standard (Class 1 and II studies only), the explanations for the Basic Results page 132 MRL Australia ASHRAE RP-884 Final Report PMV model’s building-specific prediction errors must lie in non-thermal factors beyond the human heat balance. For example, the average prediction error in NV buildings was nearly a full degree, and its between-building standard deviation exceeded 5 K, suggesting that contextual factors degraded the model’s predictive powers. Preferred temperatures (as distinct from neutral or acceptable temperatures) could only be derived within only a subset of the RP-884 building sample. Preferences were found to occur within the 21~27°C range in most buildings. The average semantic discrepancy between neutral and preferred temperatures in buildings was generally within half a degree. While the sign and magnitude of the semantic discrepancy was unrelated to mean warmth within naturally ventilated buildings, Section 3.1.5 reported a significant tendency for neutrality to diverge away from preferred temperature within the sample of HVAC buildings (r=+0.50) as mean temperatures within buildings departed from 22.5°C. 3.4.2. Summary of thermal sensitivity and behavioural thermoregulation The linear dependence of thermal sensation votes and indoor climate showed a complex pattern of differences between HVAC and NV buildings, and also between the various indices of indoor climate (Section 3.1.1). Using the simpler indices such as TOP and ET, we found that persons in centrally controlled HVAC buildings were, on average, more than twice as sensitive to changes in temperature as their counterparts in naturally ventilated buildings. However, this heightened sensitivity diminished when the more complex heat- balance indices of warmth such as PMV and SET were used, with a sensation category having a fairly constant temperature width of about four degrees. One interpretation is that occupants of naturally ventilated buildings behaviorally regulate their heat balance with clothing and air speed adjustments such that they can accommodate wide variations in temperature indoors without adverse impacts on thermal sensation -- that is, they are actively thermoregulating their sensations. In contrast, occupants of centrally heated and air- conditioned buildings seem less adaptive behaviorally, and as a result their thermal sensations appear more sensitive to excursions of indoor temperature away from average, expected set-points. This interpretation is further supported by the clear relationships between behavioral factors (clothing and air speed) and indoor temperature (Section 3.1.6). While the seasonal mean Basic Results page 133 MRL Australia ASHRAE RP-884 Final Report insulation (clothes plus chair) levels were broadly similar in NV and HVAC buildings, there was considerably greater variability within and between the NV buildings. Also, there was a much higher correlation between insulation and indoor temperature in NV buildings (r= - 0.81) compared to HVAC buildings (r= -0.42). The data for indoor air speeds lends additional support to this interpretation, with the summer average being twice as high in NV as in HVAC buildings, and the variability within NV buildings also being greater. Furthermore, the closer correlation between indoor air speed and indoor temperature in NV buildings (r=+0.73) compared with HVAC buildings (r=+0.58) reinforces the conclusion that occupants of naturally ventilated buildings were behaviorally more active in thermoregulating their thermal sensations than were their counterparts in HVAC buildings. 3.4.3. Summary of the effects of outdoor climate on thermal perception indoors The temperatures found to be neutral within both HVAC and NV buildings varied, depending on season, with significantly warmer neutralities (defined in terms of operative temperature) occurring in summer compared to winter. These seasonal differences became less consistent as the thermal index used to define neutrality increased in complexity (PMV and SET), but this may simply result from the climatologically inaccurate definition of “summer” and “winter” applied throughout the database. Parameterizing outdoor climate simply as the mean of daily maximum and minimum effective temperatures (in shade) provided a more rational basis for exploring these effects in Section 3.2.1. Thermal neutrality within buildings was found to correlate positively (r=+0.65) with mean outdoor ET. While the strength of correlation was roughly comparable between HVAC and NV buildings, the slope of the linear relationship was not -- indoor neutrality was about twice as responsive to outdoor temperature in naturally ventilated buildings compared to air-conditioned. This difference suggests that much of the adaptability observed in free-running buildings, described earlier as being driven by expectations of warmth indoors, may in fact be driven by outdoor climate. Obviously indoor and outdoor temperatures are highly correlated in naturally ventilated buildings (r=+0.91, compared to r=+0.53 in HVAC buildings), so the temptation to include both in a multiple regression model of thermal neutrality must be resisted if the stability of regression coefficients is to be maintained. Basic Results page 134 MRL Australia ASHRAE RP-884 Final Report As for explaining why thermal comfort adaptability might be related to outdoor climate, the role of behavioral adjustments was the first place to look (Section 3.2.4). In particular, mean clothing insulation worn inside buildings, both HVAC and NV, was found to correlate negatively with mean warmth in the outdoor environment (r=-0.63). Mean air speeds inside buildings were also found to be, correlated with outdoor warmth, but much more so in the case of NV buildings (r=+0.80 compared to r=+0.44 in HVAC). It was also clear that the range of mean air speeds found within naturally ventilated buildings (often exceeding 0.4~0.5 m/s) was much wider than in HVAC buildings where it rarely exceeding the 0.2 m/s mandated in ASHRAE Standard 55 (1992). The combined effect of these behavioral thermoregulatory processes and their relationships with outdoor climate were examined in 3.2.1.4 where building neutralities predicted by the heat-balance index PMV were regressed on mean outdoor ET. The simplistic description of the PMV index as a “static” model throughout much of the adaptive comfort literature (reviewed in Chapter 1) was clearly not supported in this analysis, because observed regression equations were statistically significant and positive in both HVAC and NV samples. 3.4.4. Summary of the effects of contextual factors and perceived control The RP-884 index of perceived thermal control comprised a check-list of specific adaptive opportunities and their relative efficacy which we applied to each of the buildings in the database. The index clearly differentiated mixed-mode buildings from naturally ventilated buildings as affording their occupants the greatest degree of thermal control, largely due to their provision of both thermostats and operable windows. Naturally ventilated buildings came up second in average control index rankings, while the centrally-controlled HVAC buildings scored worst on the index. Despite the ability of the index to differentiate the three type of building in the RP-884 database, we found it had no correlation with thermal acceptability, sensitivity or preferences. Rather than interpreting this as a categorical negation of the role of perceived control in thermal perception, we think there are at least two alternative explanations: Basic Results page 135 MRL Australia ASHRAE RP-884 Final Report 1. The validity of the index itself is flawed. The perceived control scale was constructed very simplistically, mainly due to the nature of the raw data supplied to the RP-884 database. 2. Alternatively, the effects of perceived control may not have such a simple and direct relationship with thermal perception. The constructs of perceived control and adaptive opportunity within buildings may in fact exert more complex effects on thermal perception, and as a result, be statistically significant once other dimensions of indoor and outdoor climate have been taken into account. The possibility of complex, interactive effects of the pcc_ag index on thermal perception will be explored further in Chapter 4. The functional classification of RP-884 sample buildings into office, residential and industrial uncovered sharp differences in the basic indoor thermal environmental parameters such as air speed and temperature. The three classes of building were also clearly differentiated in terms of their compliance with effective temperature index limits within ASHRAE Standard 55. For example, a majority of the observations made inside office buildings, regardless of whether they were air-conditioned or not, complied with the ASHRAE Standard 55’s ET* limits, whereas the typical residential or industrial building scored less than 20% compliance with the standard. We also observed distinct differences in the degree of behavioral thermoregulatory adjustment made by residential building occupants compared to office workers. For example, seasonal clothing insulation contrasts were sharper in the residential as opposed to office setting. Despite these obvious differences in physical and behavioral features of indoor climate for office and residential buildings in the sample, we couldn’t discern sharp differences in occupant evaluations of the buildings’ indoor climatic quality. Despite their relatively poor performance on the objective physical indoor climatic criteria, occupants’ thermal acceptability ratings for residential buildings were comparable to those within office buildings. The strength of this contextual effect on subjective response is no doubt part of the explanation for the lack of any statistical correlation between thermal acceptability responses and indoor or outdoor climatic indices, as noted in Section 3.4.1 of this chapter. Basic Results page 136 MRL Australia ASHRAE RP-884 Final Report Basic Results page 137 MRL Australia ASHRAE RP-884 Final Report CHAPTER 4 -- TOWARDS ADAPTIVE MODELS The preceding chapter presented field evidence for the effects of indoor and outdoor climatic factors on the way building occupants perceive the environments provided by their buildings. Contextual factors such as whether the building is residential or work-place, and how much adaptive opportunity it affords were also investigated. The aim of this chapter is to develop these themes into adaptive models of thermal comfort, relating them back to previous research on the topic, as reviewed in Chapter 1. These adaptive models will form the basis of a variable temperature standard for indoor climate to be proposed in the next chapter. The reader should note that we have tried to make the terminology of equations in this and subsequent chapters more descriptive than the nomenclature of earlier chapters. 4.1. The semantics of thermal comfort Chapter 3 demonstrated that the indoor temperature regarded by building occupants as “neutral” did not always coincide with that which they rated as most “acceptable” or “preferable.” Evidence was presented for a “semantic artefact” which caused neutrality (derived from thermal sensation) to be displaced to the right of preference (warmer) in hot climates, and to the left of preference (cooler) in cold climates. In other words, in hot climates people preferred a thermal sensation slightly cooler than neutral, while in colder climates they preferred to feel slightly warmer than neutral. Earlier researchers have found similar semantic effects -- de Dear et al. (1991a) recorded a group-mean thermal sensation of -0.33 for their Singaporean climate chamber subjects while they were seated in their self-determined preferred temperature (i.e. they preferred to feel cooler than neutral). This is the equivalent of one whole degree (K) in semantic offset for the clothing, metabolic rate and air speed in question. Oseland (1994a,b) also reported semantic discrepancies between preference and thermal sensations, finding that they were stronger in their winter study where subjects decidedly preferred thermal sensations that were slightly warmer than neutral. The extent to which culture and climate affect people’s ______Towards Adaptive Models page 139 MRL Australia ASHRAE RP-884 Final Report thermal preferences, and the semantics they use to describe them, has also been discussed at length by McIntyre (1978a, 1978b, 1982). A test of this semantic artefact hypothesis within the RP-884 database appeared in Figure 3.24 as a set of graphs relating the discrepancy (discrep) between neutrality and preference within each building to the mean level of effective temperature in outdoor climate. There we found a significant linear correlation of r=+0.62 between discrep and mean outdoor effective temperature (dayavet) within the HVAC (and a few mixed-mode) buildings in the database. The linear equation indicated that neutrality and preference coincided only in those HVAC (and mixed-mode) buildings located in climates where the mean outdoor effective temperature was 13.6° C. semantic effect = -0.95 + 0.07 * mean outdoor ET* for HVAC buildings eq. 4.1 In climates warmer than this, indoor preference became progressively cooler than neutrality, while in regions where mean outdoor effective temperature fell below 13.6° C, preferred temperature was warmer than neutral temperature. Clearly this semantic effect needs to be accounted for when we develop variable temperature standards for HVAC buildings in the next chapter. In contrast to the situation just described for HVAC (and a few mixed mode) buildings, there was no empirical evidence in the RP-884 database for a semantic effect within naturally ventilated buildings (Figure 3.24). Exactly why people use words like “slightly warm” or “slightly cool” differently in different types of buildings remains unclear at this stage. Whatever the interpretation, it seems reasonable to develop a variable temperature standard for naturally ventilated buildings exclusively on the basis of thermal neutrality, as derived from rating scales such as the ASHRAE and Bedford 7-pt scales, ignoring the semantic offset altogether. The implications of the semantic effect on the HVAC building adaptive model can be depicted graphically in Figure 4.1. There the “adaptive model” represents the thermal neutrality function with respect to outdoor temperature from chapter three minus the semantic effect just discussed. ______Towards Adaptive Models page 140 MRL Australia ASHRAE RP-884 Final Report buildings with centralized HVAC 30 30 25 25 C) o 20 20 neutrality 15 15 semantic effect 10 adaptive model 10 including semantics 5 5 semantic effect (K) comfort temperature ( 0 0 -5 -5 -5 0 5 10 15 20 25 30 35 mean daily outdoor effective temperature (oC) Figure 4.1: An adaptive model for HVAC buildings that accounts for the semantic offset between neutrality and preference. 4.2. Comparison of RP-884 models with earlier adaptive model publications Indoor Climate: As noted in Chapter 1 (literature review), Humphreys (1975, 1978; 1981) published various statistical models of the adaptive dependence of indoor thermal neutrality on mean indoor and outdoor temperature. His statistical analysis of thirty six Class III studies from various countries around the world revealed a clear dependence of thermal neutralities (roughly equivalent to neut_top in RP-884 nomenclature) on the mean levels of air or globe temperature (roughly equivalent to top in RP-884) recorded within the buildings (Humphreys, 1975): neutrality = 2.56 + 0.83 * operative temperature (r=+0.96) eq 4.2 The RP-884 adaptive model that is most comparable to this equation of Humphreys’ can be found in Figure 3.1 of the preceding chapter, where thermal neutrality (defined in terms of operative temperature, neut_top) achieved weaker but still highly significant weighted regression and correlation with building mean indoor operative temperature (top): neutrality = 15.34 + 0.35 * operative temperature (r=+0.62) eq 4.3 ______Towards Adaptive Models page 141 MRL Australia ASHRAE RP-884 Final Report This RP-884 correlation coefficient can be improved slightly by deletion of all RP-884 industrial buildings and all Class III field studies from the analysis, since these were the clear outliers throughout the Chapter 3 analyses. With a total of 11,620 subjects inside 98 separate buildings (HVAC plus naturally ventilation) remaining in the analysis, the following statistically significant adaptive model was derived: neutrality = 12.93 + 0.44 * operative temperature (r=+0.68) eq 4.4 The RP-884 model in eq 4.4 indicates that thermal neutrality is barely half as sensitive to mean building temperature compared to Humphreys’ model in eq 4.2. The RP-884 version of the model indicates that neutral temperatures indoors increase by one degree for each two-and-a-third degrees increase in mean indoor operative temperature, whereas the comparable figure in Humphreys’ eq 4.2 approaches unity (one degree per 1.2 K of indoor). Possibly the difference can be accounted for by the varying compositions of the two building samples. Section 3.1.2.1 in the last chapter demonstrated a significant difference in the sensitivities for HVAC and naturally ventilated buildings, so a different balance in the composition of the all-building samples (HVAC plus natural ventilation) that were used to generate eqs. 4.2 (Humphreys) and 4.4 (RP-884) would logically affect the resulting models’ gradients. Outdoor Climate: Auliciems (1983) reanalysed Humphreys’ database of Class III field research after deleting some suspect data and including new Class III studies that had been published post Humphreys. The revised database included a total of 52 Class III field studies. Fortunately the Auliciems (1983) paper included a table of fundamental statistical data from each study in his database, including thermal neutrality (based on indoor air temperature), mean monthly outdoor temperature (based exclusively on air temperature), as well as an indication of whether the buildings in which the studies were conducted had central HVAC systems or were naturally ventilated (see Appendix G). We have reanalysed Auliciems’ database for the purpose of comparison with ASHRAE RP-884 and the resulting regression models are presented below in Figure 4.2. The models in Figure 4.2 indicate a disparity between the regression models for HVAC and NV buildings, with the HVAC model having the smaller gradient. The statistical significance ______Towards Adaptive Models page 142 MRL Australia ASHRAE RP-884 Final Report of this difference was confirmed using the technique described in Kleinbaum et al. (1988) (T=3.89, df=48, p<0.05). This begins to suggest that people in naturally ventilated buildings are more connected to the natural swings and cycles in outdoor climate, and their optimum thermal comfort conditions are more strongly influenced by these experiences. All Buildings - Auliciems' Data 32 30 C) o 28 26 24 22 20 neutral indoor air temperature ( neut_ta = 0.31 * ( mean month outdoor temp) + 17.6 18 R2 = 0.77 16 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 mean monthly outdoor air temperature (oC) Central Heated/HVAC Buildings- Naturally Ventilated Buildings- Auliciems' Data Auliciems' Data 32 32 neut_ta = 0.19 * (mean month outdoor temp) + 19.0 2 30 R = 0.48 C) 30 C) o o 28 28 26 26 24 24 22 22 20 20 neut_ta = 0.52 * (mean month outdoor temp) + 12.3 2 neutral indoor temp ( 18 R = 0.89 neutral indoor temp ( 18 16 16 0 4 8 12 16 20 24 28 32 0 4 8 12 16 20 24 28 32 o o mean outdoor air temperature ( C) mean outdoor air temperature ( C) Figure 4.2: Reanalysis of Auliciems’ (1983) Class 3 field study database (Appendix G) for the effects of outdoor climate on thermal neutrality The RP-884 adaptive models that are most comparable to Auliciems’ in Figure 4.2 are those based on the linear relationship between building neutralities and mean outdoor daily effective temperature (Figure 3.18). Clearly both the RP-884 and Auliciems results support the adaptive hypothesis inasmuch as both models depict a positive dependence of indoor ______Towards Adaptive Models page 143 MRL Australia ASHRAE RP-884 Final Report thermal neutrality on temperature outside the buildings surveyed, although the “all buildings” RP-884 model achieved a lower overall correlation coefficient (r=+0.64 compared to r=+0.87 in Auliciems). Another difference is that the gradient for the “all buildings” model in RP-884 (Figure 3.18) was only half that found using the Auliciems database (b=0.16 as opposed to b=0.31). It should be noted, however, that this may not necessarily mean that the RP-884 building occupants were that much less climatically adapted than their counterparts in the Auliciems database -- the divergent gradients could simply be an artefact of the different indices used to represent outdoor climate in the two databases. Auliciems used outdoor data based on climatological (30 year mean) monthly outdoor air temperatures, whereas the RP-884 index was based on observed 2-node effective temperature (ET*) outdoors. The reason this might have an effect is because the 2-node model’s ET* index quantifies the incremental thermal impacts of elevated humidity. If the observations from warm and humid climates in Auliciems’ database were transformed from simple air temperatures into ET*, they would be non-uniformly displaced to the right along the abscissa in Figure 4.2, with the effect being most pronounced for the warmer temperatures. These effects, if incorporated into Auliciems’ database, could be expected to depress his regression model’s gradient towards the slope found in the RP-884 analysis. Despite being conceptually comparable, the Auliciems and RP-884 approaches to outdoor climatic adaptive models have fundamental differences which dissuade us from simply pooling the two databases together. These include: · Internal consistency for data going into the RP-884 database was more rigorously controlled. For example, the dependent variable, thermal neutrality, was recalculated from raw data by us rather than relying on those published by the original researchers. · The RP-884 outdoor climatic index (ET*) included humidity effects which, as noted earlier, were ignored in the Auliciems database. · The RP-884 indoor climatic index, operative temperature, included mean radiant temperature effects which would be overlooked by simple air temperature measurements, as used in the Auliciems database. ______Towards Adaptive Models page 144 MRL Australia ASHRAE RP-884 Final Report · The unit of analysis in Auliciems’ database was the field study, whereas we analysed data at the level of individual buildings. It is felt that potentially significant contextual effects may have been glossed over at the former level of data aggregation. · The ranges of predictor variable, outdoor climate, covered by the two databases were significantly different. Centrally heated, ventilated and air-conditioned buildings were observed in climate zones ranging from mean outdoor temperature -5° CÛ +33° C in the RP-884 database, whereas Auliciems’ database only covered the range 0° CÛ +23° C. Naturally ventilated buildings were observed in outdoor climates ranging from mean temperature +5° CÛ +33° C in the RP-884 database, whereas Auliciems’ database only covered the range +14° CÛ +33° C. Therefore, the wider range of climates within the RP- 884 database encourages their extensive application across diverse climate zones around the world. Apart from these methodological differences between databases, another factor dissuades us from calculating an RP-884 multiple regression model for comparison with the Auliceims’ combined indoor-outdoor adaptive model (1983): neutrality = 9.22 + 0.48 * mean indoor temp + 0.14 * mean outdoor temp eq 4.5 High correlations between the “independent” indoor and outdoor temperature variables throughout the RP-884 database (weighted Pearson’s r=+0.66, p=0.0001) would render any regression coefficients within multiple regression models unstable. This would have the effect of making comparisons with Auliciems’ model in eq. 4.5 unreliable (Michael Humphreys, pers. com. BRE meeting UK, 1993). 4.3. Comparison of RP-884 models with the PMV “static model” Section 3.2.1.3 indicated that the so-called “static model” of thermal comfort, PMV, predicted not-so-static comfort temperatures for the buildings in RP-884’s database. In particular, building occupants’ behavioral manipulations of heat-balance factors such as clothing and air speeds showed a systematic dependence on outdoor climate. That is, clothing insulation decreases in warm climates while air speeds increase. Obviously the adaptive opportunity for manipulating these parameters is context specific, so comparisons ______Towards Adaptive Models page 145 MRL Australia ASHRAE RP-884 Final Report between the PMV predictions and RP-884 database observations need to be disaggregated to HVAC and NV sub-samples. 4.3.1. Comparisons within the centrally conditioned building sample The “static” versus adaptive comparison for RP-884’s HVAC buildings in Figure 4.3 shows that comfort temperatures, after correction for semantic effects, have only a moderate variation (less than 2 K) across a very wide range of outdoor climates (spanning about 40 K). An interpretation of this finding could be that occupants of such buildings have become finely adapted to the mechanically conditioned and static indoor climates being provided by centralized HVAC services. The question of “what type is this adaptation?” can be answered by comparison with the comfort temperatures predicted by the so-called “static” model (PMV). The two models appear very close together in Figure 4.3, with the discrepancy being a 0.1 K offset in their Y-intercepts. This discrepancy is neither statistically nor practically significant. PMV, therefore, appears to have been remarkably successful at predicting comfort temperatures in the HVAC buildings of RP-884’s database. A corollary of this finding is that the relatively minor behavioral adjustments to clothing and room air speeds observed for the occupants of HVAC buildings explain the systematic response in comfort temperature to outdoor climatic variation, and that these adaptive behaviors are, in fact, being accounted for by the PMV model. Against this picture of general agreement between models, a subtle but nonetheless important distinction between the PMV and RP-884 adaptive models deserves a mention. The latter were based on thermal sensation data, after being corrected for semantic artefacts, whereas the PMV model was based exclusively on thermal sensation data without semantic considerations taken into account. While in practical terms the distinction may seem trivial, what this means is that PMV successfully predicts optimum comfort temperatures in field settings despite being intended to predict neutral temperatures. For these reasons we have labelled the Y-axis in Figure 4.3. as “comfort temperature” instead of “thermal neutrality.” ______Towards Adaptive Models page 146 MRL Australia ASHRAE RP-884 Final Report buildings with centralized HVAC 25 C) 24 o 23 22 comfort temperature ( RP-884 adaptive model with semantics 21 "static" model (PMV) 20 -5 0 5 10 15 20 25 30 35 o mean daily outdoor effective temperature ( C) Figure 4.3: Comparison of the RP-884 adaptive model (based on observed neutralities corrected for semantic effects) and the “static” model (based on PMV predictions) for HVAC buildings. It is interesting to note that this graph so closely matches predictions of PMV with observations in real HVAC buildings, whereas so many of the earlier thermal comfort field research papers which we discussed in Chapter 1’s literature review indicated quite the opposite. Indeed, some of those anomalous papers were from authors who contributed their raw data to this project’s database. Therefore our success at bringing PMV predictions into line with observations in HVAC buildings most probably can be attributed to the quality controls and precautions we took when assembling the RP-884 database, which transformed, to some extent, the raw data used in the authors’ original analyses. Among the more important of these were probably: · setting minimum standards on instrumentation and protocols for data going into the RP- 884 database, · conversion of all clo estimates throughout the entire database to a single standard (ASHRAE 55-92), · inclusion of the thermal insulation effects of the chairs used by subjects (McCullough and Olesen, 1994), · recalculation of thermal indices from raw data throughout the entire database with a consistent software tool (Fountain and Huizenga, 1995), ______Towards Adaptive Models page 147 MRL Australia ASHRAE RP-884 Final Report · application of a consistent set of statistical techniques to all raw data instead of relying on different author’s approaches to thermal neutrality, preference and other statistically derived parameters, · conducting the meta-analysis at the appropriate scale of statistical aggregation, namely the individual building. We are therefore led to the conclusion that Fanger’s PMV model is, in reality, an “adaptive” model which is suitable for application as it was initially proposed back in 1970 by Fanger himself; as an engineering guide in HVAC buildings the world over1. The main sticking point with its application in predictive mode before a building is constructed, or occupied, is that it is unusual to have detailed observations on mean clo values or air speeds within a building at the design stage. The practical solution here is to seek further guidance from the RP-884 database. We know from Figure 3.25 that the thermal insulation (in clo units, clothes plus chair) applicable to PMV calculations is highly correlated with mean outdoor effective temperature (dayavet): thermal insulation = 0.93 * e-0.013*(mean outdoor ET*) (r= +0.80) eq 4.6 Figure 3.27 indicates that mean room air speeds (m s-1) within HVAC buildings are also correlated with mean outdoor effective temperature: mean room air speed = 0.08 * e+0.014*(mean outdoor ET*) (r= +0.44) eq 4.7 so it seems not unreasonable to anticipate the unknown inputs to PMV simply from a knowledge of the outdoor weather/climate conditions for the site in question. An even simpler approach is to directly predict PMV-based neutrality using the linear regression model depicted in Figure 3.20. In effect this amounts to predicting the aggregate effects of climate on clothing insulation and room air speeds within HVAC buildings. 