A Strategy for Fit-For-Purpose Occupant Behavior Modelling in Building Energy and Comfort Performance Simulation

A strategy for fit-for-purpose occupant behavior modelling in building energy and comfort performance simulation

Citation for published version (APA): Gaetani dell'Aquila d'Aragona, I. I. (2019). A strategy for fit-for-purpose occupant behavior modelling in building energy and comfort performance simulation. Technische Universiteit Eindhoven.

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Download date: 24. Sep. 2021 A strategy for t-for-purpose occupant behavior modelling in building energy and comfort performance simulation

Isabella Gaetani

Bouwstenen 256

A strategy for fit-for-purpose occupant behavior modelling in building energy and comfort performance simulation

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magnificus prof.dr.ir. F.P.T. Baaijens, voor een commissie aangewezen door het College voor Promoties, in het openbaar te verdedigen op donderdag 27 juni 2019 om 13:30 uur

door

Isabella Iasmina Gaetani dell’Aquila d’Aragona

geboren te Milaan, Italië

Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de promotiecommissie is als volgt: voorzitter: prof.dr.ir. T.A.M. (Theo) Salet 1e promotor: prof.dr.ir. J.L.M. (Jan) Hensen co-promotor: dr.ir. P. (Pieter-Jan) Hoes leden: prof. dipl.- ing. A. (Andreas) Wagner (Karlsruher Institut für Technologie KIT, Germany) prof.dr. L.C.M. (Laure) Itard (Technische Universiteit Delft) prof.dr.ir. B. (Bauke) de Vries

A strategy for fit-for-purpose occupant behavior modelling in building energy and comfort performance simulation

The research reported in this dissertation was supported by PIT/VABI, the SPARK consortium and TKI EnerGO TRECO-office.

© Isabella Gaetani, 2019 All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author and promotors.

A catalogue record is available from the Eindhoven University of Technology Library ISBN: 978-90-386-4770-8 NUR: 955 Published as issue 272 in the Bouwstenen series of the Department of the Built Environment, Eindhoven University of Technology.

Cover design by Isabella Gaetani Printed by TU/e print service – Dereumaux Eindhoven, The Netherlands

Contents

Summary ...... vii Nomenclature ...... ix List of Abbreviations ...... ix List of Symbols ...... x List of Pictograms ...... xii 1 Introduction ...... 1 1.1 Energy use and comfort in the built environment ...... 2 1.2 Building performance simulation ...... 2 1.2.1 OB-related uncertainties in BPS ...... 4 1.2.2 Perception of OB-related uncertainties ...... 5 1.3 Occupant behavior modeling in BPS: research vs. practice ...... 6 1.4 Aim and objectives ...... 8 1.5 Contributions ...... 9 1.6 Thesis outline ...... 10 2 Occupant behavior in building performance simulation ...... 13 2.1 Modeling the influence of occupant behavior on building performance ...... 14 2.1.1 Modeling occupant presence ...... 15 2.1.2 Modeling adaptive behaviors ...... 16 2.1.3 Modeling non-adaptive behaviors ...... 18 2.1.4 Concluding remarks ...... 19 2.2 Overview of widely used occupant behavior modeling approaches ...... 20 2.2.1 Schedules, deterministic and non-probabilistic models ...... 21 2.2.2 Probabilistic models ...... 21 2.2.3 Agent-based modeling (ABM) ...... 24 2.2.4 Concluding remarks ...... 24 2.3 Literature review of widely used occupant behavior models ...... 25 2.4 Inter-comparisons of models with different complexity levels ...... 28

2.4.1 Description of comparison studies: which model performs best? ...... 29 2.4.2 Other studies dealing with model complexity in occupant behavior ...... 33 2.4.3 Concluding remarks ...... 34 2.5 Validity and application range of occupant behavior models ...... 35 2.6 Concluding remarks ...... 35 3 The fit-for-purpose approach: methods, concepts and development process ...... 37 3.1 Overview of the methodology ...... 38 3.2 Theoretical concepts ...... 39 3.2.1 What can the occupants do? ...... 40 3.2.2 Which simulation output do I need? ...... 41 3.2.3 Does occupant behavior matter? ...... 42 3.2.4 Which model should I choose? ...... 45 3.2.5 What should a FFP-OBm strategy look like? ...... 48 3.3 Iterative development process ...... 48 3.3.1 Scope ...... 49 3.3.2 Overview ...... 52 3.3.3 Implementation ...... 55 3.3.4 Application ...... 56 3.3.5 Evaluation ...... 58 3.4 Concluding remarks ...... 59 4 The fit-for-purpose approach: Impact Indices screening Method ...... 61 4.1 Introduction ...... 62 4.1.1 Literature review ...... 62 4.1.2 Research gap ...... 63 4.1.3 The Impact Indices (II) Method ...... 64 4.2 Impact indices ...... 65 4.2.1 Impact indices for blind operation, equipment use, light use and occupant presence ...... 65 4.2.2 Impact indices for temperature setpoint setting ...... 68 4.2.3 Impact indices limitations ...... 69 4.3 Methodological steps ...... 70

4.4 Virtual experiment to test the proposed method ...... 74 4.4.1 Description of the case study ...... 74 4.4.2 Virtual experiment results and observations ...... 75 4.5 Reliability test ...... 81 4.5.1 Description of the one-at-a-time (OAT) sensitivity analysis ...... 81 4.6 Discussion and methodological limitations ...... 85 4.7 Concluding remarks ...... 86 5 The fit-for-purpose approach: the Diversity Patterns Method ...... 87 5.1 Introduction ...... 88 5.2 Methodological steps ...... 88 5.2.1 Quantify the sensitivity of results to different OB aspects ...... 88 5.2.2 Distinguish influential from non-influential aspects ...... 91 5.3 Virtual experiment to test the proposed method ...... 92 5.3.1 Description of the case study ...... 92 5.3.2 Definition of diversity patterns for uncertain OB aspects ...... 94 5.3.3 Run simulations and assess potential OB influence ...... 96 5.3.4 Identify influential OB aspects ...... 98 5.4 Reliability test ...... 100 5.4.1 Implementation of stochastic models to building variant 1 ...... 101 5.4.2 Implementation of stochastic models to building variant 16 ...... 102 5.5 Discussion and methodological limitations ...... 103 5.6 Concluding remarks ...... 105 6 The fit-for-purpose approach: overall view ...... 107 6.1 Dealing with influential OB aspects: possible outcomes...... 108 6.1.1 Epistemic uncertainty of influential OB aspects...... 108 6.1.2 Influential OB aspects’ model complexity ...... 109 6.1.3 Decision-making at higher model complexity ...... 109 6.2 Overall view ...... 111 6.3 Virtual experiment to demonstrate the fit-for-purpose approach ...... 115 6.3.1 Problem definition ...... 115 6.3.2 Results and discussion ...... 117

iii

6.4 Discussion and concluding remarks ...... 125 7 Applications ...... 127 7.1 Product development and performance assessment (R&D phase) ...... 128 7.1.1 Problem definition ...... 128 7.1.2 Choice of building model complexity and preliminary results ...... 133 7.1.3 Impact Indices Method ...... 136 7.1.4 Diversity Patterns Method ...... 136 7.1.5 Decision-making ...... 140 7.1.6 Higher model complexity ...... 141 7.1.7 Concluding remarks ...... 143 7.2 Performance assessment (Design/Commissioning phase) ...... 144 7.2.1 Problem definition ...... 145 7.2.2 Choice of building model complexity and preliminary results ...... 147 7.2.3 Impact Indices Method ...... 149 7.2.4 Diversity Patterns Method ...... 150 7.2.5 Decision-making and Mann-Whitney U test ...... 151 7.2.6 Higher model complexity ...... 152 7.3 Energy Performance Contracting (Retrofitting phase) ...... 153 7.3.1 The ABT Building ...... 154 7.3.2 The Strukton buildings ...... 160 7.4 Concluding remarks ...... 165 8 Conclusions and discussion ...... 167 8.1 Conclusions ...... 168 8.1.1 Development of the FFP-OBm strategy ...... 169 8.1.2 Integration of the FFP-OBm strategy in BPS tool and testing of its reliability 169 8.1.3 Illustration of the usability and efficacy of the FFP-OBm strategy ...... 170 8.1.4 New insights derived from the application of the FFP-OBm strategy ...... 171 8.2 Limitations ...... 172 8.2.1 Limitations of the OB modeling field ...... 172 8.2.2 Limitations of the FFP-OBm strategy ...... 173

8.3 Future research ...... 174 8.3.1 Extension of the research’s scope ...... 174 8.3.2 Application to connected areas of interest ...... 175 8.3.3 Automation of the strategy ...... 175 Appendices ...... 177 Appendix A: Mathematical formulations for probabilistic models ...... 177 Appendix B: List of existing OB models from literature...... 181 Appendix C: Mathematical formulation of Mann-Whitney U test ...... 194 Appendix D: Mann-Whitney U test for heating energy and WOH ...... 197 Appendix E: Stochastic models implemented in Chapter 5 ...... 199 Appendix F: Method to determine the appropriate number of runs nRuns for stochastic models ...... 208 Appendix G: Thermophysical properties variations considered for the uncertainty analysis in Chapter 6 ...... 211 Appendix H: Thermophysical properties of the Task 56 reference building ...... 213 Appendix I: EMS code for modeling the innovative solar shading controls investigated in Section 7.1 ...... 215 Appendix J: Example of occupancy status obtained using Page et al. occupant presence model ...... 221 References ...... 222 Curriculum Vitae ...... 239 Acknowledgements ...... 241 Publication List ...... 245

Summary

The building sector accounts for more than one third of global energy consumption. Concurrently, this sector also represents huge untapped potential for saving energy and reducing emissions of greenhouse gases (GHG). The challenges of accurately predicting and subsequently optimizing the energy performance of existing and new buildings must be overcome in order to tap this potential. However, any attempt to overcome these challenges must take account of the comfort expectations of building occupants, which continue to increase. Building performance simulation (BPS) software are able to simulate the energy and comfort performance of buildings. Their use is becoming widespread in supporting the design and operation of buildings with high indoor environmental quality (IEQ). However, a number of uncertainties related to the BPS models impair the reliability of their simulation outcomes.

Occupant behavior (OB), defined as the presence and actions of occupants that affect building energy use, is recognized as a main source of discrepancy between simulation predictions and actual building performance. For this reason, researchers are attempting to offer more realistic alternatives to the simplistic assumptions regarding occupants that are commonly made in BPS models. Literature shows a proliferation of increasingly complex, data-based models that describe occupant presence and behaviors. However, the use of these models in practice is still very limited. Moreover, simpler models might be preferable, depending on the purpose of the investigation.

This doctoral dissertation explores how computational modelling, simulation and sensitivity analysis techniques can be used to appropriately represent occupants and their behaviors when predicting the energy and comfort performance of buildings. More specifically, the aim of this work is to develop a computational methodology that can be used to facilitate the model selection of various OB aspects in order to achieve fit-for-purpose OB modelling.

The methodology comprises four main steps. The first three steps (the Impact Indices Method, the Diversity Patterns Method, and the Mann-Whitney U test) are developed to identify which OB aspects are truly influential for the simulation output. These three steps can be seen as a sequence of increasingly complex sensitivity analyses. The fourth step concerns dealing with the influential OB aspects. Two options are assessed: i) reducing the epistemic uncertainty connected with the influential aspects; and ii) refining the modelling approach adopted to represent

vii

influential OB aspects. In this dissertation, two refined modelling approaches are explored: increasing the model complexity using stochastic models, and the use of high-resolution real data. Each step of the methodology has been iteratively developed through virtual experiments and reliability tests. The complete methodology is coded in a numerical computing environment coupled with a BPS software.