1 In the introductory chapter to his book entitled “Thermal Comfort - Analysis and Applications in Environmental Engineering” which introduced the PMV model, Fanger was quite clear that the book, and by implication, the PMV model at its core, were intended for application by the HVAC industry in the creation of “artificial climates” in “controlled spaces.” The generalisation of the PMV model to all spaces intended for human occupancy, HVAC or NV, was a much later development that we disagree with. ______Towards Adaptive Models page 148 MRL Australia ASHRAE RP-884 Final Report Therefore, the adaptive model for HVAC buildings (noted here as “PMV, adaptive”) reduces to this simple linear equation (from Figure 3.20): comfort temperature in HVAC = 22.6 + 0.04 * mean outdoor ET* (r= +0.50) eq 4.8 So, it would appear that the occupants of buildings with centralized HVAC systems have become adapted to the temperatures that they encounter within their buildings -- generally within the narrow 22~24° C range. Of course this begs the question of whether or not it is possible to extend the range of comfort adaptation by deliberately letting indoor HVAC setpoints more closely track outdoor weather and climatic conditions? We concede that a purposely designed intervention field experiment on a “real” building, would be the most appropriate way to test this hypothesis. However, we can draw comparisons with the naturally ventilated RP-884 sample where the rate of change in thermal insulation with respect to variations in outdoor climate (Figure 3.25) was significantly greater than in centrally conditioned buildings. Across the 5Þ 30° C range of mean outdoor effective temperatures in Figure 3.25, building occupants’ mean insulation (including chair effects) varied by about 0.3 clo units in the HVAC sample, whereas the clothing response was more than double this in naturally ventilated buildings across the same outdoor temperatures. In short, naturally ventilated building occupants appear to be prepared to take on greater personal responsibility for maintaining their thermal comfort, when required to. Whether they would be prepared to do likewise if required in HVAC buildings remains a moot point deserving further research. The same line of reasoning can be applied to indoor air velocities in HVAC buildings. We noted they were confined to very low levels (virtually still air, at <0.2 m s-1) within the RP-884 sample of HVAC buildings, almost regardless of outdoor climate (see Figure 3.27). This stood in marked contrast to the naturally ventilated sample where within-building mean velocities went up to 0.4 m s-1 for outdoor mean effective temperatures of about 30° C. These velocities could possibly be feasible inside centrally conditioned buildings, perhaps with supplementary air movement in the occupied zone provided by local fans or other means for individual thermal control. Present-day HVAC building occupants appear adapted to conditioned, still-air conditions, but they may be willing to more actively regulate ______Towards Adaptive Models page 149 MRL Australia ASHRAE RP-884 Final Report convective and latent heat losses if the behavioral opportunities to do so were made available to them and HVAC set-points provided the stimulus (eg allowing for warmer indoor temperatures during summer). As noted above, new research is required to establish just how much thermoregulatory responsibility occupants of HVAC buildings may be prepared to accept. Future field experimentation may suggest that simply predicting PMV-based neutralities from a knowledge of mean outdoor temperature, as in eq. 4.8 above, is inappropriate for the purpose of establishing HVAC set-points. Instead, it may well be more appropriate to first estimate likely indoor clothing insulation levels and air velocities from equations resembling those established for the naturally ventilated sample in Figures 3.25 and 3.27 (instead of the HVAC models presented in eqs. 4.6 and 4.7 above), and then iteratively solving the PMV model for “neutral” operative temperature. Clearly further field experimentation on these questions of thermal adaptation in HVAC buildings is required. 4.3.2. Comparisons within the naturally ventilated building sample Figure 4.4 repeats the “adaptive” versus “static” comparisons for the naturally ventilated buildings within the RP-884 database. One important departure from the method just applied to HVAC buildings, however, is the omission of the semantic effect, as discussed in Section 4.1. This is because we were unable to discern any systematic relationship between the preferred and neutral temperatures for the naturally ventilated buildings analysed in Figure 3.24. The remarkable agreement found between PMV and adaptive models in the HVAC building sample clearly breaks down in the context of naturally ventilated buildings where the adaptive model shows a gradient almost twice as steep as the heat-balance PMV model’s. This divergence tested positive using the Kleinbaum et al. technique (1988) (T=2.43, df=80, p<0.05). It therefore appears as if behavioral adjustments to body heat balance (i.e. biophysical effects) account for only about half of the climatic dependence of comfort temperatures within naturally ventilated buildings. In effect, the PMV model has been demonstrated to function as a partially adaptive model of thermal comfort in naturally ventilated buildings. ______Towards Adaptive Models page 150 MRL Australia ASHRAE RP-884 Final Report However, there still remains the other half of the adaptive effect to be explained. Having partialled out the effects of behavioral adaptations, we’re left with the physiological (acclimatization) and psychological (habituation) hypotheses discussed in Chapter 1. There we noted that effects of acclimatization were not in evidence during climate chamber experiments on moderate heat/cold stress exposures, so it is not surprising that they failed to reappear in the field settings analyzed in RP-884. Therefore, by a process of elimination, we are left with psychological adaptation (i.e. expectation and habituation) as the most likely explanation for the divergence between field observations and heat-balance (PMV) predictions. buildings with natural ventilation (no HVAC) 30 29 C) 28 o 27 26 25 24 comfort temperature ( 23 RP-884 adaptive model 22 "static" model (PMV) 21 20 5 10 15 20 25 30 35 o mean daily outdoor effective temperature ( C) Figure 4.4: Comparison of the RP-884 adaptive model (based on observed neutralities in Figure 3.18) and the “static” model (based on PMV predictions) applied to naturally ventilated buildings. One might wonder why the laboratory-based PMV heat balance model works so well in RP- 884’s HVAC buildings but not so for the NV buildings? Perhaps we can regard the former as being quite comparable to the climate chamber setting? In both climate chambers and HVAC buildings the thermal environment is entirely regulated by processes outside the person-environment feedback loop discussed in Chapter 1. Naturally ventilated buildings, on the other hand, are much more “interactive,” with adaptive feedback loops being closed at both behavioral and psychological levels. ______Towards Adaptive Models page 151 MRL Australia ASHRAE RP-884 Final Report 4.4. Adaptive models for acceptable ranges of indoor temperatures Preceding sections defined some simple adaptive models for predicting optimal comfort temperatures indoors, but overlooked the question of what sort of temperature inhomogeneity might be acceptable. We saw in Section 3.1.3.4, particularly in Figure 3.9, a direct correlation between the range of acceptable operative temperatures within each building and its internal temperature variability (standard deviation). The relationship reached statistical significance only for the naturally ventilated buildings in the RP-884 sample where the following linear regression model achieved a correlation coefficient of r= +0.51: range of acceptable temperatures = 4.2 + 1.65 * (stdev of indoor temperature) eq 4.9 The failure of a similar model to reach significance within centrally conditioned buildings reinforces the fundamental difference between the HVAC and natural ventilation contexts discussed at length throughout in this report. In the naturally ventilated setting, it appears as if building occupants extend their range of thermal acceptability to accommodate the range of thermal variation expected within their buildings. We propose the simple model in eq 4.9 as the adaptive approach to prediction of 80% acceptable ranges within naturally ventilated buildings. But for many applications it simply will not be feasible to anticipate the standard deviation of indoor operative temperatures for a building that is either yet to be built or not fully monitored for any significant length of time. Therefore a more practical alternative for prescribing acceptable indoor temperature ranges may be to rely on the RP-884 observations, as described in Table 3.9. In HVAC buildings the general comfort 80% acceptability criterion corresponded, on average, to a range of two degrees (K) either side of the optimal comfort temperature. Tightening the acceptability criterion from 80% to just 90% in RP-884’s HVAC building sample meant a narrowing of the acceptable range to ± 1.2 K. In either case, the corresponding 80% and 90% ranges observed in the naturally ventilated RP-884 sample were significantly wider, at ± 3.5 K and ± 2.5 K respectively. ______Towards Adaptive Models page 152 MRL Australia ASHRAE RP-884 Final Report If these acceptable ranges are going to be applied to adaptive models which predict optimum indoor temperatures on the basis of outdoor climatic conditions, we need to address the possibility that the acceptable ranges themselves are also dependent on outdoor climate. For example, one might speculate that the acceptable range diminishes as outdoor climate becomes hotter for the simple reason that indoor clothing insulation levels also decrease with increasing outdoor temperature (see Section 3.2.4.1). A statistical regression test of this possibility was performed by fitting a regression model to the dependence of acceptable indoor temperature ranges on outdoor effective temperature, and the results are reported in Table 4.1. It can be assumed that, if the regression model turns out to have a statistically insignificant gradient term, the subsample’s mean acceptable range (as described in Table 3.9) can legitimately be applied across all climate zones. As seen below in Table 4.1, none of the acceptable range models achieved statistical significance at the 95% confidence level, regardless of building type nor acceptability level. Therefore, the variable temperature standards to be proposed in the next chapter can be based on an optimal temperature predicted from outdoor climate, plus or minus a constant acceptable temperature range for the building type in question, which does not vary with climate. ______Towards Adaptive Models page 153 MRL Australia ASHRAE RP-884 Final Report Table 4.1: Assessment of the dependence of acceptable indoor temperature ranges on outdoor effective temperature. centrally naturally heated/air- ventilated conditioned buildings buildings number of buildings 108 41 (3 missing values) (4 missing values) number of buildings with thermal sensation regression models achieving 95% significance* 63 33 (58% of total) (75% of total) Mean range of indoor temperatures based on the 80% acceptability criterion (K) 4.1 6.9 Regression model for the dependence of the 80% acceptable temperature ranges on outdoor effective y=3.08 + 0.05*x y=6.28 + 0.03*x temperature Statistical T-test for the regression gradient 1.81 0.36 Statistical significance of T-test p>0.05 p>0.10 Mean range of indoor temperatures based on the 90% acceptability criterion (K) 2.4 4.9 Regression Model for the dependence of the 90% acceptable temperature ranges on outdoor effective y=1.81 + 0.03*x y=3.70 + 0.02*x temperature Statistical T-test for the regression gradient 1.81 0.36 Statistical significance of T-test p>0.05 p>0.10 * Based on those thermal sensation (ASH) models in Appendix A (y=a + b*TOP) achieving 95% statistical significance or better The next chapter in this report will summarize this chapter’s adaptive models into a pair of variable temperature standards - one for application in HVAC buildings and another for application in the naturally ventilated context. ______Towards Adaptive Models page 154 MRL Australia ASHRAE RP-884 Final Report CHAPTER 5 - VARIABLE TEMPERATURE STANDARDS The last remaining task for ASHRAE RP-884 is to propose variable temperature thermal comfort standards. The statistical analyses and adaptive models in Chapters 3 and 4 were presented separately for buildings with and without centrally controlled HVAC systems. It seems logical, therefore, to partition this chapter’s variable temperature standards along the same lines. This distinction between centrally- controlled HVAC buildings in which individual occupants have little or no control over their immediate thermal environment, and naturally ventilated buildings in which occupants at least have control over windows, is a unique feature of the ASHRAE RP-884 project. All thermal comfort standards to date (see Chapter 1), both extant and proposed, regardless of whether they were based on so-called “static” or “adaptive” models, have been promulgated as universally applicable across all types of building. By not differentiating their contexts for application, earlier comfort standards are, in effect, extrapolating from relationships established in centrally controlled HVAC settings to naturally ventilated contexts, and vice versa. In contrast, a fundamental tenet of RP-884 has been that the indoor climates found in HVAC and naturally ventilated buildings are not only quantitatively different, but also qualitatively different, and as such, they require separate comfort standards. The reader is requested to regard the two standards in this Chapter as self-contained documents. There is, therefore, some duplication of definitions and related material across the two standards. 5.1. A variable temperature standard for application in buildings with centrally controlled HVAC 5.1.1. Purpose To specify the combinations of indoor space environment and personal factors that will produce thermal environmental conditions acceptable to a majority of the occupants within centrally heated and air- conditioned spaces. Variable Temperature Standard page 155 MRL Australia ASHRAE RP-884 Final Report 5.1.2. Scope · The environmental factors addressed are temperature, thermal radiation, humidity, and air speed; the personal factors are those of activity and clothing. · It is intended that all of the criteria in this standard be applied together, since comfort in the space environment is complex and responds to the interaction of all of the factors that are addressed. · This standard applies to general thermal comfort conditions and excludes local discomforts such as draft, vertical thermal stratification, and radiant asymmetry. · This standard specifies thermal environmental conditions acceptable for healthy people at atmospheric pressure equivalent to altitudes up to 3000 m in indoor spaces designed for human occupancy for periods not less than 15 minutes. · This standard does not address such non-thermal environmental factors as air quality, acoustics, and illumination; nor other physical, chemical or biological space contaminants which may affect comfort or health. · This standard is intended for use in design of HVAC-systems, design of buildings, evaluation of existing thermal environments, building ratings or labelling, and testing of HVAC system performance. · The standard applies exclusively to indoor environments with HVAC systems over which the occupants have no control. The occupants of such buildings are presumed to have no option to open/close windows. 5.1.3. Definitions adaptive model: A linear regression model that relates indoor design temperatures or acceptable temperature ranges to outdoor meteorological or climatological parameters. Note that the range of applicable outdoor climates should be restricted to that appearing on the X-axis of the adaptive model’s graph (i.e. they should not be extrapolated beyond the range of the regression models’ X-variable). adaptive opportunity: Buildings provide their occupants with varying degrees of adaptive opportunity or scope to adjust the internal environment (and themselves) to achieve thermal comfort. Sealed, centrally air-conditioned office buildings with open-plan floor layouts provide minimal adaptive opportunity, while Variable Temperature Standard page 156 MRL Australia ASHRAE RP-884 Final Report naturally ventilated buildings with operable windows and ceiling fans within small single- or dual-occupant offices typically afford high degrees of adaptive opportunity. clo: a unit used to express the thermal insulation provided by garments and clothing ensembles, where 1 clo = 0.155 m2 K/W. comfort, thermal: that condition of mind which expresses satisfaction with the thermal environment; it requires subjective evaluation. Optimum thermal comfort is assumed to correspond with a thermal preference vote of “want no change” environment, thermal: the characteristics of the environment which affect a person’s heat loss. environment, acceptable thermal: an environment which at least 80% of the occupants would find thermally acceptable. humidity, relative (rh): the ratio of the mole fraction of water vapor present in the air to the mole fraction of water vapor present in saturated air at the same temperature and barometric pressure; alternatively, it equals the ratio of the partial pressure (or density) of the water vapor in the air to the saturation pressure (or density) of water vapor at the same temperature. insulation, chair: incremental thermal insulation of chairs used by building occupants. The typical office chair’s clo value is ~0.15 clo units. This effect needs to be included in overall thermal insulation estimates for the PMV model to yield accurate results. insulation, clothing (Icl): the resistance to sensible heat transfer provided by a clothing ensemble (i.e., more than one garment). It is described as the intrinsic insulation from the skin to the clothing surface, not including the resistance provided by the air layer around the clothed body; it is usually expressed in clo units. Clothing worn by people indoors is modified to a great extent by the season and outside weather conditions. During the summer months, typical clothing in commercial establishments consists of lightweight dresses, lightweight trousers, short or long sleeved shirts and blouses and occasionally a suit jacket or sweater. These ensembles have clothing insulation values (Icl) ranging from 0.35 to 0.6 clo. During the winter season, people wear garments constructed of thicker, heavier (ie. warmer) fabrics and often add more garment layers to an ensemble. A typical indoor winter ensemble would have an Icl value ranging from 0.8 to 1.2 clo. Where the outside temperature range does not vary a great deal from season Variable Temperature Standard page 157 MRL Australia ASHRAE RP-884 Final Report to season, people do not change the types of garments they wear year round as much as people who experience extreme hot and cold climates. The (Icl) provided by clothing ensembles can be estimated by summing the garment Iclu values as described in ASHRAE Standard 55-92 (1992). insulation, garment (Iclu): the increased resistance to sensible heat transfer obtained from adding an individual garment over the nude body. It is the effective increase in overall insulation attributable to the garment and is usually expressed in clo units. mean air speed (velocity): arithmetic mean of instantaneous air speed measurements within the occupied zone, integrated over a period of not less than three minutes (m s-1). mean monthly (or daily) outdoor effective temperature: Arithmetic average of 6am outdoor ET* (assumed minimum), and 3pm outdoor ET* (assumed maximum) for a calendar month or particular day. metabolic rate (met): rate of energy production of the body. Metabolism, which varies with activity, is expressed in met units in this standard. One met is defined as 58.2 Wm-2 which is equal to the energy produced per unit surface area of a seated person at rest. The surface area of an average person is about 1.8 m2. In today’s society, most people are occupied with light, primarily a sedentary activity level corresponding to 1 to 1.6 met. Metabolic activity should be assessed for a period between 30 and 60 minutes before any thermal assessment is made. For more detailed values see ASHRAE Standard 55- 1992, ISO 7730, ISO 8996 or the ASHRAE Handbook of Fundamentals (1993). neutrality, thermal: the indoor thermal index value (usually operative temperature) corresponding with a maximum number of building occupants voting “neutral” on the thermal sensation scale. preference, thermal: a conscious desire for change in one’s thermal state, commonly graded into the categories, 1“want cooler,” 2 “want no change,” 3“want warmer”; it requires subjective evaluation. Preferred temperature, i.e. that corresponding with a maximum number of “2” votes, does not necessarily correspond with thermal neutrality. PMV: Predicted Mean Vote is a thermal index derived from the heat-balance model of thermal comfort developed by Fanger (1970). PMV predicts the mean thermal sensation of a large group of subjects experiencing a thermal environment specified in terms of mean air and radiant temperatures, air speed, humidity, thermal insulation and metabolic rate. Variable Temperature Standard page 158 MRL Australia ASHRAE RP-884 Final Report PMV, analytic: Predicted Mean Vote index calculated analytically from mean measurements or estimates of the six primary comfort parameters: mean air and radiant temperatures, mean air speed, humidity, clothing (+ chair) thermal insulation and metabolic rate. PMV, adaptive: the RP-884 adaptive regression model that predicts optimum thermal comfort temperature (thermal sensation corrected for semantics). The name “adaptive PMV” is used for the model because it predicts essentially the same optimum operative temperature answer as the analytic PMV approach, but uses mean outdoor effective temperature as the only input instead of the usual four inputs (clo, met, rh and v) required by the analytic PMV method. sensation, thermal: a conscious feeling commonly graded into the categories, -3 cold, -2 cool, -1 slightly cool, 0 neutral, +1 slightly warm, +2 warm, and +3 hot; it requires subjective evaluation. An individual’s ideal thermal comfort does not necessarily correspond with a thermal sensation vote of “neutral” (zero). summer: operationally defined as the cooling season; climatologically defined for the purposes of this standard as having a mean daily outdoor effective temperature of 25oC. temperature, air (ta): the dry-bulb temperature of the air surrounding the occupant. temperature, dew point (tdp): [or ambient water vapor pressure (Pa)], the temperature at which moist air becomes saturated (100% relative humidity) with water vapor (Psdp = Pa) when cooled at constant pressure. temperature, mean radiant (tr): the uniform surface temperature of an imaginary black enclosure in which an occupant would exchange the same amount of radiant heat as in the actual nonuniform space. temperature, operative (to): the uniform temperature of an imaginary black enclosure in which an occupant would exchange the same amount of heat by radiation plus convection as in the actual non- uniform environment. Operative temperature is numerically the average of the air temperature (ta) and mean radiant temperature (tr), weighted by their respective heat transfer coefficients (hc and hr): (hc ta + hr tr ) to = (hc + hr ) which typically equates to the arithmetic average of mean air and radiant temperatures. Variable Temperature Standard page 159 MRL Australia ASHRAE RP-884 Final Report temperature, effective (ET*): the operative temperature (to) of an enclosure at 50% relative humidity which would cause the same sensible plus latent heat exchange from a person as would the actual environment. temperature, optimum operative: the operative temperature that satisfies the greatest possible number of people at a given clothing and activity level. Due to the semantic offset between preferred and neutral temperatures, optimum operative temperature in this standard does not necessarily correspond exactly with thermal neutrality (i.e. optimum temperature is neutrality after correction for semantic offset). temperature, thermodynamic wet bulb: (also called the Adiabatic Saturation Temperature), that temperature at which water, by evaporating into air, can bring the air to saturation adiabatically at the same temperature. The wet bulb temperature measured with an appropriate psychrometer can approach the thermodynamic wet bulb temperature. winter: operationally defined as the heating season; climatologically, for the purposes of this standard a typical winter condition is assumed to have a mean daily outdoor effective temperature of 0oC. zone, occupied: the region normally occupied by people within a space, generally considered to be between the floor and 1.8 m above the floor and more than 0.6 m from walls or fixed air conditioning equipment. 5.1.4. Conditions for an acceptable thermal environment. The conditions for an acceptable thermal environment shall be based on one of the following three techniques, listed in descending order of preference: · the analytic PMV method, as described in ISO 7730 (1994) , if mean clothing and metabolic rates are known in advance, or · the adaptive PMV method in which indoor optimum operative temperature is predicted from a knowledge of outdoor effective temperature using RP-884 regression models, or · the prescriptive method in which summer and/or winter comfort zones for either 90% or 80% thermal acceptability levels are selected from the RP-884 psychrometric charts. Variable Temperature Standard page 160 MRL Australia ASHRAE RP-884 Final Report 5.1.4.1 Analytic PMV Method See the detailed procedures for estimation of the optimum temperature for a group of building occupants described in ISO 7730 (1994). The only departure from the methods described there is the inclusion of the incremental thermal insulation of the chair into the seated occupants’ overall thermal insulation. Optimum operative temperature may be predicted by inputting measured or estimated values of insulation (clothing + chair), metabolic rate, relative humidity, air speed and solving for the unknown operative temperature by setting PMV = zero. Note that the actual group mean thermal sensation expressed by building occupants under the optimum operative temperature predicted by this method may not necessarily equal zero (“neutral”). This is due to the semantic offset between group thermal neutrality and preference. Therefore PMV equal to zero may correspond with a non-zero mean thermal sensation for the group of building occupants in question, but they will still be in their optimum operative temperature. 5.1.4.2. Adaptive PMV method In HVAC situations where the mean thermal insulation (clothing and chairs) and mean air speed cannot be observed or accurately anticipated, the adaptive PMV method may be applied. Weather data in the form of mean outdoor effective temperature for the relevant time of year is required. In the absence of current meteorological observation, published mean climatological data for the relevant month from the nearest or most relevant weather station may suffice. Variable Temperature Standard page 161 MRL Australia ASHRAE RP-884 Final Report 28.0 C) 80% acceptability upper limit o 26.0 24.0 comfort temp. in HVAC = 22.6 + 0.04 * outdoor ET* 22.0 80% acceptability lower limit 20.0 comfort temperature ( 18.0 -5 0 5 10 15 20 25 30 35 mean daily outdoor effective temperature (oC) Figure 5.1: The adaptive PMV comfort zone’s optimum and limits for an 80% acceptability level in HVAC premises. 28 C) o 26 90% acceptability upper limit 24 22 90% acceptability lower limit 20 comfort temp. in HVAC = 22.6 + 0.04 * outdoor ET* comfort temperature ( 18 -5 0 5 10 15 20 25 30 35 mean daily outdoor effective temperature (oC) Figure 5.2: The adaptive PMV comfort zone’s optimum and limits for an 90% acceptability level in HVAC premises. Variable Temperature Standard page 162 MRL Australia ASHRAE RP-884 Final Report 5.1.4.3. Prescriptive method Where outdoor meteorological or climatological data are unavailable, the RP-884 prescriptive method may be used to define acceptable ranges of temperatures. The prescriptions are designed to provide environments in which minimum levels of thermal acceptability (based on general thermal comfort) can be selected as either 90% or 80%. 15 15 o 70% rh 19 C Wet Bulb o 70% rh 60% rh 19 C Wet Bulb 60% rh o 18o C Wet Bulb 18 C Wet Bulb 50% rh 50% rh 100% rh 100% rh Summer 10 Summer 10 Winter Winter 30% rh 30% rh 5 5 20.5 ET* 25.5 ET* 21.3 ET* 24.7 ET* HUMIDITY MIXING RATIO (g/kg) HUMIDITY MIXING RATIO (g/kg) 0 0 15 20 25 30 15 20 25 30 o o OPERATIVE TEMPERATURE ( C) OPERATIVE TEMPERATURE ( C) Figure 5.3: Psychrometric charts showing summer and winter comfort zone prescriptions for 90% acceptability (left panel) and 80% acceptability (right panel) Operative Temperature. The operative temperature range between which, theoretically, no more than 20% of occupants during light, primarily sedentary activity ( 1.2. met), assuming they wear the same level of clothing insulation, will find the environment thermally unacceptable is given in Table 5.1. The acceptable range of operative temperatures and humidities for winter and summer is further defined on the psychrometric chart of Figure 5.3. The comfort zones are: o o a) Winter: to = 20.5 C to 24.5 C at 50% rh for 80% acceptability level. o o to = 21.3 C to 23.7 C at 50% rh for 90% acceptability level. The slanting side boundaries of the winter zones in Figure 5.3 are defined in terms of effective temperature (ET*) lines and are loci of constant thermal sensations. o o b) Summer: to = 21.5 C to 25.5 C at 50% rh for 80% acceptability level. o o to = 22.3 C to 24.7 C at 50% rh for 90% acceptability level. The slanting side boundaries of the summer zones in Figure 5.3 are defined in terms of effective temperature (ET*) lines and are loci of constant thermal sensations. Variable Temperature Standard page 163 MRL Australia ASHRAE RP-884 Final Report The winter and summer comfort zones overlap in the 22oC to 23oC range. In this region people in summer dress would tend to approach slightly cool sensation while those in winter clothing would be near the slightly warm sensation. In reality, the boundaries of each zone are not as sharp as depicted in Figure 5.3 due to inter-individual clothing and activity differences. Table 5.1: Optimum and acceptable ranges of operative temperature for persons engaged in light, primarily sedentary activity ( 1.2 mets) at 50% relative humidity and mean air speed 0.15 ms-1. For use in buildings with central HVAC systems. Description of Icl Operative Temperature Season typical thermal insulation clo optimum range range temperature (90% accept.) (80% accept.) Winter heavy slacks, long sleeve shirt, 1.05 22.5 oC 21.3 - 23.7 oC 20.5 - 24.5 oC sweater and office chair Summer light slacks, short sleeve shirt 0.65 23.5 oC 22.3 - 24.7 oC 21.5 - 25.5 oC and office chair For infants, certain elderly persons, and individuals who are physically disabled, the lower limits of Table 5.1 should be avoided. Variable Temperature Standard page 164 MRL Australia ASHRAE RP-884 Final Report 5.2. A variable temperature standard for application in naturally ventilated buildings 5.2.1. Purpose To specify the thermal environmental conditions that will be acceptable to a majority of the occupants within naturally ventilated spaces. 5.2.2 Scope · The environmental factors addressed are temperature, thermal radiation, humidity. · It is intended that all of the criteria in this standard be applied together, since comfort in the space environment is complex and responds to the interaction of all of the factors that are addressed. · This standard applies to general thermal comfort conditions and excludes local discomforts such as draft, vertical thermal stratification, and radiant asymmetry. · This standard specifies thermal environmental conditions acceptable for healthy people at atmospheric pressure equivalent to altitudes up to 3000 m in indoor spaces designed for human occupancy for periods not less than 15 minutes. · This standard does not address such non-thermal environmental factors as air quality, acoustics, and illumination; nor other physical, chemical or biological space contaminants which may affect comfort or health. · This standard is intended for use in design of naturally ventilated buildings and evaluation of existing thermal environments within such buildings. · The standard applies exclusively to indoor environments without centralised HVAC systems. Such buildings are presumed to have operable windows which the occupants have some degree of control over. They may have some form of heating installed, but it would be controlled by the building occupants, either individually or in small groups. · The standard cannot be used to decide when and where to install centralised air-conditioning. While it may provide useful information in relation to such decisions, the standard cannot be regarded as the Variable Temperature Standard page 165 MRL Australia ASHRAE RP-884 Final Report sole criterion. For example, the adaptive opportunity afforded the occupants of naturally ventilated buildings should also be borne in mind. 5.2.3. Definitions adaptive model: A linear regression model that relates indoor design temperatures or acceptable temperature ranges to outdoor meteorological or climatological parameters. Note that the range of applicable outdoor climates should be restricted to that appearing on the X-axis of the adaptive model’s graph (i.e. they should not be extrapolated beyond the range of the regression models’ X-variable). adaptive opportunity: Buildings provide their occupants with varying degrees of adaptive opportunity or scope to adjust the internal environment (and themselves) to achieve thermal comfort. Sealed, centrally air-conditioned office buildings with open-plan floor layouts provide minimal adaptive opportunity, while naturally ventilated buildings with operable windows and ceiling fans within small single- or dual-occupant offices typically afford high degrees of adaptive opportunity. comfort, thermal: that condition of mind which expresses satisfaction with the thermal environment; it requires subjective evaluation. Optimum thermal comfort is assumed to correspond with a thermal preference vote of “want no change” environment, thermal: the characteristics of the environment which affect a person’s heat loss. environment, acceptable thermal: an environment which at least 80% of the occupants would find thermally acceptable. humidity, relative (rh): the ratio of the mole fraction of water vapor present in the air to the mole fraction of water vapor present in saturated air at the same temperature and barometric pressure; alternatively, it equals the ratio of the partial pressure (or density) of the water vapor in the air to the saturation pressure (or density) of water vapor at the same temperature. mean monthly (or daily) outdoor effective temperature: Arithmetic average of 6am outdoor ET* (assumed minimum), and 3pm outdoor ET* (assumed maximum) for a calendar month or particular day. Variable Temperature Standard page 166 MRL Australia ASHRAE RP-884 Final Report naturally ventilated: Those premises in which a centralised heating, ventilation and air-conditioning systems are absent and windows are operable. Some form of heating may be present, but it would normally be under the control of building occupants, either individually or in small groups. neutrality, thermal: the indoor thermal index value (usually operative temperature) corresponding with a maximum number of building occupants voting “neutral” on the thermal sensation scale. preference, thermal: a conscious desire for change in one’s thermal state, commonly graded into the categories, 1“want cooler,” 2 “want no change,” 3“want warmer”; it requires subjective evaluation. Preferred temperature, i.e. that corresponding with a maximum number of “2” votes, corresponds reasonably well with thermal neutrality in naturally ventilated buildings. sensation, thermal: a conscious feeling commonly graded into the categories, -3 cold, -2 cool, -1 slightly cool, 0 neutral, +1 slightly warm, +2 warm, and +3 hot; it requires subjective evaluation. Optimum thermal comfort corresponds reasonably well with a thermal sensation vote of “neutral” in naturally ventilated buildings. temperature, air (ta): the dry-bulb temperature of the air surrounding the occupant. temperature, mean radiant (tr): the uniform surface temperature of an imaginary black enclosure in which an occupant would exchange the same amount of radiant heat as in the actual nonuniform space. Variable Temperature Standard page 167 MRL Australia ASHRAE RP-884 Final Report temperature, operative (to): the uniform temperature of an imaginary black enclosure in which an occupant would exchange the same amount of heat by radiation plus convection as in the actual non- uniform environment. Operative temperature is numerically the average of the air temperature (ta) and mean radiant temperature (tr), weighted by their respective heat transfer coefficients (hc and hr): (hc ta + hr tr ) to = (hc + hr ) which typically equates to the arithmetic average of mean air and radiant temperatures temperature, effective (ET*): the operative temperature (to) of an enclosure at 50% relative humidity which would cause the same sensible plus latent heat exchange from a person as would the actual environment. temperature, optimum operative: the operative temperature that satisfies the greatest possible number of people at a given clothing and activity level. Optimum operative temperature in this standard corresponds reasonably well with both thermal neutrality and preferred temperature. zone, occupied: the region normally occupied by people within a space, generally considered to be between the floor and 1.8 m above the floor and more than 0.6 m from walls or fixed air conditioning equipment. 5.2.4. Conditions for an acceptable thermal environment. The conditions for an acceptable thermal environment shall be based exclusively on the adaptive model (linear regression) approach. The PMV/PPD model is inapplicable to naturally ventilated premises because it only partially accounts for processes of thermal adaptation to indoor climate. The prescription of summer and winter comfort zones is inappropriate for this standard because the steep gradient on the naturally ventilated adaptive model would render climatological definitions of universal “summer” and “winter” conditions misleading. The adaptive models in this section can be applied where weather data in the form of mean outdoor effective temperature for the relevant time of year are available. These need to be calculated from basic outdoor air temperature maxima (3 pm) and minima Variable Temperature Standard page 168 MRL Australia ASHRAE RP-884 Final Report (6 am), along with coincident humidity. In the absence of current meteorological observations, published mean climatological data for the relevant month from the nearest weather station may suffice. 32 80% acceptability upper limit 30 C) o 28 26 24 22 80% acceptability lower limit comfort temp ( 20 18 comfort temp. in NV = 18.9 + 0.255 * outdoor ET* 16 5 10 15 20 25 30 35 mean daily outdoor effective temperature (oC) Figure 5.4: The adaptive comfort zone’s optimum and limits for an 80% acceptability level in naturally ventilated premises. 32 30 90% acceptability upper limit C) o 28 26 24 22 90% acceptability lower limit comfort temp ( 20 18 comfort temp. in NV = 18.9 + 0.255 * outdoor ET* 16 5 10 15 20 25 30 35 mean daily outdoor effective temperature (oC) Figure 5.5: The adaptive comfort zone’s optimum and limits for a 90% acceptability level in naturally ventilated premises. 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Bibliography page 183 MRL Australia ASHRAE RP-884 Final Report Bibliography page 184 MRL Australia ASHRAE RP-884 Final Report APPENDIX A - THERMAL SENSATION AND NEUTRALITY FOR EACH BUILDING IN THE RP-884 DATABASE Appendix A page 185 MRL Australia ASHRAE RP-884 Final Report South Wales UK (summer), HVAC South Wales UK (summer), HVAC building #2 building #4 3 3 2 2 1 this plot had only one value 1 0 0 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv -3 -3 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 Operative Temp (degC) Operative Temp (degC) South Wales UK (summer), HVAC South Wales UK (summer), HVAC South Wales UK (summer), HVAC building #8 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 15 16 17 18 19 20 21 15 16 17 18 19 20 21 Operative Temp (degC) ET* (degC) ET* (degC) South Wales UK (summer), HVAC South Wales UK (summer), HVAC South Wales UK (summer), HVAC building #4 building #8 building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae -2 pmv pmv -3 -3 -3 15 16 17 18 19 20 21 15 16 17 18 19 20 21 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV South Wales UK (summer), HVAC South Wales UK (summer), HVAC South Wales UK (summer), HVAC building #3 building #4 building #8 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV South Wales UK (summer), HVAC South Wales UK (summer), HVAC South Wales UK (summer), HVAC building #2 building #3 building #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) South Wales UK (summer), HVAC building #8 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 18 20 22 24 26 28 30 32 SET (degC) Appendix A page 186 MRL Australia ASHRAE RP-884 Final Report South Wales UK (winter), HVAC South Wales UK (winter), HVAC building building #1 #5 South Wales UK (winter), HVAC building 3 3 #6 2 2 3 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 -2 Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 18 19 20 21 22 18 19 20 21 22 18 19 20 21 22 23 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) South Wales UK (winter), HVAC building South Wales UK (winter), HVAC building South Wales UK (winter), HVAC building #7 #1 #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote -2 ashrae -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 18 19 20 21 22 11 12 13 14 15 16 17 11 12 13 14 15 16 17 Operative Temp (degC) ET* (degC) ET* (degC) South Wales UK (winter), HVAC building South Wales UK (winter), HVAC building South Wales UK (winter), HVAC building #6 #7 #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 ASH -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 -3 -3 -3 11 12 13 14 15 16 17 11 12 13 14 15 16 17 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV South Wales UK (winter), HVAC building South Wales UK (winter), HVAC building South Wales UK (winter), HVAC building #5 #6 #7 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV South Wales UK (winter), HVAC building South Wales UK (winter), HVAC building South Wales UK (winter), HVAC building #1 #5 #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 14 16 18 20 22 24 26 28 30 14 16 18 20 22 24 26 28 30 14 16 18 20 22 24 26 28 30 SET (degC) SET (degC) SET (degC) South Wales UK (winter), HVAC building #7 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 14 16 18 20 22 24 26 28 30 SET (degC) Appendix A page 187 MRL Australia ASHRAE RP-884 Final Report Bangkok Thailand (summer), HVAV Bangkok Thailand (summer), HVAV Bangkok Thailand (summer), HVAC building #1 building #2 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20 21 22 23 24 25 26 27 20 21 22 23 24 25 26 27 20 21 22 23 24 25 26 27 28 Operative Temp (degC) Operative Temp (degC) ET* (degC) Bangkok Thailand (summer), HVAC Bangkok Thailand (summer), HVAC Bangkok Thailand (summer), HVAC building #2 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 ASH -1 Mean Vote ashrae -2 pmv -2 -2 -3 -3 -3 20 21 22 23 24 25 26 27 28 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 ET* (degC) PMV PMV Bangkok Thailand (summer), HVAC Bangkok Thailand (summer), HVAC Bangkok Thailand (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 19.5 21.5 23.5 25.5 27.5 29.5 31.5 19.5 21.5 23.5 25.5 27.5 29.5 31.5 25 26 27 28 29 30 31 32 33 34 35 SET (degC) SET (degC) Operative Temp (degC) Bangkok Thailand (summer), NV Bangkok Thailand (summer), NV Bangkok Thailand (summer), NV building #4 building #5 building 3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 25 26 27 28 29 30 31 32 33 34 25 26 27 28 29 30 31 32 25 27 29 31 33 35 37 Operative Temp (degC) Operative Temp (degC) ET* (degC) Bangkok Thailand (summer), NV Bangkok Thailand (summer), NV Bangkok Thailand (summer), NV building #4 building #5 building #3 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 -3 -3 -3 25 27 29 31 33 35 37 25 27 29 31 33 35 37 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV Bangkok Thailand (summer), NV building #4 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 188 MRL Australia ASHRAE RP-884 Final Report Bangkok Thailand (summer), NV Bangkok Thailand (summer), NV Bangkok Thailand (summer), NV building #5 building #3 building #4 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 24 26 28 30 32 34 36 24 26 28 30 32 34 36 PMV SET (degC) SET (degC) Bangkok Thailand (summer), NV Antioch CA US (winter), HVAC Antioch CA US (winter), HVAC building #5 building #1 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 24 26 28 30 32 34 36 22.5 23.5 24.5 21.5 22.5 23.5 SET (degC) Operative Temp (degC) ET* (degC) Antioch CA US (winter), HVAC Antioch CA US (winter), HVAC Jakarta Indonesia (summer), HVAC building #1 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 -2 Mean Vote ashrae Maen Vote ashrae -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 20.5 22.5 24.5 26.5 28.5 22 24 26 28 30 32 PMV SET (degC) Operative Temp (degC) Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), HVAC building #3 building #4 building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Maen Vote -2 ashrae Maen Vote -2 ashrae Maen Vote -2 ashrae pmv pmv pmv -3 -3 -3 22 24 26 28 30 32 22 24 26 28 30 32 22 24 26 28 30 32 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), HVAC building #7 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Maen Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 22 24 26 28 30 32 22 24 26 28 30 32 34 22 24 26 28 30 32 34 Operative Temp (degC) ET* (degC) ET* (degC) Jakarta Indonesia (summer), HVAC building #4 3 2 1 0 -1 Mean Vote -2 ashrae pmv -3 22 24 26 28 30 32 34 ET* (degC) Appendix A page 189 MRL Australia ASHRAE RP-884 Final Report Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), HVAC building #5 building #7 building #2 3 3 3 ashrae 2 pmv 2 2 1 1 1 0 0 0 -1 -1 ASH -1 Mean Vote Mean Vote ashrae -2 -2 pmv -2 -3 -3 -3 22 24 26 28 30 32 34 22 24 26 28 30 32 34 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), HVAC building #3 building #4 building #5 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Jakarta Indonesia (summer), HVAC Jakarta Indonesia (summer), NV Jakarta Indonesia (summer), NV building #7 building #1 building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote Mean Vote ashrae -2 ashrae -2 -2 pmv pmv -3 -3 -3 -3 -2 -1 0 1 2 3 28 29 30 31 32 33 30 31 32 33 34 35 PMV Operative Temp (degC) ET* (degC) Jakarta Indonesia (summer), NV Jakarta Indonesia (summer), Mixed Jakarta Indonesia (summer), Mixed building #1 building #6 building #6 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote Mean Vote -2 -2 ashrae -2 ashrae pmv pmv -3 -3 -3 -3 -2 -1 0 1 2 3 26 27 28 29 26 27 28 29 30 31 PMV Operative Temp (degC) ET* (degC) Jakarta Indonesia (summer), Mixed Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), building #6 HVAC building #1 HVAC building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 21 22 23 24 25 26 27 21 22 23 24 25 26 27 PMV Operative Temp (degC) Operative Temp (degC) Montreal Canada RP-821 (summer), The Jakarta, Indonesian files HVAC building #3 do not have SET as a variable 3 2 1 0 -1 Mean Vote -2 ashrae pmv -3 21 22 23 24 25 26 27 Operative Temp (degC) Appendix A page 190 MRL Australia ASHRAE RP-884 Final Report Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #4 HVAC building #5 HVAC building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 21 22 23 24 25 26 27 21 22 23 24 25 26 27 21 22 23 24 25 26 27 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #7 HVAC building #8 HVAC building #9 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 21 22 23 24 25 26 27 21 22 23 24 25 26 27 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #10 HVAC building #11 HVAC building #12 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 21 22 23 24 25 26 27 21 22 23 24 25 26 27 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #1 HVAC building #2 HVAC building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 21 22 23 24 25 26 27 21 22 23 24 25 26 27 ET* (degC) ET* (degC) ET* (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #4 HVAC building #5 HVAC building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 21 22 23 24 25 26 27 21 22 23 24 25 26 27 ET* (degC) ET* (degC) ET* (degC) Montreal Canada RP-821 (summer), HVAC building #7 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 21 22 23 24 25 26 27 ET* (degC) Appendix A page 191 MRL Australia ASHRAE RP-884 Final Report Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #8 HVAC building #9 HVAC building #10 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 21 22 23 24 25 26 27 21 22 23 24 25 26 27 ET* (degC) ET* (degC) ET* (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #11 HVAC building #12 HVAC building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 ASH -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 -3 -3 -3 21 22 23 24 25 26 27 21 22 23 24 25 26 27 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #2 HVAC building #3 HVAC building #4 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #5 HVAC building #6 HVAC building #7 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #8 HVAC building #9 HVAC building #10 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Montreal Canada RP-821 (summer), HVAC building #11 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 192 MRL Australia ASHRAE RP-884 Final Report Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #12 HVAC building #1 HVAC building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 PMV SET (degC) SET (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #3 HVAC building #4 HVAC building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #6 HVAC building #7 HVAC building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), Montreal Canada RP-821 (summer), HVAC building #9 HVAC building #10 HVAC building #11 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Montreal Canada RP-821 (summer), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #12 HVAC building #1 HVAC building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20 22 24 26 28 30 32 20 21 22 23 24 25 20 21 22 23 24 25 SET (degC) Operative Temp (degC) Operative Temp (degC) Montreal Canada RP-821 (winter), HVAC building #3 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 20 21 22 23 24 25 Operative Temp (degC) Appendix A page 193 MRL Australia ASHRAE RP-884 Final Report Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #4 HVAC building #5 HVAC building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20 21 22 23 24 25 20 21 22 23 24 25 20 21 22 23 24 25 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #7 HVAC building #8 HVAC building #9 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20 21 22 23 24 25 20 21 22 23 24 25 20 21 22 23 24 25 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #10 HVAC building #11 HVAC building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 20 21 22 23 24 25 20 21 22 23 24 25 19.5 20.5 21.5 22.5 23.5 24.5 Operative Temp (degC) Operative Temp (degC) ET* (degC) Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #2 HVAC building #3 HVAC building #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19.5 20.5 21.5 22.5 23.5 24.5 19.5 20.5 21.5 22.5 23.5 24.5 19.5 20.5 21.5 22.5 23.5 24.5 ET* (degC) ET* (degC) ET* (degC) Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #5 HVAC building #6 HVAC building #7 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19.5 20.5 21.5 22.5 23.5 24.5 19.5 20.5 21.5 22.5 23.5 24.5 19.5 20.5 21.5 22.5 23.5 24.5 ET* (degC) ET* (degC) ET* (degC) Montreal Canada RP-821 (winter), HVAC building #8 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 19.5 20.5 21.5 22.5 23.5 24.5 ET* (degC) Appendix A page 194 MRL Australia ASHRAE RP-884 Final Report Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #9 HVAC building #10 HVAC building #11 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19.5 20.5 21.5 22.5 23.5 24.5 19.5 20.5 21.5 22.5 23.5 24.5 19.5 20.5 21.5 22.5 23.5 24.5 ET* (degC) ET* (degC) ET* (degC) Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #1 HVAC building #2 HVAC building #3 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #4 HVAC building #5 HVAC building #6 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #7 HVAC building #8 HVAC building #9 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #10 HVAC building #11 HVAC building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 Mean Vote ashrae -2 -2 -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 20 22 24 26 28 30 32 PMV PMV SET (degC) Montreal Canada RP-821 (winter), HVAC building #2 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 20 22 24 26 28 30 32 SET (degC) Appendix A page 195 MRL Australia ASHRAE RP-884 Final Report Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #3 HVAC building #4 HVAC building #5 3 3 3 ashrae 2 2 pmv 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote Mean Vote ashrae -2 pmv -2 -2 pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #6 HVAC building #7 HVAC building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), Montreal Canada RP-821 (winter), HVAC building #9 HVAC building #10 HVAC building #11 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 19 20 21 22 23 24 25 26 27 19 20 21 22 23 24 25 26 27 19 20 21 22 23 24 25 26 27 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC building #4 building #5 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19 20 21 22 23 24 25 26 27 19 20 21 22 23 24 25 26 27 19 20 21 22 23 24 25 26 27 Operative Temp (degC) Operative Temp (degC) ET* (degC) Brisbane Australia (summer), HVAC building #2 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 19 20 21 22 23 24 25 26 27 ET* (degC) Appendix A page 196 MRL Australia ASHRAE RP-884 Final Report Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC building #3 building #4 building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19 20 21 22 23 24 25 26 27 19 20 21 22 23 24 25 26 27 19 20 21 22 23 24 25 26 27 ET* (degC) ET* (degC) ET* (degC) Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC building #4 building #5 building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 Mean Vote ashrae -2 -2 -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 17 19 21 23 25 27 29 31 PMV PMV SET (degC) Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC Brisbane Australia (summer), HVAC building #2 building #3 building #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 17 19 21 23 25 27 29 31 17 19 21 23 25 27 29 31 17 19 21 23 25 27 29 31 SET (degC) SET (degC) SET (degC) Brisbane Australia (summer), HVAC Brisbane Australia (summer), NV Brisbane Australia (summer), NV building #5 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 17 19 21 23 25 27 29 31 25.5 26.5 27.5 28.5 29.5 30.5 31.