The methodology is applied to existing buildings or building components to test its capabilities. A number of design questions are explored, corresponding to different phases in the building lifecycle. More specifically, the first application case study concerns the appropriate modelling of occupant presence during the R&D process of adaptive shading control strategies. A second case study illustrates the potential of the methodology to reach fit-for-purpose OB models during the design stage. Lastly, the Impact Indices Method is tested for energy performance contracting (EPC) of three existing Dutch office buildings.

The outcomes of this research show that the computational methodology presented here is able to produce BPS models that include an adequate (fit-for-purpose) representation of occupants and their behavior, while satisfying the purpose of the simulation in an efficient and effective way. The models obtained with the methodology proposed here allow for more informed decision- making compared to traditional OB modelling techniques. When compared to calibrated models, the fit-for-purpose models require fewer input parameters without compromising the desired predictive ability. For these reasons, the computational methodology is effective in supporting simulation users in the selection of OB models and shows good potential to improve the traditional building design process.

viii

Nomenclature

List of Abbreviations

ABM Agent-based Model ACH Air Changes per Hour AHU Air Handling Unit AIC Akaike's information criterion ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers BBN Bayesian Belief Network BCVTB Building Controls Virtual Test Bed BEP Building Energy Performance BIC Bayesian information criterion BPS Building Performance Simulation CIBSE Chartered Institution of Building Services Engineers DA Daylight Autonomy DGI Daylight Glare Index DHW Domestic Hot Water DOE Department of Energy EMS Energy Management System EP Energy Performance EPBD Energy Performance of Buildings Directive EPC Energy Performance Contracting EPD Equipment Power Density ERL EnergyPlus Runtime Language ESCO Energy Service Company FFP Fit-for-purpose FFP-OBm Fit-for-purpose Occupant Behavior Modeling FMU Functional Mockup Unit GLM Generalized Linear Models GMM Gaussian Mixture Model GP Genetic Programming HABIT Human and Building Interaction Toolkit HDD Heating Degree Day HMM Hidden Markov Model HVAC Heating, Ventilation and Air-Conditioning IEA International Energy Agency IEA EBC International Energy Agency in Buildings and Communities Program IEQ Indoor Environmental Quality

II Impact Index LDC Load Duration Curve LMM Linear Mixed Effects Models LPD Lighting Power Density MAS Multi-agent simulation MC Monte-Carlo MDP Markov Decision Process MuMo Multiple Modules MWW Mann-Whitney-Wilcoxon OAT One-at-a-time OB Occupant Behavior obXML Occupant Behavior Extensible Markup Language PD Power Density PI Performance Indicator PMV Predicted Mean Vote SA:V Surface Area to Volume [m2/m3] SHGC Solar Heat Gain Coefficient SHOCC Sub-hourly Occupancy-based Control UGF Utilized gains fraction WOH Weighted Overheating Hours WWR Window-to-wall Ratio

List of Symbols c OB model complexity

CD Window discharge coefficient cFFP Fit-for-purpose OB model complexity cmin Minimum OB model complexity (= c0) cn OB model complexity for iteration n COP Coefficient of performance cp Specific heat capacity [J/kgK] D Density [kg/m3] g Link function g-value Solar heat gain coefficient [-] h Occupied hour

IIblinds, C Impact index for blind use, cooling mode [-]

IIblinds, H Impact index for blind use, heating mode [-]

IIequipment, C Impact index for equipment use, cooling mode [-]

IIequipment, H Impact index for equipment use, heating mode [-]

IIlights, C Impact index for light use, cooling mode [-]

IIlights, H Impact index for light use, heating mode [-]

IIpresence, C Impact index for presence, cooling mode [-]

IIpresence, H Impact index for presence, heating mode [-]

IITsp Impact index for temperature setpoint setting [-] k Thermal conductivity [W/mK] n Iteration number nRuns Number of runs p Probability

QG, Eq Heat gains due to equipment use [J]

QG, Inf Heat gains due to infiltration [J]

QG, Int Heat gains due to interzone air transfer [J]

QG, Lights Heat gains due to light use [J]

QG, Op Heat gains due to conduction of opaque surfaces and other unclassified gains [J]

QG, People Heat gains due to occupant presence [J]

QG, Win Heat gains due to conduction and radiation through windows [J]

QGains, Tot Total heat gains [J]

QL, Inf Heat losses due to infiltration [J]

QL, Int Heat losses due to interzone air transfer [J]

QL, Op Heat losses due to conduction of opaque surfaces and other unclassified losses [J]

QL, Win Heat losses due to conduction and radiation through windows [J]

QLosses, Tot Total heat losses [J]

QNC HVAC input sensible cooling [J]

QNH HVAC input sensible heating [J] R2 Statistical coefficient of determination [-] R Thermal resistance [m2K/W] S State s Thickness [m] t Time step

Tin Indoor temperature [°C]

Tmax Maximum allowed temperature [°C]

Top Operative temperature [°C]

Tsp Temperature setpoint [°C] U Heat transfer coefficient [W/m2K] x Stochastic model predictors β Stochastic model parameters ∆T Temperature difference [°C] ∆Tin-out Temperature difference between indoor and outdoor environment [°C] η Efficiency OR utilization factor [-] μ Mean value [-] ρ Solar reflectance [-]

ρvis Visible reflectance [-]

σ Standard deviation [-] τ Solar transmittance [-]

τvis Visible transmittance [-]

List of Pictograms

Occupant presence

Light use

Blind operation / Shading with manual blinds

Window operation

Temperature setpoint setting

Equipment use

Domestic Hot Water (DHW) use

Absence of shading

Shading with overhang

xii

1 Introduction

‘In Science – in fact, in most things – it is usually best to begin at the beginning. In some things, of course, it’s better to begin at the other end. For instance, if you wanted to paint a dog green, it might be best to begin with the tail, as it doesn’t bite at that end. And so -’

Lewis Carroll, Sylvie and Bruno Concluded

This chapter presents the background knowledge necessary to comprehend this thesis and to motivate this research in Section 1.1 to Section 1.3. There are four main topics of interest: i) the energy use in the built environment; ii) building performance simulation (BPS) as a tool to enable the design and operation of traditional as well as energy-efficient buildings; iii) the uncertainties connected to BPS models, among which occupant behavior (OB) plays a central role; and iv) the profound inconsistency among OB modeling in practice and in the academic community. Further, Section 1.4 discusses the main aim of this thesis: the development of a methodology to support appropriate OB model selection, and the objectives connected with such an aim. Section 1.5 summaries the relevant contributions of this thesis to the state-of-the-art. The thesis outline is given in Section 1.6.