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 SET (degC) Operative Temp (degC) Operative Temp (degC) Brisbane Australia (summer), NV building #3 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 25.5 26.5 27.5 28.5 29.5 30.5 31.5 Operative Temp (degC) Appendix A page 197 MRL Australia ASHRAE RP-884 Final Report Brisbane Australia (summer), NV Brisbane Australia (summer), NV Brisbane Australia (summer), NV building #4 building #5 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 25.5 26.5 27.5 28.5 29.5 30.5 31.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 25 27 29 31 33 35 37 Operative Temp (degC) Operative Temp (degC) ET* (degC) Brisbane Australia (summer), NV Brisbane Australia (summer), NV Brisbane Australia (summer), NV building #2 building #3 building #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 25 27 29 31 33 35 37 25 27 29 31 33 35 37 25 27 29 31 33 35 37 ET* (degC) ET* (degC) ET* (degC) Brisbane Australia (summer), NV Brisbane Australia (summer), NV Brisbane Australia (summer), NV building #5 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 Mean Vote ashrae -2 pmv -2 -2 -3 -3 -3 25 27 29 31 33 35 37 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 ET* (degC) PMV PMV Brisbane Australia (summer), NV Brisbane Australia (summer), NV Brisbane Australia (summer), NV building #3 building #4 building #5 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Brisbane Australia (summer), NV Brisbane Australia (summer), NV Brisbane Australia (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae -2 ashrae ashrae -2 pmv pmv -2 pmv -3 -3 -3 23 25 27 29 31 33 35 37 23 25 27 29 31 33 35 37 23 25 27 29 31 33 35 37 SET (degC) SET (degC) SET (degC) Brisbane Australia (summer), NV building #4 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 23 25 27 29 31 33 35 37 SET (degC) Appendix A page 198 MRL Australia ASHRAE RP-884 Final Report Brisbane Australia (summer), NV Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #5 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae ashrae -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 23 25 27 29 31 33 35 37 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 SET (degC) Operative Temp (degC) Operative Temp (degC) Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #3 building #4 building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #6 building #7 building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 ET* (degC) ET* (degC) ET* (degC) Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #4 building #5 building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote -2 ashrae Mean Vote ashrae -2 pmv pmv -2 pmv -3 -3 -3 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 ET* (degC) ET* (degC) ET* (degC) Darwin Australia (summer-dry), HVAC building #7 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 18 20 22 24 26 28 30 32 ET* (degC) Appendix A page 199 MRL Australia ASHRAE RP-884 Final Report Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #8 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 Mean Vote ashrae -2 pmv -2 -2 -3 -3 -3 18 20 22 24 26 28 30 32 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 ET* (degC) PMV PMV Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #3 building #4 building #5 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #6 building #7 building #8 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC Darwin Australia (summer-dry), HVAC building #4 building #5 building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae -2 ashrae -2 ashrae -2 pmv pmv pmv -3 -3 -3 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 18 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Darwin Australia (summer-dry), HVAC building #7 3 2 1 0 -1 Mean Vote -2 ashrae pmv -3 18 20 22 24 26 28 30 32 SET (degC) Appendix A page 200 MRL Australia ASHRAE RP-884 Final Report Darwin Australia (summer-dry), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC building #8 building #8 building #9 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18 20 22 24 26 28 30 32 20 22 24 26 28 30 20 22 24 26 28 30 SET (degC) Operative Temp (degC) Operative Temp (degC) Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC building #10 building #11 building #12 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 20 22 24 26 28 30 20 22 24 26 28 30 20 22 24 26 28 30 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC building #13 building #14 building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote ashrae pmv pmv -2 pmv -3 -3 -3 20 22 24 26 28 30 20 22 24 26 28 30 20 22 24 26 28 30 Operative Temp (degC) Operative Temp (degC) ET* (degC) Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC building #9 building #10 building #11 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 20 22 24 26 28 30 20 22 24 26 28 30 20 22 24 26 28 30 ET* (degC) ET* (degC) ET* (degC) Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (Summer), HVAC building #12 building #13 building #14 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 20 22 24 26 28 30 20 22 24 26 28 30 20 22 24 26 28 30 ET* (degC) ET* (degC) ET* (degC) Darwin Australia (summer-wet), HVAC building #8 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 201 MRL Australia ASHRAE RP-884 Final Report Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC building #9 building #10 building #11 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC building #12 building #13 building #14 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC building #8 building #9 building #10 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 23 25 27 29 31 21 23 25 27 29 31 21 23 25 27 29 31 SET (degC) SET (degC) SET (degC) Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC Darwin Australia (summer-wet), HVAC building #11 building #12 building #13 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 23 25 27 29 31 21 23 25 27 29 31 21 23 25 27 29 31 SET (degC) SET (degC) SET (degC) Darwin Australia (summer-wet), HVAC Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC building #14 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 23 25 27 29 31 19 21 23 25 27 29 31 19 21 23 25 27 29 31 SET (degC) Operative Temp (degC) Operative Temp (degC) Melbourne Australia (summer), HVAC building #3 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 19 21 23 25 27 29 31 Operative Temp (degC) Appendix A page 202 MRL Australia ASHRAE RP-884 Final Report Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC building #4 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19 21 23 25 27 29 31 19.5 21.5 23.5 25.5 27.5 29.5 19.5 21.5 23.5 25.5 27.5 29.5 Operative Temp (degC) ET* (degC) ET* (degC) Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC building #3 building #4 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 ASH -1 Mean Vote Mean Vote ashrae ashrae -2 pmv -2 pmv -2 -3 -3 -3 19.5 21.5 23.5 25.5 27.5 29.5 19.5 21.5 23.5 25.5 27.5 29.5 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC building #2 building #3 building #4 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC Melbourne Australia (summer), HVAC building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19 21 23 25 27 29 31 33 19 21 23 25 27 29 31 33 19 21 23 25 27 29 31 33 SET (degC) SET (degC) SET (degC) Melbourne Australia (summer), HVAC Melbourne Australia (summer), NV Melbourne Australia (summer), NV building #4 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19 21 23 25 27 29 31 33 10 15 20 25 30 35 10 15 20 25 30 35 SET (degC) Operative Temp (degC) Operative Temp (degC) Melbourne Australia (summer), NV building #3 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 10 15 20 25 30 35 Operative Temp (degC) Appendix A page 203 MRL Australia ASHRAE RP-884 Final Report Melbourne Australia (summer), NV Melbourne Australia (summer), NV Melbourne Australia (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 12 14 16 18 20 22 24 26 28 30 32 12 14 16 18 20 22 24 26 28 30 32 12 14 16 18 20 22 24 26 28 30 32 ET* (degC) ET* (degC) ET* (degC) Melbourne Australia (summer), NV Melbourne Australia (summer), NV Melbourne Australia (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Melbourne Australia (summer), NV Melbourne Australia (summer), NV Melbourne Australia (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19 21 23 25 27 29 31 19 21 23 25 27 29 31 19 21 23 25 27 29 31 SET (degC) SET (degC) SET (degC) Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC building #4 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 18 20 22 24 26 18 20 22 24 26 Operative Temp (degC) ET* (degC) ET* (degC) Ottawa Canada (summer), HVAC building #3 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 18 20 22 24 26 ET* (degC) Appendix A page 204 MRL Australia ASHRAE RP-884 Final Report Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC building #4 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 Mean Vote ashrae -2 pmv -2 -2 -3 -3 -3 18 20 22 24 26 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 ET* (degC) PMV PMV Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC building #3 building #4 building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 -1 Mean Vote ashrae -2 -2 -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 20 22 24 26 28 30 32 PMV PMV SET (degC) Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC Ottawa Canada (summer), HVAC building #2 building #3 building #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Karachi Pakistan (summer), NV building Karachi Pakistan (winter), NV Multan Pakistan (summer), NV #1 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 19 21 23 25 27 29 31 33 35 19 21 23 25 27 29 31 33 35 10 15 20 25 30 35 40 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Peshawar Pakistan (summer), NV Peshawar Pakistan (winter), NV building Quetta Pakistan (summer), NV building building #3 #3 #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 10 15 20 25 30 35 40 10 15 20 25 30 35 40 10 15 20 25 30 35 40 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Quetta Pakistan (winter), NV building #4 3 2 1 0 -1 Mean Vote -2 ashrae pmv -3 10 15 20 25 30 35 40 Operative Temp (degC) Appendix A page 205 MRL Australia ASHRAE RP-884 Final Report Saidu Sharif Pakistan (summer), NV Saidu Sharif Pakistan (winter), NV Karachi Pakistan (summer), NV building building #5 building #5 #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 10 15 20 25 30 35 40 10 15 20 25 30 35 40 19 21 23 25 27 29 31 33 35 Operative Temp (degC) Operative Temp (degC) ET* (degC) Karachi Pakistan (winter), NV Multan Pakistan (summer), NV Peshawar Pakistan (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19 21 23 25 27 29 31 33 35 23 25 27 29 31 33 35 37 10 15 20 25 30 35 40 ET* (degC) ET* (degC) ET* (degC) Peshawar Pakistan (winter), NV building Quetta Pakistan (summer), NV Quetta Pakistan (winter), NV #3 building #4 building #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 10 15 20 25 30 35 40 10 15 20 25 30 35 40 10 15 20 25 30 35 40 ET* (degC) ET* (degC) ET* (degC) Saidu Sharif Pakistan (summer), NV Saidu Sharif Pakistan (winter), NV Karachi Pakistan (summer), NV building building #5 building #5 #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 -3 -3 -3 10 15 20 25 30 35 10 15 20 25 30 35 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV Karachi Pakistan (winter), NV Multan Pakistan (summer), NV Peshawar Pakistan (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Peshawar Pakistan (winter), NV building #3 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 206 MRL Australia ASHRAE RP-884 Final Report Quetta Pakistan (summer), NV Quetta Pakistan (winter), NV Saidu Sharif Pakistan (summer), NV building #4 building #4 building #5 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Saidu Sharif Pakistan (winter), NV Karachi Pakistan (summer), NV building Karachi Pakistan (winter), NV building #5 #1 building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 19 21 23 25 27 29 31 33 35 37 19 21 23 25 27 29 31 33 35 37 PMV SET (degC) SET (degC) Multan Pakistan (summer), NV Peshawar Pakistan (summer), NV Peshawar Pakistan (winter), NV building building #2 building #3 #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote -2 ashrae Mean Vote ashrae -2 ashrae pmv -2 pmv pmv -3 -3 -3 10 15 20 25 30 35 40 10 15 20 25 30 35 40 10 15 20 25 30 35 40 SET (degC) SET (degC) SET (degC) Quetta Pakistan (summer), NV Quetta Pakistan (winter), NV Saidu Sharif Pakistan (summer), NV building #4 building #4 building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 10 15 20 25 30 35 40 10 15 20 25 30 35 40 10 15 20 25 30 35 40 SET (degC) SET (degC) SET (degC) Saidu Sharif Pakistan (winter), NV Athens Greece (summer), NV Athens Greece (summer), NV building #5 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 10 15 20 25 30 35 40 15 20 25 30 35 40 15 20 25 30 35 40 SET (degC) Operative Temp (degC) Operative Temp (degC) Athens Greece (summer), NV building #3 3 2 1 0 -1 Mean Vote -2 ashrae pmv -3 15 20 25 30 35 40 Operative Temp (degC) Appendix A page 207 MRL Australia ASHRAE RP-884 Final Report Athens Greece (summer), NV Athens Greece (summer), NV Athens Greece (summer), NV building #4 building 5 building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 15 20 25 30 35 40 15 20 25 30 35 40 15 20 25 30 35 40 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Athens Greece (summer), NV Athens Greece (summer), NV Athens Greece (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 15 20 25 30 35 40 15 20 25 30 35 40 15 20 25 30 35 40 ET* (degC) ET* (degC) ET* (degC) Athens Greece (summer), NV Athens Greece (summer), NV Athens Greece (summer), NV building #4 building #5 building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 15 20 25 30 35 40 15 20 25 30 35 40 15 20 25 30 35 40 ET* (degC) ET* (degC) ET* (degC) Athens Greece (summer), NV Athens Greece (summer), NV Athens Greece (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Athens Greece (summer), NV Athens Greece (summer), NV Athens Greece (summer), NV building #4 building #5 building #6 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Athens Greece (summer), NV building #1 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 10 15 20 25 30 35 40 SET (degC) Appendix A page 208 MRL Australia ASHRAE RP-884 Final Report Athens Greece (summer), NV Athens Greece (summer), NV Athens Greece (summer), NV building #2 building #3 building #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote ashrae Mean Vote ashrae pmv -2 pmv -2 pmv -3 -3 -3 10 15 20 25 30 35 40 10 15 20 25 30 35 40 10 15 20 25 30 35 40 SET (degC) SET (degC) SET (degC) Athens Greece (summer), NV Athens Greece (summer), NV Oxford UK (summer), NV building #1 building #5 building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 10 15 20 25 30 35 40 10 15 20 25 30 35 40 14 16 18 20 22 24 26 28 30 SET (degC) SET (degC) Operative Temp (degC) Oxford UK (summer), NV building #2 Oxford UK (summer), NV building #3 Oxford UK (summer), NV building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 14 16 18 20 22 24 26 28 30 14 16 18 20 22 24 26 28 30 14 16 18 20 22 24 26 28 Operative Temp (degC) Operative Temp (degC) ET* (degC) Oxford UK (summer), NV building #2 Oxford UK (summer), NV building #3 Oxford UK (summer), NV building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote Mean Vote -2 ashrae -2 ashrae -2 pmv pmv -3 -3 -3 14 16 18 20 22 24 26 28 14 16 18 20 22 24 26 28 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV Oxford UK (summer), NV building #2 Oxford UK (summer), NV building #3 Oxford UK (summer), NV building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 Mean Vote -2 -2 -2 ashrae pmv -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 15 17 19 21 23 25 27 29 31 33 PMV PMV SET (degC) Oxford UK (summer), NV building #2 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 15 17 19 21 23 25 27 29 31 33 SET (degC) Appendix A page 209 MRL Australia ASHRAE RP-884 Final Report Oxford UK (summer), NV building #3 Sydney Australia (summer), Mixed Sydney Australia (summer), Mixed 3 building #1 building #1 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote ashrae Mean Vote ashrae pmv -2 pmv -2 pmv -3 -3 -3 15 17 19 21 23 25 27 29 31 33 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 SET (degC) Operative Temp (degC) ET* (degC) Sydney Australia (summer), Mixed Sydney Australia (summer), Mixed Sydney Australia (winter), Mixed building #1 building #1 building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote Mean Vote ashrae ashrae -2 -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 19 21 23 25 27 29 31 17 19 21 23 25 27 29 PMV SET (degC) Operative Temp (degC) Sydney Australia (winter), Mixed Sydney Australia (winter), Mixed Sydney Australia (winter), Mixed building #1 building #1 building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote Mean Vote ashrae ashrae -2 pmv -2 -2 pmv -3 -3 -3 17 19 21 23 25 27 -3 -2 -1 0 1 2 3 19 21 23 25 27 29 31 ET* (degC) PMV SET (degC) Sydney Australia (winter), HVAC Sydney Australia (winter), HVAC Sydney Australia (winter), HVAC building #2 building #2 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 ASH -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 -3 -3 -3 21 22 23 24 20.5 21.5 22.5 23.5 -3 -2 -1 0 1 2 3 Operative Temp (degC) ET* (degC) PMV Sydney Australia (winter), HVAC San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 building #2 (summer) HVAC building #2 (summer), HVAC building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19 21 23 25 27 29 31 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 SET (degC) Operative Temp (degC) Operative Temp (degC) San Francisco Bay Area RP-462 (summer), HVAC building #4 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 Operative Temp (degC) Appendix A page 210 MRL Australia ASHRAE RP-884 Final Report San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #7 (summer), HVAC building #8 (summer), HVAC building #9 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae -2 ashrae ashrae -2 pmv pmv -2 pmv -3 -3 -3 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #10 (summer), HVAC building #2 (summer), HVAC building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 20 21 22 23 24 25 26 27 28 20 21 22 23 24 25 26 27 28 Operative Temp (degC) ET* (degC) ET* (degC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #4 (summer), HVAC building #7 (summer), HVAC building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae -2 pmv pmv pmv -3 -3 -3 20 21 22 23 24 25 26 27 28 20 21 22 23 24 25 26 27 28 20 21 22 23 24 25 26 27 28 ET* (degC) ET* (degC) ET* (degC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #9 (summer), HVAC building #10 (summer), HVAC building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 -3 -3 -3 20 21 22 23 24 25 26 27 28 20 21 22 23 24 25 26 27 28 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #3 San Francisco Bay Area RP-462 (summer), HVAC building #7 3 (summer), HVAC building #4 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 ASH -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV San Francisco Bay Area RP-462 (summer), HVAC building #8 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 211 MRL Australia ASHRAE RP-884 Final Report San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #9 (summer), HVAC building #10 (summer), HVAC building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 -2 Mean Vote ashrae -2 -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5 PMV PMV SET (degC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #3 (summer), HVAC building #4 (summer), HVAC building #7 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5 18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5 18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5 SET (degC) SET (degC) SET (degC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #8 (summer), HVAC building #9 (summer), HVAC building #10 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5 18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5 18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5 SET (degC) SET (degC) SET (degC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), NV building #1 (summer), NV building #5 (summer), NV building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20.5 22.5 24.5 26.5 28.5 30.5 20.5 22.5 24.5 26.5 28.5 30.5 20.5 22.5 24.5 26.5 28.5 30.5 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), NV building #1 (summer), NV building #5 (summer), NV building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 21 22 23 24 25 26 27 28 29 21 22 23 24 25 26 27 28 29 21 22 23 24 25 26 27 28 29 ET* (degC) ET* (degC) ET* (degC) San Francisco Bay Area RP-462 (summer), NV building #1 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 212 MRL Australia ASHRAE RP-884 Final Report San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), NV building #5 (summer), NV building #6 (summer), NV building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 -1 Mean Vote ashrae -2 -2 -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 20.5 22.5 24.5 26.5 28.5 30.5 PMV PMV SET (degC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Area RP-462 (winter), (summer), NV building #6 HVAC building #2 (summer), NV building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote -2 ashrae Mean Vote -2 ashrae ashrae pmv pmv -2 pmv -3 -3 -3 20.5 22.5 24.5 26.5 28.5 30.5 20.5 22.5 24.5 26.5 28.5 30.5 17 19 21 23 25 27 29 SET (degC) SET (degC) Operative Temp (degC) San Francisco Area RP-462 (winter), San Francisco Area RP-462 (winter), San Francisco Area RP-462 (winter), HVAC building #3 HVAC building #4 HVAC building #7 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 17 19 21 23 25 27 29 17 19 21 23 25 27 29 17 19 21 23 25 27 29 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) San Francisco Area RP-462 (winter), San Francisco Area RP-462 (winter), San Francisco Area RP-462 (winter), HVAC building #8 HVAC building #9 HVAC building #10 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 17 19 21 23 25 27 29 17 19 21 23 25 27 29 17 19 21 23 25 27 29 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), HVAC building #2 HVAC building #3 HVAC building #4 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 17 19 21 23 25 27 29 17 19 21 23 25 27 29 17 19 21 23 25 27 29 ET* (degC) ET* (degC) ET* (degC) San Francisco Bay Area RP-462 (winter), HVAC building #7 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 17 19 21 23 25 27 29 ET* (degC) Appendix A page 213 MRL Australia ASHRAE RP-884 Final Report San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), HVAC building #8 HVAC building #9 HVAC building #10 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae ashrae -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 17 19 21 23 25 27 29 17 19 21 23 25 27 29 17 19 21 23 25 27 29 ET* (degC) ET* (degC) ET* (degC) San Francisco Area RP-462 (winter), San Francisco Area RP-462 (winter), San Francisco Area RP-462 (winter), HVAC building #2 HVAC building #3 HVAC building #4 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV San Francisco Area RP-462 (winter), San Francisco Area RP-462 (winter), San Francisco Area RP-462 (winter), HVAC building #7 HVAC building #8 HVAC building #9 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV San Francisco Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), HVAC building #10 HVAC building #2 HVAC building #3 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 18 20 22 24 26 28 30 18 20 22 24 26 28 30 PMV SET (degC) SET (degC) San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), HVAC building #4 HVAC building #7 HVAC building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae ashrae -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 18 20 22 24 26 28 30 18 20 22 24 26 28 30 18 20 22 24 26 28 30 SET (degC) SET (degC) SET (degC) San Francisco Bay Area RP-462 (winter), HVAC building #9 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 18 20 22 24 26 28 30 SET (degC) Appendix A page 214 MRL Australia ASHRAE RP-884 Final Report San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), HVAC building #10 NV building #1 NV building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae -2 ashrae -2 ashrae -2 pmv pmv pmv -3 -3 -3 18 20 22 24 26 28 30 19.5 20.5 21.5 22.5 23.5 24.5 25.