1.1 Energy use and comfort in the built environment

Energy consumption of buildings has dramatically increased over the past decade due to urban population growth, more time spent indoors, bigger floor area per capita and improved indoor environmental quality standards (IEA 2017). People in developing countries are adapting their comfort expectations to western levels, further increasing the demand for energy in buildings (Amecke et al. 2013). For these reasons, cities have become a major frontline in our struggle to save energy and combat human-related climate change. A number of international agreements (such as the Energy Performance of Buildings Directive (EPBD) in Europe) seek to control the energy use in buildings around the world, today estimated to represent a significant share of the total energy used, for example, in India (35%), in China (37%), and in Europe and United States (40%) (Cao, Dai, and Liu 2016). Global and local building policies set stringent energy goals for new and existing buildings with the aim of reaching near zero energy and carbon emissions. At the same time, concerns exist about the comfort-related risks arising from low-energy, highly insulated buildings in a changing climate (Chvatal and Corvacho 2009; McLeod, Hopfe, and Kwan 2013; Sameni et al. 2015). In this context, predicting, assessing, and verifying the energy and comfort performance of buildings has become vital at different stages of the building design cycle, from conceptual design to post-occupancy evaluation.

1.2 Building performance simulation

Building performance simulation (BPS) software are useful tools to calculate the energy and comfort performance of buildings. Their use has become widespread in supporting the design and operation of buildings, which are becoming more and more complex and energy-efficient, without compromising indoor environmental quality (IEQ). BPS is defined as the use of computational, mathematical models to represent the physical characteristics and control strategies of a building design or actual building (or buildings) and its (their) systems (Hong, Langevin, and Sun 2018). Available BPS tools differ in terms of ease-of-use, accuracy, required inputs, and level of complexity (Hensen and Lamberts 2011). They vary from simple, reduced-order models based on physical models, to more detailed, high-order dynamic simulations. Typically, a BPS model is composed of a number of sub-models, or sub-domains, such as thermal model, daylight model, airflow model, heating, ventilation, and air conditioning (HVAC) model, and occupant behavior (OB) model (Fig. 1.1). Therefore, even within a single BPS tool, different levels of model complexity can be achieved according to the chosen sub-models (Gaetani, Hoes, and Hensen

2016a). Different tools and sub-models may be preferable depending on the aim of the simulation and on the building design stage (CIBSE 2015). BPS models are typically deterministic, i.e. they contain no random variables and no degree of randomness. These models have known inputs and they result in a unique set of outputs (for example, the amount of yearly energy use in kWh/m2 or the indoor temperature distribution in °C). However, it frequently occurs that not all inputs are known with certainty by the simulation user. As a result, the BPS outputs rarely correspond to reality because the models necessarily include uncertainty. The discrepancy between simulation results and reality is often referred to as the performance gap (van Dronkelaar et al. 2016).

Geometry Thermal Model

Daylight Model Thermal Energy Properties Performance

Airflow Model

Weather Comfort HVAC Model Performance

Use Scenario OB Model

Fig. 1.1: Overview of exemplary input and modeling sub-domains needed to achieve building energy and comfort performance predictions

The reliability of BPS tools’ outputs is dependent on the quality of the BPS models and on their input, following the garbage in = garbage out principle. However, even though much effort may be put into the model, the BPS models are still subjected to uncertainty. In particular, the following types of uncertainties are distinguished (de Wit and Augenbroe 2002):

- Numerical uncertainty arising from the numerical techniques, model discretization, and computational hardware used to run a mathematical model;

- Modeling uncertainty resulting from the fact that all models are imperfect representations of reality;

- Input uncertainty associated with the unknown or uncertain input parameters.

This research focuses on the third type of uncertainties – the input uncertainties –, which

represent the lion’s share of the discrepancy between simulation results and reality (Macdonald, Clarke, and Strachan 1999). Other types of uncertainty, such as modeling uncertainty, are somewhat avoidable, for example by improving the quality of the models. Conversely, the uncertainty associated with model inputs may not be reduced if the information is not available. Input uncertainties are themselves categorized into two main groups: i) epistemic uncertainties (from Greek epistēmē, ‘knowledge’), which are connected to knowledge and can be reduced by gathering more data; and ii) aleatory uncertainties, which are associated with the unpredictable boundary conditions and use scenarios of a building, such as weather and OB.

OB-related uncertainties in BPS

Numerous researchers have identified OB as the single most important source of uncertainty in BPS (e.g., Li, Hong, and Yan 2014; Yan and Hong 2018; Yan et al. 2017). OB includes the physical presence of occupants, as well as their actions that have an influence on the energy use and thermophysical behavior of a building, such as using plug-loads, operating windows and shading devices, setting thermostats, etc. Indeed, how occupants behave within buildings can have a dramatic effect on the building’s energy and comfort performance. Fig. 1.2 shows the difference between the highest and lowest consumers in heating energy (Fig. 1.2a) and total energy (Fig. 1.2b) in terms of ratio in identical buildings where the sole difference are the occupants.

(a)Chicago (b) Chicago #1 Washington D.C. Minneapolis #1 Atlanta Fairbanks #1 San Francisco Chicago #2 Median: 2.4 Miami Minneapolis #2 Chicago Median: 2.2 Fairbanks #2 San Francisco #1 Chicago #1 #2 Phoenix #2 Field Studies Simulation Studies Field Studies Simulation Studies [7] [6] [5] [4] [3] [2] [1] [14] [13] [12] [11] [10] [9] [8] 0.0 5.0 10.0 15.0 20.0 0.0 2.0 4.0 6.0 8.0 10.0 Ratio of Highest Energy Consumer : Lowest Energy Consumer (Heating) [-] Ratio of Highest Energy Consumer : Lowest Energy Consumer (Total) [-]

Fig. 1.2: Ratio of highest energy consumer to lowest heating energy (a) and total energy (b) consumer in identical buildings due to OB only. Climate and #n indicate where different buildings were investigated in the same publication

Fig. 1.2a includes two simulation studies (Gaetani, Hoes, and Hensen 2016 [1]; Lin and Hong

2013 [2]) and five field studies (Morley and Hazas 2011 [3]; Gram-Hanssen 2011 [4]; Maier, Krzaczek, and Tejchman 2009 [5]; Gaunt and Berggren 1983 [6]; Andersen 2012 [7]), while Fig. 1.2b presents the outcome of three simulation studies (Wang, Mathew, and Pang 2012 [8]; Hong and Lin 2012 [9]; Clevenger, Haymaker, and Jalili 2014 [10]) and four field studies (Norford et al. 1994 [11]; Lutzenhiser 1993 [9]; Muroni 2017 [12]; Sonderegger 1978 [13]). On average, a ratio of about 1:4.5 (heating energy) and 1:3 (total energy) between the buildings using the least and the most energy was observed due to differences in OB alone. Conversely, the median values (reported in the figure) are 2.4 and 2.2 for heating energy and total energy, respectively. These values give an impression of the potential uncertainty of BPS output if variation in OB is not taken into account in a suitable way. However, Fig. 1.2 also shows the high variability of OB influence among different studies. The study showing the highest discrepancy is by Andersen (2012), who collected data in 290 identical townhouses located in Copenhagen, Denmark. The data indicated that the heating consumption ranged from 9.7 kWh/m2 to 197 kWh/m2, or a ratio of about 20. Despite being identical, the townhouses must have had entirely different OB patterns in terms of number of occupants, presence and behaviors. On the other hand, Lin and Hong (2013) found through simulation that the impact of OB in a large office building in Fairbanks, US led to a -19% to +22% (or a ratio of 1.5) discrepancy with the baseline model. In this building, most of the variation due to OB was related to the setting of the heating temperature setpoint. The different studies investigated diverse buildings, climates, occupants, and available interactions between occupants and building. This research demonstrates that it is impossible to define a priori what the influence of OB on building performance may be. What is important to note, however, is that the influence of certain aspects of OB on building performance increases as building envelopes and systems are optimized, technical performance standards become tighter and low-energy systems become more widespread (Clevenger and Haymaker 2006). In other words, the more energy-efficient the building, the more attention needs to be paid to the impact of OB on building performance.

Perception of OB-related uncertainties

A large survey was carried out among BPS users to determine if the uncertainty in OB and its modeling was also perceived as a relevant topic outside the academic community (O’Brien et al. 2016). The majority of the survey’s respondents agreed that OB was the most important source of discrepancy between BPS predictions and measurements in real buildings, confirming that this issue was also seen as relevant among practitioners (Fig. 1.3).

Occupant behavior Controls and operations Input assumptions in BPS tools HVAC system functionality Weather data Construction quality Raw material properties Building component/equipment quality Numerical approximations in BPS tools Other

0 20 40 60 80 100 120 140 Number of respondents

Fig. 1.3: Survey results regarding 274 participants’ attitude about the leading source of discrepancy between BPS predictions and measurements

Once the significant effect of OB on building performance has been recognized, a number of options are available. One could choose to accept the performance variations caused by OB as inevitable, or one could try to change the current situation, for example by encouraging behavioral changes towards energy-efficiency. The first option is currently widespread, but it severely undermines the reliability of BPS tools, and the possibility of using them for decision- making. The second option typically involves social scientists rather than building engineers. A third option is available, i.e. trying to include such variations in simulations, so that the effect of OB can at least be predicted and taken into account when making decisions. Appropriate modeling of OB allows such variations to be included in BPS to achieve reliable predictions. However, while BPS tools are extremely advanced in predicting the functioning of building envelopes and systems, they are still lacking in the ability to properly address how occupants behave within buildings.

1.3 Occupant behavior modeling in BPS: research vs. practice

Despite understanding the relevance of OB modeling in BPS, most simulation users still rely upon fixed, a priori schedules and other simple IF-THEN models to describe occupant presence and their behavior. Schedules and IF-THEN models represent a completely foreseeable, repetitive environment where changes only occur because of predefined shifts in a one or more

variables (typically, time or environmental triggers). They represent the lowest level of complexity of the OB sub-model, and are often dictated by standards such as ASHRAE 90.1 (ASHRAE 2013). Simulation experts agree that OB modeling features should be improved in BPS tools, but they object that there are no better available modeling options, nor guidance for model selection (O’Brien et al. 2016). In response to these concerns, researchers have been developing a growing number of increasingly complex models (for example, stochastic, agent-based models). Nevertheless, these models are subject to questions about their validity and seldom find application in practice (Gaetani, Hoes, and Hensen 2016a). Moreover, simulation users are not supported in the model selection for a specific case, which leads to a negligible application of research models in practice. An oversimplified approach to OB modeling may lead to a number of pitfalls such as: designing buildings that do not achieve the desired performance; over- or under-sizing of building systems; and failing to optimize building design and control for actual occupant presence and behavior. On the other hand, using complex, often stochastic models, requires specific expertise and large expenditures of time and money, which may not be justified by the improvement in the simulation’s output quality. Other applications that could benefit from an appropriate representation of OB include: risk assessment; controls and operations; demand-side management; building flexibility; occupant comfort analysis; and simulation-based occupant- feedback mechanisms. A growing understanding of the importance of the human dimension from the early stages of the design phase is identifiable (e.g., (“IEA EBC Annex 79” 2019)). Consideration of building/occupant interactions during the design phase has been shown to lead to more efficient and comfortable designs (Bullinger et al. 2010). Among the international projects, the upcoming IEA EBC Annex 79 proposes a paradigm shift in the way occupants are modeled in buildings, towards occupants as active decision-making agents who respond to indoor environmental conditions (Fig. 1.4).