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 SET (degC) Operative Temp (degC) Operative Temp (degC) San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), NV building #6 NV building #1 NV building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19.5 20.5 21.5 22.5 23.5 24.5 25.5 19.5 21.5 23.5 25.5 19.5 21.5 23.5 25.5 Operative Temp (degC) ET* (degC) ET* (degC) San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), NV building #6 NV building #1 NV building #5 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 Mean Vote -2 ashrae -2 -2 pmv -3 -3 -3 19.5 21.5 23.5 25.5 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 ET* (degC) PMV PMV San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), NV building #6 NV building #1 NV building #5 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 20 21 22 23 24 25 26 27 28 29 20 21 22 23 24 25 26 27 28 29 PMV SET (degC) SET (degC) San Francisco Bay Area RP-462 (winter), Townsville Australia RP-702 Townsville Australia RP-702 NV building #6 (summer-dry), HVAC building #1 (summer-dry), HVAC building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 20 21 22 23 24 25 26 27 28 29 20 22 24 26 20 21 22 23 24 25 26 SET (degC) Operative Temperature (degC) Operative Temperature (degC) Townsville Australia RP-702 (summer-dry), HVAC building #3 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 20 21 22 23 24 25 26 Operative Temperature (degC) Appendix A page 215 MRL Australia ASHRAE RP-884 Final Report Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #4 (summer-dry), HVAC building #5 (summer-dry), HVAC building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote -2 ashra Mean Vote ashrae -2 ashrae e -2 pmv pmv -3 pmv -3 -3 20 21 22 23 24 25 26 20 21 22 23 24 25 26 20 21 22 23 24 25 26 Operative Temperature (degC) Operative Temperature (degC) Operative Temperature (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #7 (summer-dry), HVAC building #8 (summer-dry), HVAC building #9 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 20 21 22 23 24 25 26 20 21 22 23 24 25 26 20 21 22 23 24 25 26 Operative Temperature (degC) Operative Temperature (degC) Operative Temperature (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #10 (summer-dry), HVAC building #11 (summer-dry), HVAC building #12 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 20 21 22 23 24 25 26 20 21 22 23 24 25 26 20 21 22 23 24 25 26 Operative Temperature (degC) Operative Temperature (degC) Operative Temperature (degC) Townsville Australia RP-884 Townsville Australia RP-884 Townsville Australia RP-884 (summer-dry), HVAC building #1 (summer-dry), HVAC building #2 (summer-dry), HVAC building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 20 21 22 23 24 25 26 20 21 22 23 24 25 26 20 21 22 23 24 25 26 ET* (degC) ET* (degC) ET* (degC) Townsville Australia RP-884 Townsville Australia RP-884 Townsville Australia RP-884 (summer-dry), HVAC building #4 (summer-dry), HVAC building #5 (summer-dry), HVAC building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote -2 ashrae Mean Vote ashrae Mean Vote ashrae pmv -2 pmv -2 pmv -3 -3 -3 20 21 22 23 24 25 26 20 21 22 23 24 25 26 20 21 22 23 24 25 26 ET* (degC) ET* (degC) ET* (degC) Townsville Australia RP-884 (summer-dry), HVAC building #7 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 20 21 22 23 24 25 26 ET* (degC) Appendix A page 216 MRL Australia ASHRAE RP-884 Final Report Townsville Australia RP-884 Townsville Australia RP-884 Townsville Australia RP-884 (summer-dry), HVAC building #8 (summer-dry), HVAC building #9 (summer-dry), HVAC building #10 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote -2 ashrae -2 pmv -2 pmv pmv -3 -3 -3 20 21 22 23 24 25 26 20 21 22 23 24 25 26 20 21 22 23 24 25 26 ET* (degC) ET* (degC) ET* (degC) Townsville Australia RP-884 Townsville Australia RP-884 Townsville Australia RP-702 (summer-dry), HVAC building #11 (summer-dry), HVAC building #12 (summer-dry), HVAC building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 ASH -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 -3 -3 -3 20 21 22 23 24 25 26 20 21 22 23 24 25 26 -3 -2 -1 0 1 2 3 ET* (degC) ET* (degC) PMV Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #2 (summer-dry), HVAC building #3 (summer-dry), HVAC building #4 3 3 2 2 3 1 1 2 1 0 0 0 ASH ASH -1 ASH -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #5 (summer-dry), HVAC building #6 (summer-dry), HVAC building #7 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #8 (summer-dry), HVAC building #9 (summer-dry), HVAC building #10 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 ASH -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Townsville Australia RP-702 (summer-dry), HVAC building #11 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 217 MRL Australia ASHRAE RP-884 Final Report Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #12 (summer-dry), HVAC building #1 (summer-dry), HVAC building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote Mean Vote -2 -2 ashrae -2 ashrae pmv pmv -3 -3 -3 -3 -2 -1 0 1 2 3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 PMV SET (degC) SET (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #3 (summer-dry), HVAC building #4 (summer-dry), HVAC building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #6 (summer-dry), HVAC building #7 (summer-dry), HVAC building #8 3 3 3 ashrae ashrae 2 2 pmv 2 pmv 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote -2 pmv -2 -2 -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #9 (summer-dry), HVAC building #10 (summer-dry), HVAC building #11 3 3 3 ashrae 2 2 pmv 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote -2 ashrae -2 pmv -2 pmv -3 -3 -3 20 22 24 26 28 30 32 20 22 24 26 28 30 32 20 22 24 26 28 30 32 SET (degC) SET (degC) SET (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #12 (summer-wet), HVAC building #1 (summer-wet), HVAC building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote -2 ashrae Mean Vote ashrae -2 pmv pmv -2 pmv -3 -3 -3 20 22 24 26 28 30 32 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 SET (degC) Operative Temp (degC) Operative Temp (degC) Townsville Australia RP-702 (summer-wet), HVAC building #4 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 21 22 23 24 25 26 27 28 Operative Temp (degC) Appendix A page 218 MRL Australia ASHRAE RP-884 Final Report Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #5 (summer-wet), HVAC building #6 (summer-wet), HVAC building #7 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote -2 ashrae Mean Vote -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #8 (summer-wet), HVAC building #9 (summer-wet), HVAC building #10 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #11 (summer-wet), HVAC building #12 (summer-wet), HVAC building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote -2 ashrae -2 ashrae Mean Vote -2 ashrae pmv pmv pmv -3 -3 -3 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 Operative Temp (degC) Operative Temp (degC) ET* (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #3 (summer-wet), HVAC building #4 (summer-wet), HVAC building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote -2 ashrae Mean Vote -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 ET* (degC) ET* (degC) ET* (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #6 (summer-wet), HVAC building #7 (summer-wet), HVAC building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 ET* (degC) ET* (degC) ET* (degC) Townsville Australia RP-702 (summer-wet), HVAC building #9 3 2 1 0 -1 Mean Vote -2 ashrae pmv -3 21 22 23 24 25 26 27 28 ET* (degC) Appendix A page 219 MRL Australia ASHRAE RP-884 Final Report Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #10 (summer-wet), HVAC building #11 (summer-wet), HVAC building #12 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 21 22 23 24 25 26 27 28 ET* (degC) ET* (degC) ET* (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #1 (summer-wet), HVAC building #3 (summer-wet), HVAC building #4 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #5 (summer-wet), HVAC building #6 (summer-wet), HVAC building #7 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH ASH -1 -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #8 (summer-wet), HVAC building #9 (summer-wet), HVAC building #10 3 3 2 3 2 2 1 1 1 0 0 0 ASH ASH -1 ASH -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #11 (summer-wet), HVAC building #12 (summer-wet), HVAC building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 ASH -1 -1 Mean Vote ashrae -2 -2 -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 21 22 23 24 25 26 27 28 29 30 PMV PMV SET (degC) Townsville Australia RP-702 (summer-wet), HVAC building #3 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 21 22 23 24 25 26 27 28 29 30 SET (degC) Appendix A page 220 MRL Australia ASHRAE RP-884 Final Report Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #4 (summer-wet), HVAC building #5 (summer-wet), HVAC building #6 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 28 29 30 21 22 23 24 25 26 27 28 29 30 21 22 23 24 25 26 27 28 29 30 SET (degC) SET (degC) SET (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #7 (summer-wet), HVAC building #8 (summer-wet), HVAC building #9 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 28 29 30 21 22 23 24 25 26 27 28 29 30 21 22 23 24 25 26 27 28 29 30 SET (degC) SET (degC) SET (degC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #10 (summer-wet), HVAC building #11 (summer-wet), HVAC building #12 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21 22 23 24 25 26 27 28 29 30 21 22 23 24 25 26 27 28 29 30 21 22 23 24 25 26 27 28 29 30 SET (degC) SET (degC) SET (degC) Merseyside UK (summer), NV Merseyside UK (summer), NV Merseyside UK (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 16 17 18 19 20 21 22 23 24 25 26 16 17 18 19 20 21 22 23 24 25 26 16 17 18 19 20 21 22 23 24 25 26 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Merseyside UK (summer), NV Merseyside UK (summer), NV Merseyside UK (summer), NV building #1 building #2 building #3 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 16 18 20 22 24 26 16 18 20 22 24 26 16 18 20 22 24 26 ET* (degC) ET* (degC) ET* (degC) no ash versus pmv graph for building 1, Merseyside UK. Appendix A page 221 MRL Australia ASHRAE RP-884 Final Report Merseyside UK (summer), NV Merseyside UK (summer), NV Merseyside UK (summer), NV building #2 building #3 building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 -2 -2 Mean Vote -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 18 19 20 21 22 23 24 25 26 27 PMV PMV SET (degC) Merseyside UK (summer), NV Merseyside UK (summer), NV Merseyside UK (winter), NV building #2 building #3 building #3 3 3 3 ashrae 2 2 pmv 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae -2 pmv -2 -2 pmv -3 -3 -3 18 19 20 21 22 23 24 25 26 27 18 19 20 21 22 23 24 25 26 27 18 19 20 21 22 23 24 25 26 SET (degC) SET (degC) Operative Temp (degC) Merseyside UK (winter), NV Merseyside UK (winter), NV Merseyside UK (winter), NV building #5 building #6 building #7 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae Mean Vote ashrae ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18 19 20 21 22 23 24 25 26 18 19 20 21 22 23 24 25 26 18 19 20 21 22 23 24 25 26 Operative Temp (degC) Operative Temp (degC) Operative Temp (degC) Merseyside UK (winter), NV Merseyside UK (winter), NV Merseyside UK (winter), NV building #8 building #3 building #5 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote -2 ashrae -2 ashrae -2 ashrae pmv pmv pmv -3 -3 -3 18 19 20 21 22 23 24 25 26 18 20 22 24 26 18 20 22 24 26 Operative Temp (degC) ET* (degC) ET* (degC) Merseyside UK (winter), NV Merseyside UK (winter), NV Merseyside UK (winter), NV building #6 building #7 building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18 20 22 24 26 18 20 22 24 26 18 20 22 24 26 ET* (degC) ET* (degC) ET* (degC) Merseyside UK (winter), NV building #3 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 222 MRL Australia ASHRAE RP-884 Final Report Merseyside UK (winter), NV Merseyside UK (winter), NV Merseyside UK (winter), NV building #5 building #6 building #7 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 ASH -1 -1 -2 -2 -2 -3 -3 -3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 PMV PMV PMV Merseyside UK (winter), NV Merseyside UK (winter), NV Merseyside UK (winter), NV building #8 building #3 building #5 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 -2 pmv -2 pmv -3 -3 -3 -3 -2 -1 0 1 2 3 18 19 20 21 22 23 24 25 26 27 18 19 20 21 22 23 24 25 26 27 PMV SET (degC) SET (degC) Merseyside UK (winter), NV Merseyside UK (winter), NV Merseyside UK (winter), NV building #6 building #7 building #8 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 18 19 20 21 22 23 24 25 26 27 18 19 20 21 22 23 24 25 26 27 18 19 20 21 22 23 24 25 26 27 SET (degC) SET (degC) SET (degC) Merseyside UK (winter), Mixed Merseyside UK (winter), Mixed Merseyside UK (winter), Mixed building #4 building #4 building #4 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 -3 -3 -3 18.5 20.5 22.5 24.5 26.5 18 20 22 24 26 -3 -2 -1 0 1 2 3 Operative Temp (degC) ET* (degC) PMV Merseyside UK (winter), Mixed Singapore (summer), HVAC Singapore (summer), HVAC building 4 building #1 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote ashrae -2 ashrae Mean Vote ashrae -2 pmv pmv -2 pmv -3 -3 -3 19 20 21 22 23 24 25 26 27 19 21 23 25 27 29 31 33 35 19 21 23 25 27 29 31 33 35 SET (degC) Operative Temp (degC) ET* (degC) Singapore (summer), HVAC building #1 3 2 1 0 ASH -1 -2 -3 -3 -2 -1 0 1 2 3 PMV Appendix A page 223 MRL Australia ASHRAE RP-884 Final Report Singapore (summer), HVAC Singapore (summer), NV building #2 Singapore (summer), NV building #2 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote Mean Vote Mean Vote ashrae -2 ashrae -2 ashrae -2 pmv pmv pmv -3 -3 -3 17 19 21 23 25 27 29 31 33 35 37 26 27 28 29 30 31 32 27 29 31 33 35 37 SET (degC) Operative Temp (degC) ET* (degC) Singapore (summer), NV building #2 Singapore (summer), NV building #2 Grand Rapids Michigan US (winter), 3 3 HVAC building #1 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote Mean Vote -2 -2 ashrae -2 ashra pmv e -3 -3 -3 pmv -3 -2 -1 0 1 2 3 23 25 27 29 31 33 35 37 22.5 23.5 24.5 PMV SET (degC) Operative Temp (degC) Grand Rapids Michigan US (winter), Grand Rapids Michigan US (winter), Grand Rapids Michigan US (winter), HVAC building #1 HVAC building #1 HVAC building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 ASH -1 -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 -2 pmv -3 -3 -3 22 23 24 -3 -2 -1 0 1 2 3 23 24 25 26 27 28 ET* (degC) PMV SET (degC) San Ramon CA US (summer), HVAC San Ramon CA US (summer), HVAC San Ramon CA US (summer), HVAC building #3 building #3 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 ASH -1 Mean Vote -2 ashrae Mean Vote ashrae pmv -2 pmv -2 -3 -3 -3 21 22 23 24 21 22 23 24 -3 -2 -1 0 1 2 3 Operative Temp (degC) ET* (degC) PMV San Ramon CA US (summer), HVAC San Ramon CA US (winter), HVAC San Ramon CA US (winter), HVAC building #3 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 21.5 22.5 23.5 24.5 25.5 26.5 27.5 19 20 21 22 23 24 19 20 21 22 23 24 SET (degC) Operative Temp (degC) Operative Temp (degC) San Ramon CA US (winter), HVAC building #1 3 2 1 0 -1 Mean Vote ashrae -2 pmv -3 19 20 21 22 23 24 ET* (degC) Appendix A page 224 MRL Australia ASHRAE RP-884 Final Report San Ramon US (winter), HVAC building San Ramon CA US (winter), HVAC San Ramon CA US (winter), HVAC #2 building #1 building #2 3 3 3 2 2 2 1 1 1 0 0 0 ASH ASH -1 -1 -1 Mean Vote ashrae -2 pmv -2 -2 -3 -3 -3 19 20 21 22 23 24 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 ET* (degC) PMV PMV San Ramon CA US (winter), HVAC San Ramon CA US (winter), HVAC Auburn CA US (winter), HVAC building #1 building #2 building #1 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 Mean Vote ashrae Mean Vote ashrae Mean Vote ashrae -2 pmv -2 pmv -2 pmv -3 -3 -3 19.5 21.5 23.5 25.5 27.5 19.5 21.5 23.5 25.5 27.5 20 21 22 23 24 SET (degC) SET (degC) Operative Temp (degC) Auburn CA US (winter), HVAC Auburn CA US (winter), HVAC Auburn CA US (winter), HVAC building #1 building #1 building #1 3 3 3 2 2 2 1 1 1 0 0 0 ASH -1 -1 -1 Mean Vote ashrae Mean Vote ashrae -2 pmv -2 -2 pmv -3 -3 -3 20 21 22 23 24 -3 -2 -1 0 1 2 3 19 21 23 25 27 29 31 ET* (degC) PMV SET (degC) Appendix A page 225 MRL Australia ASHRAE RP-884 Final Report Appendix A page 226 MRL Australia ASHRAE RP-884 Final Report APPENDIX B - PREFERRED TEMPERATURE FOR EACH BUILDING IN THE RP-884 DATABASE Appendix B page 227 MRL Australia ASHRAE RP-884 Final Report San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #2 (summer), HVAC building #3 (summer), HVAC building #4 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), HVAC building #7 (summer), HVAC building #8 (summer), HVAC building #9 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 San Francisco Bay Area RP-462 (summer), NV building #1 (summer), NV building #5 (summer), NV building #6 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o o indoor operative temperature ( C) indoor operative temperature ( C) indoor operative temperature ( C) San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), NV building #1 NV building #5 NV building #6 100 100 100 80 80 80 60 60 60 40 40 40 change (%) change (%) 20 change (%) 20 20 percentage wanting percentage wanting 0 percentage wanting 0 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature ( C) San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 HVAC building #2 HVAC building #3 (winter), HVAC building #4 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting percentage wanting 0 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 (winter), San Francisco Bay Area RP-462 HVAC building #7 HVAC building #8 (winter), HVAC building #9 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o indoor operative temperature (oC) indoor operative temperature ( C) indoor operative temperature ( C) Appendix B page 228 MRL Australia ASHRAE RP-884 Final Report San Francisco Bay Area RP-462 (winter), Antioch CA US (winter), HVAC Auburn CA US (winter), HVAC HVAC building #10 building #1 building #1 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature (oC) indoor operative temperature ( C) indoor operative temperature (oC) Bangkok Thailand (summer), Bangkok Thailand (summer), Bangkok Thailand (summer), HVAC building #1 HVAC building #2 NV building #3 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Bangkok Thailand (summer), Brisbane Australia (summer), Brisbane Australia (summer), NV building #4 HVAC building #1 HVAC building #2 100 100 100 80 80 80 60 60 60 40 40 40 change (%) change (%) change (%) 20 20 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o o indoor operative temperature ( C) indoor operative temperature ( C) indoor operative temperature ( C) Brisbane Australia (summer), Brisbane Australia (summer), Brisbane Australia (summer), HVAC building #3 HVAC building #4 HVAC building #5 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o indoor operative temperature (oC) indoor operative temperature ( C) indoor operative temperature ( C) Brisbane Australia (summer), Brisbane Australia (summer), Brisbane Australia (summer), NV building #1 NV building #2 NV building #3 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting percentage wanting 0 percentage wanting 0 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) Brisbane Australia (summer), Brisbane Australia (summer), Darwin Australia (summer-dry), NV building #4 NV building #5 HVAC building #2 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) Appendix B page 229 MRL Australia ASHRAE RP-884 Final Report Darwin Australia (summer-dry), Darwin Australia (summer-dry), Darwin Australia (summer-dry), HVAC building #3 HVAC building #4 HVAC building #5 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o indoor operative temperature ( C) indoor operative temperature ( C) indoor operative temperature (oC) Darwin Australia (summer-dry), Darwin Australia (summer-dry), Darwin Australia (summer-dry), HVAC building #6 HVAC building #7 HVAC building #8 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Darwin Australia (summer-wet), Darwin Australia (summer-wet), Darwin Australia (summer-wet), HVAC building #8 HVAC building #9 HVAC building #11 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o indoor operative temperature ( C) indoor operative temperature ( C) indoor operative temperature (oC) Darwin Australia (summer-wet), Darwin Australia (summer-wet), Darwin Australia (summer-wet), HVAC building #12 HVAC building #13 HVAC building #14 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Melbourne Australia (summer), Melbourne Australia (summer), Melbourne Australia (summer), HVAC building #1 HVAC building #2 HVAC building #3 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o indoor operative temperature (oC) indoor operative temperature ( C) indoor operative temperature ( C) Melbourne Australia (summer), Melbourne Australia (summer), Melbourne Australia (summer), HVAC building #4 NV building #1 NV building #2 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Appendix B page 230 MRL Australia ASHRAE RP-884 Final Report Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #1 (summer-dry), HVAC building #2 (summer-dry), HVAC building #3 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #4 (summer-dry), HVAC building #5 (summer-dry), HVAC building #7 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #8 (summer-dry), HVAC building #10 (summer-dry), HVAC building #11 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature ( C) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-dry), HVAC building #12 (summer-wet), HVAC building #1 (summer-wet), HVAC building #3 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature ( C) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-702 (summer-wet), HVAC building #4 (summer-wet), HVAC building #5 (summer-wet), HVAC building #6 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o indoor operative temperature ( C) indoor operative temperature ( C) indoor operative temperature (oC) Townsville Australia RP-702 Townsville Australia RP-702 Townsville Australia RP-884 (summer-wet), HVAC building #7 (summer-wet), HVAC building #8 (summer-wet), HVAC building #9 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Appendix B page 231 MRL Australia ASHRAE RP-884 Final Report Townsville Australia RP-702 Townsville Australia RP-702 Karachi Pakistan (winter), NV (summer-wet), HVAC building #10 (summer-wet), HVAC building #12 building #1 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) Multan Pakistan (summer), NV Peshawar Pakistan (summer), NV Peshawar Pakistan (winter), building #2 building #3 NV building #3 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Quetta Pakistan (summer), NV Quetta Pakistan (winter), NV Saidu Sharif Pakistan (summer), building #4 building #4 NV building #5 100 100 100 80 80 80 60 60 60 40 40 40 change (%) change (%) 20 change (%) 20 20 percentage wanting percentage wanting 0 percentage wanting 0 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature ( C) Athens Greece (summer), NV Athens Greece (summer), NV Athens Greece (summer), NV building #1 building #3 building #4 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) Athens Greece (summer), NV Athens Greece (summer), NV Oxford UK (summer), NV building #1 building #5 building #6 100 100 100 80 80 80 60 60 60 40 40 40 change (%) change (%) 20 change (%) 20 20 percentage wanting percentage wanting 0 percentage wanting 0 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) Oxford UK (summer), NV building #2 Oxford UK (summer), NV building #3 San Ramon CA US (summer), 100 100 HVAC building #3 100 80 80 80 60 60 60 40 40 40 change (%) change (%) 20 20 change (%) 20 percentage wanting percentage wanting 0 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature (oC) indoor operative temperature ( C) indoor operative temperature (oC) Appendix B page 232 MRL Australia ASHRAE RP-884 Final Report San Ramon CA US (winter), San Ramon CA US (winter), Grand Rapids Michigan (winter), HVAC building #1 HVAC building #2 HVAC building #1 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature (oC) indoor operative temperature ( C) indoor operative temperature (oC) Montreal Canada RP-821 Montreal Canada RP-821 Montreal Canada RP-821 (summer), HVAC building #1 (summer), HVAC building #2 (summer), HVAC building #3 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature (oC) indoor operative temperature ( C) indoor operative temperature (oC) Montreal Canada RP-821 Montreal Canada RP-821 Montreal Canada RP-821 (summer), HVAC building #5 (summer), HVAC building #6 (summer), HVAC building #7 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) Montreal Canada RP-821 Montreal Canada RP-821 Montreal Canada RP-821 (summer), HVAC building #9 (summer), HVAC building #10 (summer), HVAC building #11 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Montreal Canada RP-821 Montreal Canada RP-821 Montreal Canada RP-821 (summer), HVAC building #12 (winter), HVAC building #1 (winter), HVAC building #2 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Montreal Canada RP-821 Montreal Canada RP-821 Montreal Canada RP-821 (winter), HVAC building #3 (winter), HVAC building #5 (winter), HVAC building #6 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 o indoor operative temperature ( C) indoor operative temperature ( oC) indoor operative temperature (oC) Appendix B page 233 MRL Australia ASHRAE RP-884 Final Report Montreal Canada RP-821 Montreal Canada RP-821 Montreal Canada RP-821 (winter), HVAC building #7 (winter), HVAC building #8 (winter), HVAC building #9 100 100 100 80 80 80 60 60 60 40 40 40 change (%) 20 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Montreal Canada RP-821 South Wales UK (summer), Sydney Australia (summer), (winter), HVAC building #10 HVAC building #4 Mixed building #1 100 100 100 80 80 80 60 60 60 40 40 40 change (%) change (%) change (%) 20 20 20 percentage wanting 0 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) indoor operative temperature (oC) Sydney Australia (winter), Sydney Australia (winter), Mixed building #1 HVAC building #2 100 100 80 80 60 60 40 40 change (%) 20 change (%) 20 percentage wanting 0 percentage wanting 0 16 18 20 22 24 26 28 30 32 34 36 16 18 20 22 24 26 28 30 32 34 36 indoor operative temperature (oC) indoor operative temperature ( oC) Appendix B page 234 MRL Australia ASHRAE RP-884 Final Report APPENDIX C - SUMMARY OF THE ORIGINAL FIELD EXPERIMENTS COMPRISING THE ASHRAE RP-884 DATABASE Appendix C page 235 MRL Australia ASHRAE RP-884 Final Report C.1. Project Title - ASHRAE TC 2.1 sponsored RP-702 Project filenames in the RP-884 database This project is disseminated as file numbers 36 (summer “dry” - HVAC) and 37 (summer “wet” - HVAC) in the RP-884 database. Project researchers and class of investigation Richard de Dear (Macquarie University, Sydney Australia) and Marc Fountain (University of California at Berkeley, USA). A CLASS-1 field experiment sponsored by ASHRAE TC 2.1. Project publications de Dear, R.J. and M.E. Fountain (1994) "Field experiments on occupant comfort and office thermal environments in a hot-humid climate," ASHRAE Transactions, Vol.100(2), pp.457- 475. de Dear, R.J. and M.E. Fountain (1994) Cover feature -- "Thermal comfort in air-conditioned office buildings in the tropics," Journal of the Australian Institute of Refrigerating, Air- Conditioning and Heating, Vol.48(9), pp.14-30. de Dear, R.J., M.E. Fountain, S. Popovic, S. Watkins, G. Brager, E.Arens and C Benton (1993) A Field Study of Occupant Comfort and Office Thermal Environments in a Hot-Humid Climate : Final Report on ASHRAE RP-702. (MRL: Sydney), 162 pp. Project location, climate and season The project was located in Townsville on the north-eastern coast of Australia which falls within a Tropical Savanna climate zone (wet-dry tropics). One field experiment conducted in the “Dry” season (warm-dry “summer”), another experiment conducted in the “wet” season (hot-wet “summer”). Appendix C page 236 MRL Australia ASHRAE RP-884 Final Report Sample buildings Twelve buildings, all offices, were studied. Building Sample Climate Floor Occupancy Pattern Code Size Controls Area (blcode) (n) (bldgtype) 1 56 VAV 2,010m2 Federal Government 3 storeys, mainly open department. plan. 2 22 Central AC 3,944 m2 Tertiary education (CAV) 4 levels, private and administration. mult-occupant.offices. 3 61 VAV 17,820 m2 multi-tenant office 12 storeys mainly open tower. plan. 4 22 CAV 4,865 m2 Government departments Twin tower design, mainly open plan. 5 45 CAV 1,860 m2 Single tenant - power 5 storeys, mainly open utility administration. plan. 6 38 CAV 4,632 m2 Regional bank 8 storeys, mainly open headquarters. plan. 7 100 CAV 4,851 m2 Local government offices. 3 storeys, mainly open plan. 8 105 CAV 7,780 m2 State government offices. 5 storeys, mainly open plan. 9 14 CAV 1,727 m2 State government offices. 3 storeys, mainly open plan. 10 63 VAV 2,076 m2 Office building. 3 storeys, mainly open plan. 11 19 VAV 3,942 m2 Insurance and legal firms. 6 storeys, mainly open plan. 12 82 VAV 22,910 m2 Government department. 