Conventional occupant modeling Next generation occupant modeling

Occupant(s)

Acoustic Visual comfort comfort Adaptive actions (windows, Occupant(s) thermostats, blinds, lights, etc.) IEQ Heat, moisture, CO2 gains

Energy and Interface (considering design, Operation Thermal Indoor Air Building comfort context, logic) conditions comfort Quality performance

Energy and Operation Building comfort conditions performance Fig. 1.4: Paradigm shift in the way occupants are modelled in buildings: from occupants as passive sources of heat, moisture, and emissions to active decision-making agents who respond to indoor environmental conditions. Adapted from (“IEA EBC Annex 79” 2019)

Moreover, the currently available technology renders it possible, for the first time, to quantify how efficient a building is in respect to the service it delivers, i.e. providing a comfortable and productive environment for occupants. This fact led researchers (O’Brien et al. 2017) to argue that building performance should be evaluated from an occupant-centric perspective. These initiatives demonstrate how occupant modeling is likely to play an increasingly important role for building design and operation in the near future. Due to all of the reasons mentioned above, there is a need to bring together research and application by providing guidance for practitioners about OB model selection. Doing so would also increase the probability that the efforts made in the academic community to develop models are rewarded by the successful application of the models in practice.

1.4 Aim and objectives

This PhD study seeks to improve the state-of-the-art by developing, testing and evaluating a computational approach that can be used to support informed decision-making regarding OB model complexity for building energy and comfort performance simulation. In particular, this study puts forward the concept of “fit-for-purpose” OB modeling, whereby an appropriate approach to modeling of different aspects of OB is strictly directed by the object and purpose of the BPS simulation. Four objectives are directly related to this research aim:

- To develop an effective strategy for the selection of model complexity of various OB aspects;

- To integrate such a strategy into a computational building performance simulation tool and to test its reliability by means of virtual experiments;

- To illustrate, using virtual and real case studies, how the developed strategy leads to efficient, informed decision-making and improves traditional modeling techniques;

- To better understand the relationships between various aspects of OB and building (energy and comfort) performance in a number of demonstration examples.

1.5 Contributions

The single most important contribution of the work connected to this thesis is the development of a methodology – the fit-for-purpose occupant behavior modeling (FFP-OBm) strategy – that supports practitioners and BPS users in general in the choice of appropriate OB models. As shown later in this thesis (Chapter 2), there is a large amount of studies proposing new, increasingly complex, OB models, but a very limited number of authors address the issue of model selection. The few studies that do address this issue do so in a very abstract, theoretical way, rather than providing support for practitioners. Moreover, in OB modeling, virtually all previous studies have been concerned with a case study bound demonstration of models’ predictive ability. This thesis is the first attempt to rigorously address the issue of OB model complexity such that it can be generalized to applications other than the ones presented in research papers. The integration of the FFP-OBm strategy in commonly used BPS software led to the creation of knowledge in the field of OB modeling for BPS. A number of findings emerged from applying the strategy to virtual and real case studies:

i) the appropriate OB model complexity is indeed case-specific, it cannot be selected a priori, and it depends on the object and purpose of the simulation;

ii) the fit-for-purpose OB model complexity varies per aspect of OB;

iii) higher complexity models are often not needed for decision-making, and are subject to questions regarding their validity and applicability; however, consciously addressing the issue of OB model selection as opposed to using traditional, oversimplified techniques is crucial for successful decision-making in building design and operation;

iv) the FFP-OBm strategy is able to produce BPS models that are more lightweight than purely data-driven models without compromising their desired predictive ability.

The research connected to this thesis was widely disseminated during the process by means of attending scientific conferences and publishing journal papers (see Publication List). These contributions were able to induce a shift of mindset in the OB community: the “fit-for- purpose” OB modeling approach was seen as an improvement on to “the more complex, the better” rationale. This shift of mindset was clearly visible during the IEA EBC Annex 66 project and it was advertised as one of the main deliverables of the project (Yan et al. 2017). The

integration of fit-for-purpose model selection in available OB modeling frameworks is today considered necessary (Hong, Langevin, and Sun 2018). For these reasons, this thesis provides a significant contribution towards appropriate OB modeling, and moves forward the state-of-the-art in the field.

1.6 Thesis outline

Chapter 2 concerns the state-of-the art in OB modeling. In particular, it presents the challenges connected with the sub-domain of OB in the wider context of BPS modeling. The mathematical formalisms currently adopted to represent OB are illustrated and a wide range of models are reviewed. Moreover, the few existing studies comparing the predictive ability of models characterized by different levels of complexity are summarized. The aim of the chapter is to familiarize the reader with the abundant number of OB models and their connected issues, as well as to highlight the research gap of model selection.

Chapter 3 presents the research methods that were used during the iterative process of developing, applying and evaluating the FFP-OBm strategy. This chapter includes the theoretical concepts on which the work is based, as well as the scoping of the work and computational tools used to implement the strategy.

Chapter 4 illustrates the first step of the FFP-OBm strategy: the Impact Indices Method. A virtual experiment is used to demonstrate the method. The reliability of the Impact Indices Method is assessed by comparing the method’s outcome with one-at-a-time sensitivity analysis.

Chapter 5 introduces the second and third steps of the FFP-OBm strategy: the Diversity Patterns Method and the Mann-Whitney U test. Just like in Chapter 4, a virtual experiment is used to demonstrate the method and its reliability. In particular, higher complexity models are applied to both influential and non-influential aspects to show their effect on the results.

Chapter 6 provides an overall view of the FFP-OBm strategy. This chapter includes all steps that were not previously discussed and applies the overall strategy to a simple virtual case study. The chapter aims to demonstrate the entire FFP-OBm strategy process and to illustrate the benefits of the strategy for design decision-making.

Chapter 7 presents the results of the application of the strategy: the strategy is here applied to real buildings, and its outcome is compared with real data. In particular, one case study applies the strategy to the R&D process of innovative solar shading systems; a second case study relates the strategy outcome with high-resolution real data from a university office of TU Wien; and two further case studies use the Impact Indices Method during Energy Performance Contracting

(EPC) of office buildings.

Chapter 8 discusses the main outcomes of the thesis, as well as its limitations and further work.

2 Occupant behavior in building performance simulation

While physics and mathematics may tell us how the universe began, they are not much use in predicting human behavior because there are far too many equations to solve. I'm no better than anyone else at understanding what makes people tick, particularly women.

Stephen Hawking

This chapter discusses the fundamentals of OB modeling in BPS. First, in Section 2.1, the main aspects of OB are discussed in relation to their effect on building energy and comfort performance. Occupant presence, adaptive and non-adaptive behaviors are treated separately due to their intrinsically different nature. Then, in Section 2.2, the existing modeling formalisms to represent OB are presented, with a focus on their complexity. Section 2.3 is a literature review of the available OB models. Section 2.4 reports the results of existing studies that compare the predictive ability of different modeling formalisms. The issue of model validity is discussed in Section 2.5. To conclude, Section 2.6 highlights the knowledge gaps derived from the presented literature review.

2.1 Modeling the influence of occupant behavior on building performance

As mentioned in Chapter 1, OB modeling is just one of the modeling sub-domains in BPS, alongside thermal, daylight, airflow and HVAC models. The modeling sub-domains are, however, closely interrelated. For example, the geometry of the building and its surrounding, the thermophysical properties of its windows and shading devices, as well as the description of the weather, concur in defining the use of shading devices. If the modeling formalism for the operation of shading devices is more complex, there may exist a feedback loop with other sub-domains such as thermal, daylight and HVAC models (Fig. 2.1).

Geometry Thermal Model

Daylight Model

Thermal Airflow Model Energy Properties Performance

HVAC Model

Occupancy Model Weather Comfort Use of shading Performance devices

Use of lights

Use of equipment and water appliances Use Scenario HVAC system control Use of windows and doors

Fig. 2.1: The example of use of shading devices: inter-connection with input and modeling sub-domains exist in advanced modeling formalisms

The prediction of the effects of OB on the energy and comfort performance of a building necessarily depends on the adequate representation of all inputs and modeling sub-domains. Modeling the interactions between occupants and buildings is a complex task. The goal of building performance simulation is to investigate energy- and comfort-related building performance indicators. As such, this thesis only considers behavioral aspects that have a direct effect on either building energy or comfort. This research does not seek to understand the

physiological and psychological needs that motivate occupants’ actions, but rather it concentrates on the potential effects of such actions (and presence) on building performance. But how do occupants influence building performance? Occupants affect their surrounding environment by simply being there. They influence the building heat balance by emitting sensible and latent heat, but they also produce water vapor, CO2 and pollutants. Occupants are not mere spectators of their surroundings, but rather they react to them in a number of ways, from physiological adaptation (e.g., sweating or shivering), to personal adaptation (e.g., adjusting their level of clothing, changing activity level or consuming a hot/cold beverage), to environment adaptation (e.g., adjusting the thermostat, operating a fan, opening or closing windows and blinds). The adaptations aiming at restoring comfort are named adaptive behaviors (de Dear and Brager 1998). Other than by means of adaptive behaviors, occupants may affect the building energy consumption by the activities they perform, such as computer work in offices or cooking in dwellings. The energy-related behaviors that enable an activity are named non-adaptive behaviors (O’Brien and Gunay 2014). The coming sections concern modeling occupant presence (Section 2.1.1), adaptive behaviors (Section 2.1.2) and non-adaptive behaviors (Section 2.1.3) for building energy and comfort performance.

Modeling occupant presence

Occupant presence has a dual impact on the building energy and comfort performance: i) directly, as a source of metabolic heat gains, humidity, CO2 and pollutants, and ii) indirectly, as a prerequisite trigger for adaptive and non-adaptive energy-related behaviors. The assumptions made while modeling occupants also directly impact the resulting comfort levels. Occupants’ direct impact on the heat balance occurs as heat is generated in the human body by oxidation at a rate called the metabolic rate. The heat is then dissipated from the body surface and respiratory tract by a combination of radiation, convection, and evaporation. The number of occupants present in a zone at a given time is multiplied by the metabolic rate to obtain the internal heat gains due to people. The metabolic rate depends on the representative occupant type. For example, EnergyPlus employs an average adjusted metabolic rate to a typically mixed group of occupants, where adult females and children have a metabolic rate of 0.85 and 0.75 times that of an adult male, respectively. The metabolic rate can also be input in the model in the form of activity level (for example, expressed in W/person). Values for activity levels can range from about 70 W/person for sleeping to 900 W/person for intense sport activity such as competitive wrestling (ASHRAE 2013). The relative proportion of sensible (radiation plus convection) and latent (evaporation) heat from occupants is a complex function of the metabolic