13 storeys, mainly open plan. Instruments Class-1 instrumentation includes three heights above floor level. Anemometry was measured by DANTEC 54R10 omnidirectional heated elements with fast time-constant for turbulence intensity calculations. Air temperature was measured by YSI series 700 probes (thermistors) and globe temperatures measured by fixing a table tennis ball (40mm diam.) over the sensor with appropriate steps taken to achieve correct emissivity. Dewpoint temperature (humidity) measured by a General Eastern DEW-10 chilled-mirror transducer. Appendix C page 237 MRL Australia ASHRAE RP-884 Final Report Radiant asymmetry was measured by a Bruel and Kjaer plane radiant asymmetry sensor (MM 0036). Questionnaire The questionnaire was divided into two parts, background and on-line surveys. The background questionnaire covered demographics, contextual and psychological factors. The on-line questionnaire covered the subjects assessment of their immediate thermal environment, such as their thermal sensation on a 7-point scale, acceptability as a yes/no response, thermal preference on a 3-point scale, current garment insulation assessed by tables and algorithms in ASHRAE Standard 55-1992 and metabolic activity assessed by ASHRAE Standard 55-92 and ISO 7730. Metabolic activity was recorded at four distinct time periods, from which an overall metabolic rate was established. The on-line questionnaire was conducted at the same time as physical measurements were being made of the subjects environment. Outdoor meteorological data Concurrent three-hourly observations from Townsville Airport (purchased from Australian Bureau of Meteorology), from which air temperature and relative humidity at 600 hours and 1500 hours was extracted for RP-884 purposes. RP-884 standardization assumptions The design of the database structure and coding conventions throughout the ASHRAE Adaptive Model Project (RP-884) was based on de Dear and Fountains’ (1994) Townsville (RP-702) project. Appendix C page 238 MRL Australia ASHRAE RP-884 Final Report C.2. Project Title - Thermal comfort studies in modern industrial buildings. Project file names in the RP-884 database This project is disseminated as file numbers 1 (summer - HVAC) and 2 (winter - HVAC) in the RP-884 database. Project researchers and class of investigation Jill C. Brown (Ph.D thesis, University of Wales, Cardiff). This is a CLASS-2 field experiment. Project publications Brown, J. C. (1995). Thermal Comfort Studies in Modern Industrial Buildings, Ph.D. Thesis, University of Wales, Cardiff. Brown, J. C. and Jones, P. J. (1993). Thermal Comfort in Modern Industrial Buildings, Clima 2000 Conference, London, Organised by the Chartered Institute of Building Services Engineers. Project location, climate and season This project was conducted in South Wales, UK. More precisely, in Cwmbran, Gwent; Newport, Gwent; Ebbw Vale, Gwent; Maesteg, Mid Glamorgan; Cwmfelinfach, Gwent; Gwent; Llanelli, Dyfed and Llantrisant, Glamorgan. Summer and Winter seasons investigated. Climatically, this region can be classified as west coast marine. Instruments Indoor climatic instrumentation included: pre-calibrated thermistors to measure air temperature, hot-wire anemometer for air speed, solid-state hygrometer to measure humidity, and a thermistor inside a 38mm diameter ping-pong ball to measure globe temperature. Air temperature was measured at ankle, waist and head heights (0.3m, 1.5m and 2m) while all other parameters were only measured at waist height. Questionnaire The questionnaire addressed both conditions at the time of physical measurements and typical/overall conditions, of which only the former was used for RP-884’s purposes. Appendix C page 239 MRL Australia ASHRAE RP-884 Final Report Sensation was rated on the ASHRAE 7-pt scale. The questionnaire assessed thermal preference but not thermal acceptability. Metabolic ratings were established at the time of the questionnaire and prior to questionnaire, using the ASHRAE 55-92 standard for guidance. However, the author expressed reservations that this checklist did not fully describe the types of activities being performed within the study. Clo was estimated using the ASHRAE 55-92 and ISO/DIS 9920-91 checklist and if clothing insulation data was absent then an estimation was made using the garment weight relationship suggested by McCullough et al., 1984). Sample buildings Location Building Sample Climate Floor Occupancy Code Size Controls Area Pattern (blcode) (n) (bldgtype) and season Cwmbran, 1 16 - winter HVAC 576m2 Light Industrial Gwent Factory Newport, Gwent 2 17 - summer HVAC 3000m2 Med-heavy Industrial Factory Ebbw Vale, 3 15 - summer HVAC 1000m2 Light-med Gwent Industrial Factory Maesteg, 4 32 - summer HVAC c. Light Glamorgan 1500m2 Industrial Factory Llanelli, Dyfed 5 6 - winter HVAC c. Light 850m2 Industrial Factory Llantrisant, mid - 6 9 - winter HVAC c. Light Glamorgan 2500m2 Industrial Factory Llantrisant, mid - 7 7 - winter HVAC c. Med-heavy Glamorgan 2500m2 Industrial Factory Cwmfelinfach, 8 16 - summer HVAC c. Med-heavy Gwent 6700m2 Industrial Factory Outdoor meteorological data In the absence of accessible outdoor meteorological observations at the same time as the questionnaire data, RP-884 researchers substituted climatological data at 600 hrs and 1500 hrs. This data was retrieved from two sources -- air temperature from the journal Weather (using the UK Met Office site of Roose), and humidity (by derivation of dew point using the Appendix C page 240 MRL Australia ASHRAE RP-884 Final Report UK Met Office site for Cardiff) based on data entries the International Station Meteorological and Climate Summary (ISMCS 1992) CDROM. RP-884 standardization assumptions Instrumentation in the original data set took measurements at heights 2m, 1.5m and 0.3m. We mapped 0.3m to 0.1m and 1.5m to 1.1m for the RP-884 database. Clo was estimated with the ASHRAE 55-92 checklist so no corrections were needed, but the activity variable in the original data set had to be used to determine whether or not the subject was seated and so whether 0.15 clo for the insulation due to a chair needed to be subtracted. This provided two variables within the RP-884 database, clothing insulation with and without the effects of a chair. The research design was cross-sectional which satisfied the assumptions for RP- 884, that all subjects were independent. Appendix C page 241 MRL Australia ASHRAE RP-884 Final Report C.3. Project Title - Doctoral dissertation. From comfort to kilowatts: an integrated assessment of electricity conservation in Thailand’s commercial sector. Project file names in the RP-884 database This project is disseminated as file numbers 3 (summer - HVAC) and 4 (summer - NV) in the RP-884 database. Project researchers and class of investigation John F. Busch, Jr (Lawrence Berkeley Lab. Berkeley California, USA). This is a CLASS-2 field experiment. Project publications Busch, 1990 “Thermal responses to the Thai office environment.” ASHRAE Trans., V. 96(1), pp. 859-872. Busch J. F. (1992) A tale of two populations: thermal comfort in air-conditioned and naturally ventilated offices in Thailand. Energy and Buildings Vol 18 pp 235-249. Busch J. (1995) Thermal comfort in Thai air-conditioned and naturally ventilated offices in Thailand Standards for thermal comfort pp 114-121. Busch J. F. (1990) From Comfort to Kilowatts - An Integrated Assessment of Electricity Conservation in Thailand’s Commercial Sector. (UC Berkeley PhD. Thesis). Project location, climate and season The project was located in Bangkok, Thailand (peninsular, Southeast Asia). Bangkok is the largest city in Thailand as well as being the capital. Being tropical, Bangkok does not display much seasonality and can been classified under a hot humid climate. The project was conducted in the hot season and the wet season. Appendix C page 242 MRL Australia ASHRAE RP-884 Final Report Sample buildings Building Code Sample Size Climate Controls Floor Occupancy Pattern (blcode) (n) (bldgtype) Area 1 380 HVAC Sathorn Thani (SH and SW) 2 389 HVAC Thai Farmers Bank (TH and TW) 3 194 NV Dept. Science Services (DH and DW) 4 173 NV Ministry of ST and E (MH and MW) 5 25 NV KMIT Instit. Tech (pilot) (PH and PW) Instruments The instrumentation was packaged into a “toolbox” which was placed in the subject’s occupied zone, typically at a height of 0.6m above floor, or on their desk. Air and globe temperatures were registered with calibrated thermistors. The globe thermometer was based on a 38mm ping pong ball. Air speeds were registered with a Kurz 403 “hot-film” anemometer in the vicinity of the subject. Humidity was recorded with a steady-state device. All sensors were connected to a Campbell Scientific CR21 datalogger which was dumped into a tape recorder at the end of every day in the field. Questionnaire Subjects who had been seated at their workstations for more than 15 minutes were eligible for inclusion in the sample. The questionnaire covered basic sensation and preference items. Metabolic and clothing scales/check-lists were based on the McIntyre (1980) tables. Outdoor meteorological data Outdoor meteorological data were collected by the original researcher from the Royal Thai Meteorological Department. Daily maxima and minima for temperature and humidity were extracted for the RP-884 database. RP-884 standardization assumptions For this study clo was estimated by the McIntrye (1980) method. Clo therefore required correction to the ASHRAE 55-92 Standard for RP-884 purposes. To this 0.15 clo was added to create a separate variable accounting for the clothing ensemble and insulation Appendix C page 243 MRL Australia ASHRAE RP-884 Final Report effects of a chair. The research design of this project was cross-sectional which satisfied the assumptions for RP-884, that all subjects were independent. Appendix C page 244 MRL Australia ASHRAE RP-884 Final Report C.4. Project Title - The CSAA, Antioch (1995) component of the Advanced Customer Technology Test (ACT2) project. Project researchers and class of investigation Charles C. Benton and Gail S. Brager (CEDR, Department of Architecture, University of California, Berkeley). This is a CLASS-1 field experiment. Project file names in the RP-884 database This project is disseminated as file number 5 in the RP-884 database. Project publications Benton, C. et al. Advanced Customer Technology Test (ACT2) CSAA Progress Report. (CEDR UC Berkeley). Brager, G et al. (1994) “A comparison of methods for assessing thermal sensation and acceptability in the field,” In Thermal Comfort: Past, Present and Future. (eds N. A. Oseland and M. A. Humphreys). Project location, climate and season The ACT2 project was based on the ASHRAE RP-702 project (the hot-humid field experiment in Townsville Australia, de Dear et al., 1994). Data was collected for the ACT2 project between 1991 and 1995 at four sites. The Sunset Building (baseline and post- retrofit) in San Ramon, Verifone (baseline) in Auburn and CSAA (post construction) in Antioch. Antioch has a Mediterranean climate, less than 50 km inland from the San Francisco Bay but separated from the water by the Berkeley Hills (nearest major city is Concord). The season of this study was winter. Sample buildings Building Code Sample Size Climate Controls Floor Occupancy (blcode) (n) (bldgtype) Area Pattern 1 111 HVAC office building Instruments A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m. The sensors chosen were selected to meet the response time and accuracy requirements of Appendix C page 245 MRL Australia ASHRAE RP-884 Final Report ASHRAE Standard 55-81 and ISO Standard 7730 for thermal assessment. YSI series 700 probes with vinyl-coated tips were used to measure air temperature. Globe temperature was measured by attaching a 38 mm diameter table tennis ball on the temperature sensors. The balls were painted grey for correct emissivity. Air velocity was measured by Dantec 54R10 anemometers, which are omnidirectional fully temperature-compensated sensors. Dewpoint temperature was measured by a General Eastern DEW-10 chilled mirror dewpoint transducer. All parameters were measured at all three heights except dewpoint temperature which was only measured at 0.6m. Radiant asymmetry and illuminance were also measured, but are not essential to the purpose of RP-884. Questionnaire The questionnaire consisted of an on-line questionnaire, which addressed conditions at the time physical measurements were being taken and a background questionnaire. The latter covered subject details such as, health and emotional characteristics, office description, work area and job satisfaction, environmental sensitivity, plus personal comfort, satisfaction and perceived control. In the on-line section thermal sensation was rated on the 7-pt ASHRAE scale. Thermal preference was assessed on a descriptive 3-pt scale. Thermal acceptability was not rated. Metabolic rate was estimated based on a checklist referring to the subjects activity in the 15 minutes before completing the on-line questionnaire, using tables in the ASHRAE Handbook of Fundamentals (HOF, 1985). Clo estimates were based on responses to the clothing item checklist provided in the on-line questionnaire from the ASHRAE Standard 55-81 method. Outdoor meteorological data Meteorological air temperature data at 600 hrs and 1500 hrs were purchased by RP-884 from the National Oceanic and Atmospheric Administration’s National Climatic Data Center. Relative humidity, also at 600 hrs and 1500 hrs was extracted from the International Station Meteorological and Climate Summary CD-ROM (ISMCS, 1992) for the nearest site. RP-884 standardization assumptions The detailed methods and protocol used in ASHRAE RP-462 (and extended to the ASHRAE RP-702 project described above) were carried out in full for the ACT2 Project. Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462, little Appendix C page 246 MRL Australia ASHRAE RP-884 Final Report standardisation was necessary. However, clothing was based on the ASHRAE 55-81 method, and so required conversion into equivalent ASHRAE 55-92 values. 0.15 clo was then added for chair insulation. The research design of this field experiment was longitudinal, so for the purposes of RP-884, independence between subjects was assumed. Appendix C page 247 MRL Australia ASHRAE RP-884 Final Report C.5. Project Title - Higher PMV causes higher energy consumption in air- conditioned buildings: A case study in Jakarta, Indonesia. Project researchers and class of investigation Tri H. Karyono (University of Sheffield, UK). This is a CLASS-3 field experiment. Project file names in the RP-884 database This project is disseminated as file numbers 6 (summer - HVAC bdgs), 7 (summer - NV) and 8 (summer - mixed mode buildings) in the RP-884 database. Project publications Karyono, T. H. (1995) “Higher PMV causes higher energy consumption in air-conditioned buildings: A case study in Jakarta, Indonesia, “ Standards for thermal comfort. ed by Fergus Nicol, Michael Humphreys, Oliver Sykes and Susan Roaf. Chapman and Hall pp 219-226. Karyono, T. (1996) “Thermal comfort in the tropical southeast Asia region.” Architectural Science Review. V39(3), pp.135-139. Karyono, T.H (1996) “Discrepancy between actual and predicted thermal votes of Indonesian workers in Jakarta, Indonesia.” International Journal of Ambient Energy. V.17(2), pp.95-100. Project location, climate and season The project was located in Jakarta, Indonesia. This is in a wet equatorial climate zone with a season classified as “summer” all year round. Sample buildings Building Code Sample Size Climate Controls Floor Occupancy Pattern (blcode) (n) (bldgtype) Area 1 97 NV office building 2 103 HVAC office building 3 98 HVAC office building 4 96 HVAC office building 5 91 HVAC office building 6 41 Mixed (hybrid) office building 7 70 HVAC office building Appendix C page 248 MRL Australia ASHRAE RP-884 Final Report Instruments Bruel and Kjaer 1212 Thermal Comfort Meter. No anemometer used in this project. Relative humidity were measured with a solid state hygrometer. Questionnaire In Bahasa Indonesian. Outdoor meteorological data Outdoor climatological air temperature and relative humidity data at 600 hrs and 1500 hrs were obtained from the International Station Meteorological and Climate Summary (ISMCS, 1992) CD-ROM for Jakarta. RP-884 standardization assumptions The B+K 1212 instrument was used to measure operative and equivalent temperatures. As a result no radiant temperatures could be calculated (from globe temperature). Clothing estimates were based on the Bruel and Kjaer manual which closely corresponds to the ISO 7730 (1984) methods and which was mapped to the ASHRAE 55-92 standard for RP-884. Chair insulation estimates of 0.15 clo were also added to form a total insulation variable in the RP-884 database. The research design was cross-sectional which satisfied the assumptions for RP-884, that all subjects were independent. Appendix C page 249 MRL Australia ASHRAE RP-884 Final Report C.6. Project Title - Montreal ASHRAE RP-821. “Field Study of Occupant Comfort and Office Thermal Environments in a Cold Climate.” This is the third of a series of ASHRAE projects (following RP-462 in San Francisco and RP-702 in a hot-humid climate). Project researchers and class of investigation Giovanna Donnini, Jean Molina, Carlo Martello, Dorothy Ho Ching Lai, Kit Ho Lai, Ching Yu Chang, Michel Laflamme, Van Hiep Nguyen, Fariborz Haghighat (Auger, Donnini and Nguyen Inc.). This is a CLASS 1 field experiment in line with the preceding two ASHRAE- sponsored field experiments. Project file names in the RP-884 database This project is disseminated as file numbers 9 (summer - HVAC) and 10 (winter - HVAC) in the RP-884 database. Project publications Donnini, G. et al (1996) Field Study of Occupant Comfort and Office Thermal Environments in a Cold Climate: Final Report. ADN Inc., Montreal, Quebec, Canada. Project location, climate and season The cities chosen for the study are Montreal, Longueuil, Gramby, Cap-de-la-Madeleine, Shawinigan, Trois-Rivieres, Hull and Maniwaki in Canada. They are all located along the border of the Northern and Southeastern limits. The climatic classification is towards the cold extreme of the humid mid latitudes. Data were collected in both summer and winter seasons. Instruments Air temperatures were measured using Dantec 54R10 thermistors. Globe temperatures were measured using Bruel and Kjaer globe temperature sensors (MM 0030) each consisting of a Pt100 (platinum resistance) temperature sensing element situated in the centre of a 150mm diameter globe of appropriate emissivity. Air velocity and turbulence were measured by Dantec 54R10 anemometers, which are omnidirectional fully temperature-compensated sensors. The factory calibrated the sensors the week preceding Appendix C page 250 MRL Australia ASHRAE RP-884 Final Report the start of the site visits. Dew point temperature and vapour pressure was measured by a Bruel and Kjaer air humidity transducer (MM 0037). Air temperature, globe temperature, air velocity and turbulence were measured at three heights (ankle, waist and head height) and dew point temperature was measured only at waist height. Sample Buildings Building Sample Climate Floor Occupancy Pattern Code and Size Controls Area Location (n) (bldgtype) 1 summer 39 free cooling, 7220m2. 5 storey and Department of winter (37) VAV. sub-basement. Provincial Government Montreal Mainly open plan (offices). 2 39 double duct, 68198m2. 15 storeys Department of (38) CAV. and sub basements. Provincial Police Montreal Mainly open plan. (jail). 3 40 free cooling, 3963m2. 3 storeys. Department of Cap-de-la- (39) CAV. Mainly open plan. Provincial Police Madeleine (police station). 4 41 free cooling, 5265m2. 3 storeys. Department of (40) VAV and Mainly open plan. Provincial Government Shawinigan CAV. (court). 5 44 free cooling, 10,451m2. 5 storeys. Department of Trois- (44) VAV. Mainly open plan. Provincial Government Rivieres (offices). 6 41 free cooling, 14,980m2. 2 storeys. Department of (40) VAV. Mainly open plan. Provincial Government Longueuil (court). 7 40 double duct, 12,500m2. 8 storeys Department of (40) VAV and free and sub-basement. Provincial Government Longueuil cooling, CAV. Mainly open plan. (offices). 8 31 free cooling, 3500m2. 2 storeys and Department of (30) VAV sub-basement. Mainly Provincial Government Maniwaki open plan. (offices). 9 40 double duct, 8784m2. 3 storeys and Department of (39) VAV. sub-basement. Mainly Provincial Government Gramby open plan. (court). 10 40 free cooling, 3006m2. 4 storeys. Department of (39) VAV. Mainly open plan. Provincial Government Montreal (offices). 11 42 double duct, 32,345m2. 10 storeys. Department of (40) VAV and Mainly open plan. Provincial Government Hull CAV. (court). 12 6 double duct, 37,325m2. 25 storeys. Private, professional (none) CAV and free Mainly open plan. and advertising offices. Montreal cooling, VAV. Questionnaire The questionnaire used here was essentially the same as the one used in Townsville (ASHRAE RP-702 Hot Humid Field Experiment). The subjective survey was divided into two parts, Background and Online. The Background questions covered areas such as Appendix C page 251 MRL Australia ASHRAE RP-884 Final Report demographics, contextual and psychological factors. The on-line questions were related to the subjects assessment of their immediate thermal environment at that point in time and was answered at the time the physical measurements were being taken. Sensation ratings were based on the ASHRAE 7-pt scale. Thermal acceptability was addressed as a yes/no response and thermal preference was assessed on a 3-pt scale. Metabolic rating was based on the ASHRAE 55-92 Standard and ISO 7730 Standard. Met was assessed over four distinct time periods from which an overall metabolic value was obtained. Clo was estimated using the ASHRAE Standard 55-92 checklist. Adaptive behaviour questions were also addressed regarding the subjects perceived control over their thermal environment. Outdoor meteorological data The meteorological data recorded in the original field experiment data included; hourly temperatures, wind speed and direction, relative humidity, daily precipitation, start and stop times of precipitation and general conditions. These recordings were purchased by the researchers from the closest met observation site to each building tested. For the purpose of RP-884 air temperatures and relative humidities at 600 hrs and 1500 hrs were extracted for use. RP-884 standardization assumptions Due to the use of a 150 mm diameter globe with slow response time for measuring globe temperature, there was uncertainty as to whether or not the instrument achieved thermal equilibrium within the exposure time. Therefore, all rows where |TAAV-TRAV| >= 2 K were deleted from the data set before analysis continued. Clo was estimated by the ASHRAE 55-92 Standard so no correction was necessary and allowances for the insulation due to a chair had been made. The research design was cross-sectional. Appendix C page 252 MRL Australia ASHRAE RP-884 Final Report C.7. Project Title - Richard de Dear’s PhD research project in Australia. Project researchers and class of investigation Dr Richard de Dear and Andris Auliciems (University of Queensland). This is a CLASS-2 investigation . Project file names in the RP-884 database This project is disseminated as file numbers 11 (Brisbane, summer - HVAC), 12 (Brisbane, summer - NV), 13 (Darwin, summer “dry” - HVAC), 14 (Darwin, summer “wet” - HVAC), 15 (Melbourne, summer - HVAC) and 16 (Melbourne, summer - NV) in the RP-884 database. Project publications de Dear, R. J. and A. Auliciems (1985) “Validation of the Predicted Mean Vote model of thermal comfort in six Australian field studies.” ASHRAE Trans., V. 91(2), pp. 452-468. de Dear, R. J. and A. Auliciems (1985). Thermal neutrality and acceptability in six Australian field studies, Clima 2000, Indoor Climate (P.O. Fanger, editor), Vol. 4:103-108. VVS Kongress-VVS Messe, Copenhagen. de Dear, R. J. (1985) Perceptual and adaptational bases for the management of indoor climate. (St Lucia Queensland: University of Queensland PhD thesis). de Dear, R.J. and A. Auliciems (1986). Air conditioning in Australia II: User attitudes. Arch. Science Review, vol. 31, pp. 19-27. Project location, climate and season This project was conducted in three major cities, located in three distinct climate zones across Australia. Samples from both HVAC and NV buildings were taken in Brisbane (humid subtropical climate) and Melbourne (west coast marine climate) during summer. Samples were also taken from HVAC buildings in Darwin (tropical savanna or wet/dry tropics) during the “dry” and “wet” seasons. Instruments Wet and dry bulb temperatures were recorded with an Assmann aspirated psychrometer. Globe temperatures were recorded using a Zeal mercury-in-glass thermometer Appendix C page 253 MRL Australia ASHRAE RP-884 Final Report (manufactured according to British Standard 2842/66) inserted in the centre of a 40mm ping pong ball painted matt black. Air speeds were measured at three heights within the occupied zone but only an average was recorded. The anemometers were Kurz 441M with manufacturer’s claimed accuracy being ±0.03 m s-1. Sample Buildings Location Building Sample Climate Floor Occupancy Pattern Code Size Controls Area (blcode) (n) (bldgtype) Brisbane 1 195 HVAC office building Brisbane 2 102 HVAC office building Brisbane 3 69 HVAC office building Brisbane 4 114 HVAC office building Brisbane 5 84 HVAC office building Brisbane 1 157 NV office building Brisbane 2 124 NV office building Brisbane 3 69 NV office building Brisbane 4 211 NV office building Brisbane 5 49 NV office building Darwin-dry 1 14 HVAC office building Darwin-dry 2 12 HVAC office building Darwin-dry 3 131 HVAC office building Darwin-dry 4 82 HVAC office building Darwin-dry 5 97 HVAC office building Darwin-dry 6 52 HVAC office building Darwin-dry 7 53 HVAC office building Darwin-dry 8 50 HVAC office building Darwin-wet 8 48 HVAC office building Darwin-wet 9 85 HVAC office building Darwin-wet 10 100 HVAC office building Darwin-wet 11 58 HVAC office building Darwin-wet 12 157 HVAC office building Darwin-wet 13 58 HVAC office building Darwin-wet 14 49 HVAC office building Melbourne 1 83 HVAC office building Melbourne 2 243 HVAC office building Melbourne 3 102 HVAC office building Melbourne 4 84 HVAC office building Melbourne 1 126 NV office building Melbourne 2 411 NV office building Melbourne 3 16 NV office building Questionnaire Thermal sensation was assessed on the ASHRAE 7-point linear scale. Thermal preference was registered on a symmetrical 7-point scale (-3, -2, -1, 0, +1, +2, +3). Metabolic checklists were applied to the last 10 minutes, between 20 and 10 minutes ago, between 30 Appendix C page 254 MRL Australia ASHRAE RP-884 Final Report and 20 minutes ago and between 60 and 30 minutes ago. The average metabolic estimate across the last hour was recorded in the data file. Outdoor meteorological data Actual meteorological data (temperature and humidity) corresponding to the date stamped on each questionnaire were purchased from the Australian Bureau of Meteorology. RP-884 standardization assumptions The 7-point preference scale was converted to the McIntyre scale so that votes of -3, -2 and - 1 were “want cooler,” a vote of 0 was counted as “no change,” and votes of +1, +2 and +3 counted as “want warmer.” Clothing insulation was converted from the McIntyre 1980 method to the equivalent ASHRAE (1992) value and 0.15 clo was added for chair insulation to all cases with sedentary metabolic rates. The research design was cross-sectional which satisfied the assumptions for RP-884, that all subjects were independent. Appendix C page 255 MRL Australia ASHRAE RP-884 Final Report C.8. Project Title - A field study of thermal comfort using questionnaire software. Project researchers and class of investigation Guy R. Newsham, PhD. and Dale K. Tiller D.Phil. (National Research Council Canada). This is a CLASS-3 field experiment. Project file names in the RP-884 database This project is disseminated as file number 17 (winter - HVAC) in the RP-884 database. Project publications Newsham, G. R. and D. K. Tiller. (1995) A field study of Thermal Comfort using questionnaire software. IRC Internal Report. No 708. Newsham, G. R., D. K. Tiller. (1996) Questionnaire Software to Enable Study of Short-term Changes in Subjective Reactions to the indoor Environment. IRC Internal Report. Project location, climate and season Ottawa, Canada. The location is borderline between humid mid latitude and continental subarctic. The investigation was performed in winter. Sample buildings Building Sample Climate Floor Occupancy Pattern Code Size Controls Area and (blcode) (n) (bldgtype) layout 1 390 HVAC 3 storey, open Federal government. plan office on part Facilities design work. of 2nd floor. 2 437 HVAC 7 storey, open Federal government. Variety plan office on part of bibliographic tasks. of 1st floor. 3 988 HVAC 20 storey, mostly Federal government. Variety open plan on 7th of administrative, technical and part of 9th and scientific tasks. floor. 4 44 HVAC 3 storey, open Federal government. Variety plan office on part of bibliographic tasks. of 2nd floor. Instruments Appendix C page 256 MRL Australia ASHRAE RP-884 Final Report Indoor climatic instrumentation consisted of an ACR “SmartReader” thermistor for temperature and a solid-state hygrometer to measure humidity. Measurements were made at waist height only and the variables air speed and globe temperatures were not measured. Questionnaire The questionnaire was software based addressing 5 questions: environmental conditions at the time of physical data collection, sensation/comfort rating on a 7-pt scale, thermal preference, questions regarding adaptive behaviour and clo estimations. Total clothing ensemble worn by the subjects was estimated using the ASHRAE 55-92 checklist. Thermal acceptability and activity or any form of metabolic rating was not provided. Outdoor meteorological data Outdoor meteorological data including air temperature and humidity (RH%) was measured by the campus weather station. Three of the study sites were on the same campus as the station, the fourth was located 10km away. Meteorological data provided with the original dataset was that closest to the time when the questionnaire was being answered. From this information our dayta_15 and dayrh_15 variables were extracted. Also provided in the original data set was outdoor air temperature and humidity at 8:00am from which our dayta_06 and dayrh_06 variables were obtained. RP-884 standardization assumptions The research design for this study was longitudinal, but it was assumed for the purpose of RP-884 that all subjects were independent (i.e. assumed cross-sectional). Clo was estimated using the ASHRAE 55-92 Standard so no corrections were necessary. However clo was measured at the beginning of the day and so to more closely approximate the total clothing ensemble at the time of the questionnaire, the clo change variable in the original data set was used for adjustments. This variable specified at the time of the questionnaire wether the subject had had a major or minor clothing change (+ - 0.34 clo and + - 0.05 clo) since the morning. These adjustments were made and then 0.15 clo added for the insulation provided by a chair to give a total insulation as a separate variable. Age was given as the end point of a bin, but was replaced with the midpoint value. While metabolic rates were not recorded, a default value of 1.2 mets was temporarily inserted into the file for the purposes of index calculation, but then removed from the database. Appendix C page 257 MRL Australia ASHRAE RP-884 Final Report C.9. Project Title - “Thermal comfort in Pakistan.” This project was part of the 1993 Oxford Brookes University field project for The National Energy Agency Conservation Centre (ENERCON) agency of the Pakistan Government investigating the reduction of energy consumption in buildings and an adaptive model of thermal comfort. Project researchers and class of investigation Nicol, J. F., G. N. Jami, O. Sykes, S. Roaf, M. Humpherys and M. Hancook (School of Architecture, Oxford Brooks University). This is a CLASS-3 field experiment. Project file names in the RP-884 database This project is disseminated as file numbers 18 (Karachi, summer - NV), 19 (Karachi, winter - NV), 20 (Multan, summer - NV), 21 (Peshawar, summer - NV), 22 (Peshawar, winter - NV), 23 (Quetta, summer - NV), 24 (Quetta, winter - NV), 25 (Saidu, summer - NV) and 26 (Saidu, winter - NV) in the RP-884 database. Project Publications Nicol, J. F., G. N. Jami, O. Sykes, M. Humpherys, S. Roaf and M. Hancock. (1994) Thermal Comfort in Pakistan. Oxford Brookes University. Project location, climate and season This study was conducted across five cities in Pakistan including Karachi (Lower Indus Plain), Quetta (Baluchistan Plateau), Multan (southern Upper Indus Plain), Peshawar (northern Upper Indus Plain) and Saidu Sharif (northern mountains). Karachi is the capital of the Sindh province and the largest city in Pakistan in terms of population and size. Karachi is also a major Arabian Sea Port. Being only 4m above sea level warm moist air blows in from the Indian Ocean, however this does not often result in precipitation. Karachi is quite humid compared to the rest of the country and this is borne out by relatively small diurnal and annual temperature ranges (the monthly mean temperature varies just 11°C in Karachi and generally by 21°C to 25°C in other parts). Karachi has an average temperature maxima and minima of 33°C and 27°C respectively in July and 25°C Appendix C page 258 MRL Australia ASHRAE RP-884 Final Report and 13°C respectively in January. Karachi falls under a desert climate classification despite it’s location in a coastal zone. Multan is a major city on the southern Upper Indus Plain in the Punjab, surrounded by the desert region of Pakistan. However recent irrigation projects have resulted in microclimatic changes which have resulted in increases in rainfall with some associated changes in temperature and humidity. Historical records of temperature maxima and minima are 21°C and 6°C respectively in January and 42°C and 29°C respectively in June or 40°C and 29°C respectively in July. The climate zone for Multan is “desert.” Peshawar is the capital of the North/West Frontier Province and is at the northern end of the Upper indus plain at an elevation of 359m. The temperatures in Peshawar are fairly similar to those of Multan. The average maxima and minima are 17°C and 4°C respectively in January and in June 41°C and 25°C respectively or 40°C and 26°C respectively in July. The climate zone for Peshawar is semi desert. Quetta is the capital city of the Baluchistan province and is situated on the north-western Afghanistan boarder of Pakistan. The city is located at an altitude of 1692m on a dry desert plateau surrounded by mountains rising over 2500m high. Due to its elevation it is cooler than Peshawar and Islamabad, but has considerable temperature fluctuations on a daily and seasonal scale. The rainfall in Quetta is very low as is its humidity because of the surrounding desert. Average temperature maxima and minima are 10°C and -2°C respectively in January and 35°C and 18°C respectively in July. Quetta is classified as being in a cool semi desert climate zone. Saidu Sharif is a town in the northern hills at an elevation of about 1000m. Surveys were carried out in Mingora a “twin town” about a mile from Saidu Sharif. Specific climatological data for the two towns was not able to be obtained. The main factor however for both towns are their elevations giving mean temperature maxima and minima of 14.3°C and 2.2°C respectively in January and 36.4°C and 20.8°C respectively in June. The climate zone for Saidu can be described as semi desert. Season - The project was divided into two surveys, one in summer (July 1993) and the other in winter (December 1993 - January 1994) each extending over about a week. Appendix C page 259 MRL Australia ASHRAE RP-884 Final Report Sample buildings This table indicates only one building per city in Pakistan. In actual fact there were many buildings, including residences and offices. In the vast majority of cases, there was only one subject per building. In many cases the subjects were monitored during occupancy of more than a single building, making the data incompatible with the RP-884 structure. Therefore, for simplicity, all buildings within a particular city are treated as a single building. Location Building Sample Climate Floor Occupancy Code Size Controls Area Pattern (blcode) (n) (bldgtype) Karachi 1 - summer 190 NV residential houses 1 - winter 470 and office buildings Multan 2 - summer 437 NV residential houses and office buildings Peshawar 3 - summer 556 NV residential houses 3 - winter 513 and office buildings Quetta 4 - summer 492 NV residential houses 4 - winter 425 and office buildings Saidu Sharif 5 - summer 568 NV residential houses 5 - winter 548 and office buildings Instruments Indoor climatic instrumentation was recorded by a portable datalogger. Relative humidity and air temperature were monitored by a Hanna Instruments probe. This consisted of a polished aluminium sheath 19mm in diameter, containing in its ventilated tip a humidity sensor (solid-state hygrometer) and a thermistor. The instrumentation measured air temperature, globe temperature and humidity. The globe thermometer had a 38mm diameter ping pong ball with appropriate emissivity attached over the sensor. All variables were measured at subjects’ waist height. Questionnaire The questionnaire addressed conditions at time of physical measurements. Time lapse between instrument measurements and questionnaire response was never more than 10 minutes. Comfort was rated using the 7-pt semantic differential based on Bedford. Thermal preference was rated on a want to be warmer/cooler descriptive scale and thermal acceptability questions were not considered. Other thermal environmental parameters included were air movement, draft and skin moisture. Metabolic activity was based on a descriptive scale and noted at the time the questionnaire was being carried out. Total Appendix C page 260 MRL Australia ASHRAE RP-884 Final Report clothing ensemble insulation experienced by the subject was estimated using the ISO 7730 checklist and work of McCullough (eg 1985) and others. Outdoor meteorological data Daily outdoor maximum and minimum temperatures were obtained for a number of the centres from the Pakistan Meteorological office for July and December 1993 and January 1994. Where temperatures were not provided they were replaced with climatological data (monthly means) from the International Station Meteorological and Climate Summary Vol.2 CDROM (ISMCS, 1992). All outdoor humidities were also obtained from this source and had to be derived from mean dewpoint temperature and mean temperature minima and maxima. RP-884 standardization assumptions The Bedford 7-point thermal comfort scale was mapped directly to the ASHRAE 7-point thermal sensation scale for RP-884 purposes. The data was presented as subjects in individual houses, with studies conducted in summer and winter, so the project was of longitudinal research design. For the purpose of this study all houses in the same city were considered to be identical buildings, thus it was assumed there was a number of subjects from one building for each city and the subjects were independent between both the summer and winter studies. Some indices in the original data set had to be re-defined to conform to RP-884 standards. Clo was estimated by ISO 7730 (1984) and corrected to the ASHRAE 55-92 Standard using the regression models developed within RP-884. The activity variable in the original data set was used such that if activity was <= 4 then 0.15 clo was added to the total clothing ensemble to form another variable (insul), that accounted for the additional insulation provided by a chair for subjects that were seated. Velocity measurements in the raw data file indicated a systematic bias that was time-dependent. The original data in all summer files was found to be less affected and so original data were used. In the winter files, values >1.5 m/s were replaced with an average. The Multan, Winter field experiment was omitted from the RP-884 database. Appendix C page 261 MRL Australia ASHRAE RP-884 Final Report C.10. Project Title - Comfort criteria for passively cooled buildings. a PASCOOL task. Project researchers and class of investigation N. Baker and M. Standeven, The Martin Centre for Architecture and Urban Studies, University of Cambridge, UK. This is a CLASS-2 field experiment. Project file names in the RP-884 database This project is disseminated as file number 27 (summer - NV) in the RP-884 database. Project publications Baker, N and M. Standeven. (1995) “A Behavioural Approach to Thermal Comfort Assessment in Naturally Ventilated Buildings”. Proceedings from CIBSE National Conference, Ch 76-84. Baker, N. and M. Standeven. (1994) Comfort criteria for passively cooled buildings. A PASCOOL task. Renewable Energy. V 5. n 5-8 Aug 1994. p 977-984. Project location, climate and season This field experiment was carried out in Athens, Greece for the summer season. Athens has a Mediterranean climate. Sample buildings Building Code Sample Size Climate Controls Floor Occupancy Pattern (blcode) (n) (bldgtype) Area 1 409 NV residential building 2 276 NV residential building 3 443 NV residential building 4 176 NV residential building 5 187 NV residential building 6 135 NV residential building Instruments Indoor room climate instrumentation included: a thermistor to measure air temperature, an omnidirectional hot-wire sensor to measure air speed, a solid-state hygrometer to measure humidity and a globe thermometer with 38mm diameter ping pong ball to measure globe temperature. Appendix C page 262 MRL Australia ASHRAE RP-884 Final Report Local climate instrumentation consisted of: a calibrated sensor array comprising air temperature thermistor, omnidirectional thermistor anemometer and two hemispherical globe thermometers, mounted on a headset similar to that of a walkman. Data was logged on a portable logger allowing complete thermal histories to be recorded for the day, including time when the subject was away from the room. The local data (headsets) were attached ot questionnaire responses in the RP-884 database file for this PASCOOL project. In cases where local data were unsuitable or unavailable, room data were substituted. Questionnaire The questionnaire addressed the conditions at the time physical measurements were being taken. Sensation was rated on the ASHRAE 7-pt scale. Questions of thermal acceptability and thermal preference where both considered and metabolic ratings were taken. Clothing insulation was estimated using the ISO 7730 checklist. Adaptive behaviour questions regarding changes in clothing and adjustment to controls such as opening or closing shades, blinds or windows and relocations within the room were recorded. Outdoor meteorological data Outdoor Meteorological air temperature data was recorded simultaneously with indoor measurement made. For the purposes of RP-884 outdoor temperatures at 600 hrs and 1500 hrs were extracted. Humidities at 600 hrs and 1500 hrs were obtained from the International Station Meteorological and Climate Summary (ISMCS, 1992) CDROM. RP-884 standardization assumptions This project was of longitudinal research design, but for the purposes of RP-884 subjects were assumed to be independent (ie. cross-sectional). Clothing insulation was estimated using the ISO 7730 (1984) Standard, it was therefore necessary to adjust clo to conform to the ASHRAE 55-92 Standard. Also where the metabolic rate was <= 2 met it was assumed the subject was seated and so 0.15 clo was added to the total clothing ensemble in these cases to account for the insulation provided by a chair. The 5-pt variable PRF_VOTE in the original data was re-coded to our 3-pt McIntrye (MCI) scale. Where air velocity was missing 0.1 m/s was temporarily inserted for the software based index calculation and then removed from the database. Appendix C page 263 MRL Australia ASHRAE RP-884 Final Report Appendix C page 264 MRL Australia ASHRAE RP-884 Final Report C.11. Project Title - Developing indoor temperatures for naturally ventilated buildings. Project researchers and class of investigation I. A. Raja, J. F. Nicol and M. A. Humphreys (Oxford-Brookes University, UK). This is a CLASS-3 investigation. Project publications Nicol, J. F., M. A. Humphreys and I. A. Raja (1995). “Developing Indoor Temperatures for Naturally Ventilated Buildings”. Proceeding for CIBSE National Conference. Also see the Full Report. Project file names in the RP-884 database This project is disseminated as file number 28 (summer - NV) in the RP-884 database. Project location, climate and season The project is located in Oxford, South Britain about 63m above sea level and is situated at 51o 46’ North and 1o 16’ West. The climate of Oxford is typical of the low lying part of the English midlands and is also influenced by its proximity to the Atlantic. Oxford experiences one of the warmer maxima in the surrounding area with a mean maximum temperature of 21.7°C in July. The mean minima of 1.3°C in January and February reflects weather similar to that of the midlands and south-east. This field experiment was completed in the summer months of August and September and comes under the climate classification of west coast marine. Sample buildings Building Code Sample Size Climate Floor Occupancy Pattern (blcode) (n) Controls Area (bldgtype) 1 496 NV School of Architecture plus Biological and Molecular Sciences 2 334 NV Headington Hill Hall. 3 47 NV Tonge building. Appendix C page 265 MRL Australia ASHRAE RP-884 Final Report Instruments Air temperature was measured using a thermistor. An adapted thermistor probe with a 38mm diameter ping pong ball of suitable emissivity attached, was used to measure globe temperature. Air speed was registered using an omnidirectional sensor and a solid-state hygrometer was used to measure humidity. All measurements were taken at waist (generally desk) height. Questionnaire A comfort rating on the 7pt Bedford scale was addressed in the questionnaire as well as thermal preference. Thermal acceptability was not recorded. Metabolic ratings were taken at the time the questionnaire was being answered, but covered the 15 minute period before the questionnaire was completed. Clothing insulation estimates were based on the ISO 7730 checklist with the insulation effects of chair included in the total clothing ensemble of the subject. Questions of adaptive behaviour and perceived control on a subjects thermal environment were addressed. Specifically, whether doors, window and curtains or blinds could be opened and closed as well as the influence of fans and heater that could be switched on or off. Outdoor meteorological data Outdoor Meteorological data was obtained for every 0.25 hours from the Oxford University Radcliffe Observatory by the original researchers. From this, air temperatures and relative humidities at 600 hours and 1500 hours were extracted for the purposes of RP-884. RP-884 standardization assumptions The research design of this project was longitudinal, but for RP-884 purposes all subjects were assumed to be independent (ie. cross-sectional). The Bedford 7-point thermal comfort scale was mapped directly to the ASHRAE 7-point thermal sensation scale for RP-884 purposes. Clothing insulation, estimated using the ISO 7730 (1984) Standard was corrected to the ASHRAE 55-92 Standard via regression models developed within RP-884. Allowance for the insulation provided by a chair was incorporated into the total clothing ensemble by the original researchers only when the subjects reported themselves as seated. This provided the RP-884 insul variable. To obtain clothing insulation (clo) in isolation, 0.15 clo was subtracted. All rows with missing air temperature were deleted, but where velocity Appendix C page 266 MRL Australia ASHRAE RP-884 Final Report was missing, 0.1 m/s was temporarily substituted and where indoor relative humidity and metabolic rate were missing, 50% and 1 met respectively were temporarily substituted for the purposes of index calculations and then removed from the database. Appendix C page 267 MRL Australia ASHRAE RP-884 Final Report C.12. Project Title - Mixed mode climate control: some hands-on experience. Project researchers and class of investigation David Rowe. Department of Architectural and Design Science, Sydney University, Australia. This is a CLASS-2 investigation Project file names in the RP-884 database This project is disseminated as file numbers 29 (Summer - Mixed Mode), 30 (winter - Mixed Mode) and 31 (winter - HVAC) in the RP-884 database. Project publications Nothing published yet. Project location, climate and season The field experiment was conducted in Sydney, the capital of the state of New South Wales in Australia. Sydney’s climate is humid and sub-tropical. The project conducted in both summer and winter seasons. Sample buildings Building Code Sample Size Climate Controls Floor Occupancy (blcode) (n) (bldgtype) Area Pattern and season 1 137 - summer Mixed (hybrid) university offices 170 - winter 2 83 - winter HVAC administration offices Instruments RTD devices were used to measure air temperature. No globe temperatures were measured but mean radiant temperature was provided based on the average of six orthogonal plane radiant temperatures, areally weighted for the projection area factors of the human body. Air speed was assessed using an omnidirectional sensor and included turbulence intensity measurements (> 10Hz). A chilled-mirror dewpoint sensor was used to measure humidity. All measurements were taken at a single height. Questionnaire Appendix C page 268 MRL Australia ASHRAE RP-884 Final Report The questionnaire for this project was based directly on that used for the ASHRAE RP-702 Hot Humid Field Experiment in Townsville Australia (see above for de Dear et al., 1994). Thermal sensation rated on the 7-pt ASHRAE scale was recorded at the time physical measurements were being taken, along with the other items on the questionnaire that follow. Thermal acceptability and thermal preference was addressed. Metabolic ratings at the time of and one hour before the questionnaire were recorded. The total clothing ensemble insulation was estimated using the ASHRAE 55-92 checklist. Other thermal environmental parameters considered include air movement. Outdoor meteorological data Outdoor Meteorological Data consisting of air temperature and relative humidity at 600 hours and 1500 hours was obtained for this field experiment from Macquarie University’s Meteorological site, Sydney, Australia. RP-884 standardization assumptions The research design for this project was longitudinal and for the purpose of RP-884 all subjects were assumed to be independent (ie. cross-sectional). Clothing insulation was estimated from ASHRAE 55-92 checklists so no alterations were necessary apart from the addition of 0.15 clo to account for the insulation effects of a chair in creating our insul variable. Throughout the field experiment where mean radiant temperature was not provided air temperature was entered as a substitute. Appendix C page 269 MRL Australia ASHRAE RP-884 Final Report C.13. Project Title - ASHRAE sponsored RP-462. San Francisco area. Project researchers and class of investigation Gail Schiller, Edward Arens, Fred Bauman, Charles Benton, Marc Fountain and Tammy Doherty (CEDR at University of California, Berkeley). This is a CLASS-1 field experiment Project file names in the RP-884 database This project is disseminated as file numbers 32 (summer - HVAC), 33 (summer - NV), 34 (winter - HVAC) and 35 (winter - NV) in the RP-884 database. Project publications Schiller, G. E., E. Arens, F. Bauman, C. Benton, M Fountain and T. Doherty. (1988) A Field Study of Thermal Environments and Comfort in Office Buildings: Final Report--ASHRAE 462. (CEDR:UC Berkeley). Schiller, G. E. (1990) A comparison of measured and predicted comfort in office buildings. ASHRAE Transactions, 96(1). Project location, climate and season RP-462 was conducted over five locations within the San Francisco Bay area including Berkeley, San Ramon, Palo Alto, San Francisco and walnut Creek. All five cities are within a Mediterranean climate zone, but all have different local climates due to their location around the San Francisco Bay area. San Francisco is located right on the coast, but also very close to the Bay. Palo Alto is situated further from the coast close to southern end of the Bay and behind the Santa Cruz Mountains. Berkeley is located across the Bay from the Golden Gate and Walnut Creek is further inland almost directly east of Berkeley. San Ramon is a similar but shorter distance from the Bay as Walnut Creek, but instead it is almost directly east of San Francisco. The field experiments were conducted across both summer and winter seasons. Appendix C page 270 MRL Australia ASHRAE RP-884 Final Report Sample buildings Location Buildg Sample Climate Floor Occupancy Code Size Controls Area Pattern (blcode) (n) and (bldgtype) Season* Berkeley 1 122 - S NV 236,600 ft2. crowed open 121 - W plan offices. San Ramon 2 119 - S 123 HVAC - 2,000,000 ft2. office building - W thermal ice storage and evap. ponds. Palo Alto 3 92 - S HVAC 187,000 ft2. mostly private 101 - W (multizone offices. HVAC with EMS) San 4 108 - S HVAC - heat 191,000 ft2. open plan with Francisco 134 - W pump mech. private system. balconies on perimeter. San 5 115 - S roof-mounted 54,000 ft2. open plan Francisco 132 - W HV unit, no converted mech. a.c., factory. San 6 123 - S NV 90,000 ft2. open plan and Francisco 136 - W private offices. San 7 107 - S HVAC - 265,000 ft2. open plan and Francisco 122 - W thermal ice private offices. storage, VAV perimeter reheat. San 8 117 - S HVAC 634,000 ft2. large open plan. Francisco 147 - W Walnut 9 23 - S HVAC 316,400 ft2. open plan and Creek 145 - W private offices. Walnut 10 107 - S HVAC 368,000 ft2. open plan with Creek 146 - W partitions and private offices. * S = summer, W = winter in the sample size and season column. Instruments Air temperature, air velocity, humidity, and globe temperatures were measured using a mobile cart at the heights indicated below, with the exception of the one stationary observation point. Air temperature was measured with a shielded platinum RTD at 0.6m and shielded type T thermocouples at 0.1m, 0.6m and 1.1m were used. Air velocity was measured by an elliptical omnidirectional constant temperature anemometer at 0.6m and spherical omnidirectional temperature compensated anemometer at 0.1m and 1.1m. Humidity was measured by a chilled-mirror dew point sensor at 0.6m. Globe temperatures were measured by a type T thermocouple inside a 38 mm diameter table tennis ball (painted Appendix C page 271 MRL Australia ASHRAE RP-884 Final Report grey) at heights of 0.1m, 0.6m and 1.1m on the mobile cart and at 1.1m in the stationary set up. Other variables measured not of relevance to RP-884 include radiant temperature asymmetry, surface temperature and illumination. Questionnaire Questionnaire responses were collected at the time physical measurements were being taken. The ASHRAE 7-pt scale was used to determine thermal sensation. The McIntyre scale was used to assess thermal preference. Thermal acceptability was not addressed. Metabolic rating and clothing insulation estimates were based on checklists in ASHRAE Standard 55-81 (1981). The background section of the survey (not necessarily completed when physical measurements were being made) covered general descriptions of office work areas; degree of satisfaction with components of their work environment; personal and comparative comfort and personal subject related information. Outdoor meteorological data Outdoor Meteorological air temperature minima and maxima were purchased from the US National Climate Data Center (NCDC) for sites considered of similar climatic situations to the study locations. Where a suitable site could not be requisitioned, climatological data was extracted from the International Station Meteorological and Climate Summary (ISMCS, 1992) CDROM. All climatological humidity data were also obtained from ISMCS (1992). RP-884 standardization assumptions RP-884 is the fourth ASHRAE sponsored project in the series RP-462, RP-702 and RP- 821. A lot of the assumptions and standards of RP-462 project have formed the basis for the later projects including RP-884, thus limited standardisation has been necessary here. Clothing insulation was converted from ASHRAE 55-81 to the 55-92 standard. 0.15 clo was added to the total clothing ensemble for the insulation effects of a chair to create our insul variable. The research design of this project was part longitudinal and part cross-sectional, but for RP-884 purposes all subjects were assumed to be independent. Appendix C page 272 MRL Australia ASHRAE RP-884 Final Report C.14. Project Title - A field investigation of thermal comfort environmental satisfaction and perceived control levels in UK office buildings, University of Liverpool. Project file names in the RP-884 database This project is disseminated as file numbers 38 (summer -NV), 39 (winter - NV) and 40 (winter - Mixed Mode) in the RP-884 database. Project researchers and class of investigation Ruth N. Williams (The Building Services Research and Information Association, Berkshire, UK). This is a CLASS-2 investigation Project publications Williams, R. N. (1995). A field investigation of thermal comfort environmental satisfaction and perceived control levels in UK office buildings. Healthy Buildings. Vol. 3 pp. 1181-1186. Williams, R (1996) “Predicting environmental dissatisfaction in UK offices, “CIBSE/ASHRAE Joint National Conference, Harrogate UK, VII., pp.167-178. Project location, climate and season This project was conducted across three towns/cities in the UK, including Liverpool, St Helens and Chester. All three come under the west coast marine climate classification. The study was carried out in summer and winter months. Instruments Air temperature was measured using thermistors and an omnidirectional hot bead sensor to measure air speed. A Envirlog supplied sensor (type unknown) was used to measure humidity and by attaching 38mm diameter ping pong balls globe temperature was also measured. Air Speed and humidity were measured at waist height. Air temperature and globe temperature were measured at all three heights (ankle, waist and head), but provided to the RP-884 database as a single average. Appendix C page 273 MRL Australia ASHRAE RP-884 Final Report Sample buildings Location Building Sample Size Climate Floor Occupancy Code (n) Controls Area Pattern (blcode) and season (bldgtype) Liverpool 1 19 - summer NV office buildings A&B St Helens 2 8 - summer NV LC St Helens 3 140 - summer NV WH 31 - winter St Helens 4 121 - winter Mixed NWB (hybrid) Chester 5 44 - winter NV CCH Chester 6 31 - winter NV COM Chester 7 67 - winter NV ANN Liverpool 8 36 - winter NV SEN Questionnaire The questionnaire addressed both conditions at the time of physical measurements and typical overall conditions. Thermal sensation was rated using a 7-pt ASHRAE scale. Thermal comfort was rated using the 7-pt Bedford scale. Thermal acceptability was addressed but not thermal preference. Metabolic rating was dealt with by asking if the subject was sitting or standing during most of their work time, from which an estimate was derived. Clothing insulation estimates were based on the ISO 7730 (1994) checklist with corrections for the insulation from a chair included. Adaptive behaviour questions of the subjects perceived control on temperature, humidity, freshness, smell, appearance, lighting, noise and layout within their working environment was noted. Outdoor meteorological data Outdoor Climatological air temperature data at 600 hours and 1500 hours was obtained from Weather (the journal, for site - Ringway). Relative humidity at 600 hours and 1500 hours was obtained from the International Station Meteorological and Climate Summary (site - Liverpool) CDROM. RP-884 standardization assumptions The research design of this study was cross-sectional which satisfies the assumption of independence between subjects for RP-884. Coding conventions for some variables was Appendix C page 274 MRL Australia ASHRAE RP-884 Final Report altered to conform to RP-884 definitions. Clothing insulation estimated using ISO 7730 (1984) checklists, was corrected to follow the ASHRAE 55-92 Standard. The sex (gender) of subjects was not indicated in the study so an average of the adjusted clo to the ASHRAE 55- 92 Standard for males and female was used in all cases. 0.15 clo was then subtracted from this corrected clothing plus chair insulation to create our clo variable. Appendix C page 275 MRL Australia ASHRAE RP-884 Final Report C.15. Project Title - Thermal comfort in the humid tropics: field experiments in air conditioned and naturally ventilated buildings in Singapore. Project researchers and class of investigation R. J. de Dear, K. G. Leow and S. C. Foo (National University of Singapore). This is a CLASS-2 field experiment. Project file names in the RP-884 database This project is disseminated as file numbers 41 (summer - HVAC) and 42 (summer -NV) in the RP-884 database. Project publications de Dear, R. J., Leow, K. G. and S. C. Foo (1991) “Thermal comfort in the humid tropics: Field experiments in air conditioned and naturally ventilated buildings in Singapore”. International Journal of Biometeorology, Vol. 34, pp. 259-265. de Dear, R.J., Leow, K. G. and A. Ameen (1991) “Thermal comfort in the equatorial climatic zone -- Part II: Climate chamber experiments on thermal acceptability in Singapore”. ASHRAE Transactions, Vol. 97(1), pp. 880-886. Project location, climate and season The field experiments were conducted in both summer and winter seasons in Singapore which is a wet equatorial climate. Sample buildings Building Code Sample Size (n) Climate Controls Floor Occupancy (blcode) (bldgtype) Area Pattern 1 333 HVAC office building 2 583 NV residential building Instruments A hot-wire sensor was used to measure air speed. Relative humidity was measured using an aspirated psychrometer and mercury-in-glass thermometers were used to measure air and globe temperature. For globe temperature a 0.15m copper sphere was used. Appendix C page 276 MRL Australia ASHRAE RP-884 Final Report Questionnaire Thermal sensation was rated on the ASHRAE 7-pt scale. Thermal acceptability and thermal preference was not addressed. Metabolic ratings were taken and clothing insulation was estimated using the ISO7730 1984 standard. Questions of adaptive behaviour were not considered. Outdoor meteorological data Outdoor Climatological air temperature and relative humidity data at 600 hours and 1500 hours was obtained from the International Station Meteorological and Climate Summary CDROM (ISMCS, 1992) for Paya Lebar, the closest site. RP-884 standardization assumptions The research design was cross-sectional which satisfied the assumptions for RP-884, that all subjects were independent. Clothing insulation estimated using the ISO7730 1984 standard was corrected to the ASHRAE55 1992 standard. 0.15 clo was added to the total clothing ensemble insulation for the insulation effects of a chair forming a separate variable in RP-884. Appendix C page 277 MRL Australia ASHRAE RP-884 Final Report C.16. Project Title - The Steelcase building. Grand Rapids Michigan, US Project researchers and class of investigation F. Bauman et al. (CEDR at the University of California at Berkeley). This is a CLASS-1 field experiment. Project file names in the RP-884 database This project is disseminated as file number 43 (winter - HVAC) in the RP-884 database. Project publications Project location, climate and season This project was conducted in winter in Grand Rapids, Michigan. Grand Rapids has a continental location in the Great Lakes region of North America and has a humid mid- latitude climate. Sample buildings Building Code Sample Size (n) Climate Controls Floor Occupancy (blcode) (bldgtype) Area Pattern 1 85 HVAC office building Instruments The Grand Rapids, Michigan field experiment was not part of the Advanced Customer Technology Test (ACT2) study but was carried out in an identical format. A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m. The sensors chosen were selected to meet the response time and accuracy requirements of ASHRAE Standard 55-81 and ISO Standard 7730 for thermal assessment. YSI series 700 probes with vinyl- coated tips were used to measure air temperature. Globe temperature was measured by attaching a 38 mm diameter table tennis ball on the temperature sensors. The balls were painted grey for correct emissivity. Air velocity was measured by Dantec 54R10 anemometers, which are omnidirectional fully temperature-compensated sensors. Dewpoint temperature was measured by a General Eastern DEW-10 chilled mirror dewpoint transducer. All parameters were measured at all three heights except dewpoint temperature which was only measured at 0.6m. Radiant asymmetry and illuminance where also recorded but were not essential to the purpose of RP-884. Appendix C page 278 MRL Australia ASHRAE RP-884 Final Report Questionnaire The questionnaire consisted of an on-line questionnaire, which addressed conditions at the time physical measurements were being taken and a background questionnaire. The latter covered subject details such as, health and emotional characteristics, office description, work area and job satisfaction, environmental sensitivity, plus personal comfort, satisfaction and perceived control. In the on-line section thermal sensation was rated on the 7-pt ASHRAE scale. Thermal preference was assessed on a descriptive 3-pt scale. Thermal acceptability was not rated. Metabolic rate was estimated based on a checklist referring to the subjects activity in the 15 minutes before completing the on-line questionnaire, using tables in the ASHRAE Handbook of Fundamentals (HOF, 1985). Clo estimates were based on responses to the clothing item checklist provided in the on-line questionnaire from the ASHRAE Standard 55-81 method. Outdoor meteorological data Outdoor Meteorological data files are for Grand Rapids, MI, USA for the period January to February 1992 were bought from the State Climatologist for Michigan by RP-884. The files supplied had 24 hourly Temperatures (F) and Relative Humidity (%) for the 60 day period required, from which air temperatures and relative humidities at 600 hrs and 1500 hrs were extracted. RP-884 standardization assumptions The detailed methods and protocol used in ASHRAE RP-462 (and extended to the ASHRAE RP-702 project described above) were carried out in full for the ACT2 Project. Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462 little standardisation was necessary. However, clothing was based on the ASHRAE 55-81 method, and so required conversion into equivalent ASHRAE 55-92 values. 0.15 clo was then added for chair insulation. The research design of this field experiment was part longitudinal and part cross-sectional, but for the purposes of RP-884, independence between subjects was assumed. Appendix C page 279 MRL Australia ASHRAE RP-884 Final Report C.17. Project Title - Sunset Building: a study of occupant thermal comfort in support of PG&E’s Advanced Customer Technology Test (ACT2) for maximum energy efficiency Project researchers and class of investigation Charles C. Benton and Gail S. Brager (CEDR at University of California at Berkeley). This is a CLASS-1 investigation. Project file names in the RP-884 database This project is disseminated as file numbers 44 (summer - HVAC) and 45 (winter - HVAC) in the RP-884 database. Project publications Benton, C. C. and Brager, G. S. (1994) Sunset Building: Final Report; A study of occupant thermal comfort in support of PG&E’s advanced customer technology test (ACT2) for Maximum Energy Efficiency, CEDR. Benton, C. C. and Brager, G. S. Advanced Customer Technology Test (ACT2) Verifone Progress Report (CEDR UC Berkeley). Project location, climate and season San Ramon is one of 3 location, in which 2 of the 4 components of the ACT2 project were carried out. San Ramon falls within a Mediterranean climate zone, but experiences local climatic effects due its location. San Ramon is inland east of San Francisco Bay and almost directly east of the city of San Francisco. The field experiments were conducted across the summer and winter months. Sample buildings Building Code Sample Size (n) Climate Controls Floor Occupancy (blcode) (bldgtype) Area Pattern 1 152 HVAC office building 2 133 HVAC office building 3 96 HVAC office building Instruments Appendix C page 280 MRL Australia ASHRAE RP-884 Final Report A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m. The sensors chosen were selected to meet the response time and accuracy requirements of ASHRAE Standard 55-81 and ISO Standard 7730 for thermal assessment. YSI series 700 probes with vinyl-coated tips were used to measure air temperature. Globe temperature was measured by attaching a 38 mm diameter table tennis ball on the temperature sensors. The balls were painted grey for correct emissivity. Air velocity was measured by Dantec 54R10 anemometers, which are omnidirectional fully temperature-compensated sensors. Dewpoint temperature was measured by a General Eastern DEW-10 chilled mirror dewpoint transducer. All parameters were measured at all three heights except dewpoint temperature which was only measured at 0.6m. Radiant asymmetry and illuminance where also recorded but were not essential to the purpose of RP-884. Questionnaire The questionnaire consisted of an on-line, laptop-computer based questionnaire, which addressed conditions at the time physical measurements were being taken and a background questionnaire. The latter covered subject details such as health and emotional characteristics, office description, work area and job satisfaction, environmental sensitivity, plus personal comfort, satisfaction and perceived control. In the on-line section thermal sensation was rated on the 7-pt ASHRAE scale. Thermal preference was assessed on a descriptive 3-pt scale. Thermal acceptability was not rated. Metabolic rate was estimated based on a checklist referring to the subjects activity in the 15 minutes before completing the on-line questionnaire, using tables in the ASHRAE Handbook of Fundamentals (HOF, 1985). Clo estimates were based on responses to the clothing item checklist provided in the on-line questionnaire from the ASHRAE Standard 55-81 method. Outdoor meteorological data Outdoor Meteorological air temperature data was obtained by request to the National Climate Data Center (NCDC) for San Ramon and humidity was obtained from the International Station Meteorological Climate Summary CDROM for the closest available site (Stockton). From this data air temperatures and relative humidities at 600 hrs and 1500 hrs were extracted for RP-884 purposes. Appendix C page 281 MRL Australia ASHRAE RP-884 Final Report RP-884 standardization assumptions This project was conducted based on the format of RP-462 (RP-702). Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462 little standardisation was necessary. However, clothing was based on the ASHRAE 55-81 method, and so required conversion into equivalent ASHRAE 55-92 values. 0.15 clo was then added for chair insulation. The research design of this project was longitudinal, but for RP-884 purposes all subjects were assumed to be independent (ie. cross-sectional). Appendix C page 282 MRL Australia ASHRAE RP-884 Final Report C.18. Project Title - The Verifone building, a component of the Advanced Customer Technology Test (ACT2) project. Project researchers and class of investigation Charles C. Benton and Gail S. Brager (CEDR at University of California at Berkeley). This is a CLASS-1 field experiment. Project file names in the RP-884 database This project is disseminated as file number 46 (winter - HVAC) in the RP-884 database. Project publications Benton, C. C. and Brager, G. S. Advanced Customer Technology Test (ACT2) Verifone Progress Report (CEDR UC Berkeley) Project location, climate and season This field experiment was conducted in winter in Auburn, California and is one of the components of the ACT2 project. Auburn has a Mediterranean bordering on high altitude climate and is located inland and to the north east of San Francisco. Sample buildings Building Code Sample Size (n) Climate Controls Floor Occupancy (blcode) (bldgtype) Area Pattern 1 128 HVAC office building Instruments A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m. The sensors chosen were selected to meet the response time and accuracy requirements of ASHRAE Standard 55-81 and ISO Standard 7730 for thermal assessment. YSI series 700 probes with vinyl-coated tips were used to measure air temperature. Globe temperature was measured by attaching a 38 mm diameter table tennis ball on the temperature sensors. The balls were painted grey for correct emissivity. Air velocity was measured by Dantec 54R10 anemometers, which are omnidirectional fully temperature-compensated sensors. Dewpoint temperature was measured by a General Eastern DEW-10 chilled mirror dewpoint transducer. All parameters were measured at all three heights except dewpoint Appendix C page 283 MRL Australia ASHRAE RP-884 Final Report temperature which was only measured at 0.6m. Radiant asymmetry and illuminance were also recorded, but not essential to the purpose of RP-884. Questionnaire The questionnaire consisted of an on-line questionnaire, which addressed conditions at the time physical measurements were being taken and a background questionnaire. The latter covered subject details such as, health and emotional characteristics, office description, work area and job satisfaction, environmental sensitivity, plus personal comfort, satisfaction and perceived control. In the on-line section thermal sensation was rated on the 7-pt ASHRAE scale. Thermal preference was assessed on a descriptive 3-pt scale. Thermal acceptability was not rated. Metabolic rate was estimated based on a checklist referring to the subjects activity in the 15 minutes before completing the on-line questionnaire, using tables in the ASHRAE Handbook of Fundamentals (HOF, 1985). Clo estimates were based on responses to the clothing item checklist provided in the on-line questionnaire from the ASHRAE Standard 55-81 method. Outdoor meteorological data An error in dates requesting outdoor air temperature data for Auburn from the National Climate Data Center (NCDC) resulted in the use of climatological data for both air temperature and relative humidity at 600 hours and 1500 hours. The data was obtained from the International Station Meteorological Climate Summary CDROM (ISMCS, 1992) for the closest available site, Sacramento. RP-884 standardization assumptions The detailed methods and protocol used in ASHRAE RP-462 (and extended to the ASHRAE RP-702 project described above) were carried out in full for the ACT2 Project. Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462 little standardisation was necessary. However, clothing was based on the ASHRAE 55-81 method, and so required conversion into equivalent ASHRAE 55-92 values. 0.15 clo was then added for chair insulation. The research design of this project was longitudinal, so for RP-884 purposes all subjects were assumed to be independent (ie. cross-sectional). Appendix C page 284 MRL Australia ASHRAE RP-884 Final Report APPENDIX D - CLIMATE CLASSIFICATION Appendix D page 285 MRL Australia ASHRAE RP-884 Final Report Appendix D page 286 MRL Australia ASHRAE RP-884 Final Report Figure D.1: The climate classification used throughout the RP-884 database. Appendix D page 287 MRL Australia ASHRAE RP-884 Final Report APPENDIX E - CODEBOOK FOR RAW DATA IN RP-884 DATABASE Appendix E page 287 MRL Australia ASHRAE RP-884 Final Report RP-884 Variable Coding Conventions variable's Type of data code name Description of variable and units Basic blcode building ID code Identifiers sub subject number age subject's age [years] sex subject's gender [0=male, 1=female] year year day julian date (jan 1=1, dec 31=365) time time thermal ash ASHRAE Thermal Sensation Scale [-3, +3] questionnaire prxy_tsa Thermal acceptability defined as -1.5<=ASH<=+1.5 [1=unacc. 2=acc] tsa Thermal Acceptability Question [1=unacc. 2=acc] mci Thermal Preference [1=want cooler, 2=no change, 3=want warmer] vent air movement acceptability [6(very acc), 1(very unacc)] avm air movement preference [3(more), 2(no change), 1(less)] comf General thermal comfort right now [1=very uncomf, 6=very comf] act10 metabolic activity in last 10 minutes [met] act20 metabolic activity between 20 and 10 minutes ago [met] act30 metabolic activity between 30 and 20 minutes ago [met] act60 metabolic activity between 60 and 30 minutes ago [met] met average metabolic rate of subject [met] clo ensemble clothing insulation [clo] upholst insulation of the subject's chair [clo] insul clothing plus upholstery insulation [clo] Indoor Climate ta_h air temperature at 1.1m above floor [oC] Physical Obs ta_m air temperature at 0.6m above floor [oC] ta_l air temperature at 0.1m above floor [oC] dewpt dewpoint temperature [oC] prta_b plane radiant asymmetry temperature [oC] tg_h globe temperature at 1.1m above floor [oC] tg_m globe temperature at 0.6m above floor [oC] tg_l globe temperature at 0.1m above floor [oC] vel_h air speed 1.1m [m/s] vel_m air speed 0.6m [m/s] vel_l air speed 0.1m [m/s] turb_h turbulence intensity at 1.1m above floor [frac] turb_m turbulence intensity at 0.6m above floor [frac] turb_l turbulence intensity at 0.1m above floor [frac] Continue Table. Appendix E page 288 MRL Australia ASHRAE RP-884 Final Report variable's Type of data code name Description of variable and units calculated taav average of three heights' air temperature [oC] indices trav average of three heights' mean radiant temperature [oC] top average of TAAV and TRAV (operative temperature) [oC] velav average of three heights' air speed [m/s] velmax maximum of three heights' air speeds [m/s] tuav average of three heights' turbulence [frac] pa vapor pressure [kPa] rh relative humidity [%] et new effective temperature index et* [oC] set new standard effective temperature index set* [oC] tsens two-node tsens index [-1.5, +2.0] disc two-node disc index [-4, +4] pmv Predicted Mean Vote, Fanger's Model [-3, +3] ppd Predicted Percentage Dissatisfied, Fanger's Model [frac] pd_h Percent Dissatisfied due to Draft at 1.1m height, Fanger et al [frac] pd_m Percent Dissatisfied due to Draft at 0.6m height, Fanger et al [frac] pd_l Percent Dissatisfied due to Draft at 0.1m height, Fanger et al [frac] pd_max Percent Dissatisfied due to Draft, max of all 3 heights, Fanger et al [frac] personal PCC perceived control over thermal environ [1=no control, 5=complete control] environmental PCC_AG aggregate perceived control from PCEC1...PCEC7 control PCS how satisfied are you with PCC [1=very dissat, 6=very sat] PCEC1 can you open/close windows? [1=yes, 0=no] PCEC2 can you open/close external doors [1=yes, 0=no] PCEC3 can you open/close internal doors [1=yes, 0=no] PCEC4 can you adjust thermostats [1=yes, 0=no] PCEC5 can you adjust curtains/blinds [1=yes, 0=no] PCEC6 can you adjust local heaters [1=yes, 0=no] PCEC7 can you adjust local fans [1=yes, 0=no] Do you exercise any PCED1 windows [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always] of these options? PCED2 external door [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always] PCED3 internal door [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always] PCED4 thermostat [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always] PCED5 curtains/blinds [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always] PCED6 local heater [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always] PCED7 local fan [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always] Appendix E page 289 MRL Australia ASHRAE RP-884 Final Report Continue Table. variable's Type of data code name Description of variable and units Outdoor Meteorol day15_ta outdoor 3pm (max) air temp on day of survey [oC] Observations day06_ta outdoor 6am (min) air temp on day of survey [oC] dayav_ta outdoor average of min/max air temp on day of survey [oC] day15_rh outdoor 3pm (min) rel humid on day of survey [%] day06_rh outdoor 6am (max) rel humid on day of survey [%] dayav_rh outdoor average min/max rel humid on day of survey [%] day15_et outdoor 3pm ET* on day of survey (Ta and rh at time of daymx_ta) [oC] day06_et outdoor ET* on day of survey (Ta and rh at time of daymn_ta) [oC] dayav_et outdoor average of min/max ET* on day of survey [oC] Appendix E page 290 MRL Australia ASHRAE RP-884 Final Report APPENDIX F - CODEBOOK FOR THE RP-884 META ANALYSIS Appendix F page 291 MRL Australia ASHRAE RP-884 Final Report Meta Analysis Codebook Variable Description authors investigators of the study season season of study country country study carried out in city city where the study was done seasnum 1 = summer (cooling season), 2 = winter (heating season) dataclas grade of research methods and resulting data (1st, 2nd, 3rd) bldgtype type of building, ie. 1 = climate controlled (HVAC), 2 = free running (NV) and 3 = mixed blcode individual building code n sample size per building m_taav mean taav s_taav standard deviation taav m_trav mean trav s_trav standard deviation trav m_top mean top s_top standard deviation top m_velav mean velav s_velav standard deviation velav m_rh mean relative humidity s_rh standard deviation relative humidity m_et mean et* s_et standard deviation of et* ASH55_92 % indoor climatic obs falling within the relevant ASHRAE 55-92 comfort zone predneut the predicted neutral operative temperature given conditions of vel, rh, insul and met. deltneut neut_top minus predneut preftemp defined in terms of operative temperature by probit analysis of MCI discrep neut_top minus preftemp m_set mean set* s_set standard deviation set* m_pmv mean pmv s_pmv standard deviation pmv m_ppd mean ppd s_ppd standard deviation ppd mpd_max mean pd_max (pd_max being the largest PD of the three heights measured) spd_max standard deviation pd_max m_ash mean ashrae thermal sensation vote s_ash standard deviation ashrae thermal sensation vote m_met mean metabolic rate (met) s_met standard deviation metabolic rate (met) m_insul mean value of the summed clothing and chair insulation (clo) s_insul standard deviation of the summed clothing and chair insulation (clo) m_clo mean clothing insulation (clo) s_clo standard deviation of clothing insulation (clo) mpcc_ag mean pcc_ag (pcc_ag is the index of perceived control) spcc_ag standard deviation pcc_ag (pcc_ag is the index of perceived control) Appendix F page 292 MRL Australia ASHRAE RP-884 Final Report Continue Table Variable Description mday15ta mean day15_ta (daily maximum outdoor air temperature degC) sday15ta standard deviation day15_ta (daily maximum outdoor air temperature degC) mday06ta mean day06_ta (daily minimum outdoor temperature degC) sday06ta standard deviation day06_ta (daily minimum outdoor temperature degC) mdayavta mean dayav_ta (mean of daily min and max temperatures degC) sdayavta standard deviation dayav_ta (mean of daily min and max temperatures degC) mday15et mean day15_et (ET* at time of max outdoor air temperature degC) sday15et standard deviation day15_et mday06et mean day06_et sday06et standard deviation day06_et mdayavet mean dayav_et sdayavet standard deviation dayav_et f_mci_2 % frequency when mci = 2 (no change) f_tsa_2 % frequency when tsa = 2 (acceptable) fprxysat % frequency when prxy_tsa = 2 (-1.5 Appendix F page 293 MRL Australia ASHRAE RP-884 Final Report Appendix F page 294 MRL Australia ASHRAE RP-884 Final Report APPENDIX G - AULICIEMS’ ADAPTIVE MODEL DATABASE Appendix G page 295 MRL Australia ASHRAE RP-884 Final Report indoor outdoor neutrality Location active/passive Researcher (see Auliciems 1981b) TOP temp climate controls 22.8 19.8 22.7 Melbourne A Ballantyne 20.7 9.5 21.3 Melbourne A Ballantyne 20.4 11.1 20.5 Melbourne A Auliciems 1977 15.2 23.9 Sydney A Hindmarsh 13.3 22.3 Sydney A Hindmarsh 21.6 24.2 Sydney P Hindmarsh 19.4 21.4 Sydney P Hindmarsh 12.4 21 Sydney A Wong 21.3 23 Sydney A Wong 19.5 12.8 20.6 Adelaide A Auliciems 22.6 17.1 23.1 Brisbane P Auliciems 19.6 14.7 21.9 Perth A Auliciems 22.4 8.3 21.3 Armidale A Auliciems 28.1 26.2 Darwin P Macpherson 28.1 27.6 Darwin P Macpherson 28.9 26.2 Weipa P Wyndham 28.3 27.8 25.4 Pt Moresby P Ballantyne 25.9 25.4 Pt Moresby P Ballantyne 26.9 25 Pt Moresby P Ballantyne 26.9 27.2 Pt Moresby P Ballantyne 27.8 27 27.5 Honiara P Woolard 28.2 28.9 26.1 Singapore P Ellis 28.6 27 26.1 Singapore P Ellis 28.8 27 27.3 Singapore P Webb 33.4 33.5 30.1 New Delhi P Nicol 30.3 26.4 26.1 Calcutta P Rao 35.9 33.9 31.2 Baghdad P Nicol 28.8 24.8 25.8 Rio de Janeiro P Sa 24.7 21.3 24.6 Rio de Janeiro P Sa 22 22.5 Toronto A Tasker 22.8 23.9 New York A Gagge 21.5 23.6 Minneapolis A Newton 23.5 12.5 24.4 Portland A Pepler 23.6 12.5 22.1 Portland A Pepler 21.1 3.5 19.8 Swedish Towns A SIB 24.1 10.2 19 Swedish Towns A SIB 23.9 0.6 21.5 Zurich A Wanner 23.5 18.3 23.1 Zurich A Wanner 22.7 2.2 20.9 Zurich A Grandjean 23.2 17.8 21.3 Zurich/Basel/Bern A/P Grandjean 18.1 4.7 18.4 London A Bedford 18.8 6.7 19.2 London A Black 19 17 22.2 London A Black 17.2 5.2 17.5 London A Fox 20.5 10.6 22.4 London A Wyon 19.7 4.7 18.9 London A Angus 21.4 15.9 19.4 London P Hickish 21.1 17.9 21.3 Garston A Humphreys 21.4 3.8 19.9 Garston A Humphreys 21.4 7.7 19.7 Garston A Humphreys 21.4 10.8 19.3 Garston A Humphreys 21.4 14.4 20 Garston P Humphreys 21.4 16.4 20.2 Garston P Humphreys Appendix G page 296 MRL Australia ASHRAE RP-884 Final Report Appendix G page 297 MRL Australia