rate and the environmental conditions. In fact, physiological adaptation alters the sensible/latent heat component balance (e.g., sweating). For example, in EnergyPlus, a polynomial function is used to divide the total metabolic heat gains into sensible and latent portions (EnergyPlus 2019). However, the simulation user can override this value. A further required input is the fraction of radiant heat, which is multiplied by the total sensible energy load emitted by occupants to obtain the long wavelength radiation gain from human beings in a zone. The residue of the sensible energy load is assumed to be convective heat gains. Whether and how many occupants are present in a zone also has an indirect impact on buildings’ energy performance, as this information is often needed as a model input to other aspects of OB. For example, the number of occupants present may be multiplied by the total capacity of appliances installed in an office in order to derive the amount of energy use for appliances at a given time. The comfort performance of a building is influenced, apart from the number of people present in the building, by the assumptions made concerning their activity levels, work efficiency, clothing insulation, and the zone air velocity. It can be appreciated how a number of different parameters concur in providing an accurate evaluation of the influence of occupant presence on energy and comfort performance. These parameters can often be manually input within BPS software, giving the user the possibility of fully personalizing his/her model. However, in the majority of cases standard assumptions are made (O’Brien et al. 2016). This study focuses on the models which deal with predicting the occupant presence levels of a building, expressed as percentages of the maximum occupant presence, occupant presence status (present vs. absent), or number of occupants. These models are referred to as occupancy or occupant presence models. Nevertheless, a high number of variables are needed to achieve an accurate estimation of the impact of occupant presence on energy and comfort performance. Hence, despite having correct information about the number of occupants, the simulation assessment could still contain errors resulting from the great number of uncertain parameters.

Modeling adaptive behaviors

The introduction to this chapter considered how occupants adapt to their environment in many different ways. Physiological adaptation occurs in every living being and plays a predominant role in the evolution of the species (O’Neil 1998). In this study, however, the term adaptive behavior refers to all actions consciously performed by people to alter their surrounding environments to “maintain or restore their comfort” (de Dear and Brager 1998). Comfort is subjective and shaped by a number of sociological and psychological factors. Rarely do two

people feel comfort/discomfort in the exact same conditions, but there may be macro-trends. People can be clustered according to their attitude towards adaptation, e.g. passive occupants may feel the need to adapt their environment less often than active occupants. Differences in attitude towards adaptation could also result from physiological attributes, e.g. women may start feeling cold at a higher temperature than men (Kingma and van Marken Lichtenbelt 2015). Moreover, adaptive behaviors could be influenced by other contextual factors such as proximity to the device that can be adjusted (e.g., proximity to a window in an office environment), or to one’s position in a social hierarchy. There exist OB models that attempt to account for occupant diversity, a much-discussed topic in the OB community (O’Brien et al. 2016; Haldi 2013). It is evident that modeling adaptive behaviors is no easy task due to the large number of influential factors. The following sections concern those actions that have the largest impact on energy and comfort performance, i.e. shading device or blind operation, window and door operation, light use, and the HVAC system control.

2.1.2.1 Shading device or blind operation

Shading devices affect the window system transmittance and glass layer absorptance for short- wave radiation and long-wave (thermal) radiation. They influence the building energy performance by reducing solar gains, and possibly reducing heat losses (e.g. through movable insulation). Moreover, shading devices may have a secondary effect on building energy performance as they could influence the amount of required electrical lighting. They affect the visual comfort performance of a building by controlling daylight availability, transmitted luminous flux and views to the outside. The effect of shading devices depends on the shade location (interior, exterior or between-glass), its transmittance, the amount of radiation absorbed by the device, the amount of inter-reflection between the shading device and the glazing, and the shade position (partially open or closed). The incident solar radiation must be correctly modeled, for example by means of an accurate weather file and by taking into account neighboring shading surfaces. OB models aiming at representing the operation of shading devices or blinds focus on their opening/closing state. The challenge in modeling the use of manual blinds is that people generally close the blinds because of discomfort, thus also affecting the incoming thermal radiation, but they typically fail to open them again when comfort conditions would allow it (O’Brien, Kapsis, and Athienitis 2013).

2.1.2.2 Window and door operation

Windows and doors can be opened in buildings to allow the flow of air from the outdoor or neighboring environment into a zone (Fabi et al. 2012). The airflow affects the hygrothermal conditions of the zone, as well as its indoor air quality. The actual flow rate of ventilation air depends on the window or door open area fraction, on the temperature difference between the zone and the outside/neighboring environment and on the wind speed and direction. In BPS

tools, this is modeled using a number of coefficients. The coefficients could be default values or input manually by the user after a detailed analysis of the ventilation situation. The local outdoor temperature and wind speed are a function of the height of the zone. Furthermore, neighboring buildings or phenomena such as urban heat islands may have a strong impact on wind speed, direction and outdoor temperature (Ramponi, Gaetani, and Angelotti 2014). OB models concerning window and door opening focus on predicting the state (open or closed).

2.1.2.3 Light use

Lights are used in buildings to guarantee an adequate level of visual comfort to occupants. The use of lights has a direct impact on the amount of electricity used in the building. The electrical input to lighting ultimately appears as heat, which contributes to zone loads or to return air heat gains. A number of assumptions must be made regarding the lighting system characterization. The lighting level is defined as a maximum value (or lighting power density (LPD)), which is then multiplied by the appropriate schedule to calculate the final value. The OB models dedicated to manual light use typically attempt to define appropriate schedules, which describe whether lights are on, off, or dimmed. As with blinds, people tend to turn on the lights because of discomfort (e.g. inadequate illuminance levels), but often fail to turn them off when comfort conditions would allow it (e.g. adequate illuminance provided by daylight).

2.1.2.4 HVAC system control

HVAC systems may be needed to reach thermal comfort and ensure acceptable indoor air quality in buildings. HVAC systems are modeled within BPS software with sets of equations that define the performance, controls and operation strategies of chillers, boilers, thermal storage systems, distribution components, etc. The parameters of these equations can often be manually input by the simulation user, with different degrees of freedom according to the software. All system specifications, the modeling of the outdoor and indoor environment, and the system operation setting define the amount of energy required by the HVAC system and the level of comfort provided. Typically, OB models concerned with the control of HVAC systems focus on the setting of temperature setpoints and on system operation schedules.

Modeling non-adaptive behaviors

In this study, those actions that are connected to performing an activity and that influence the energy use of buildings are referred to as non-adaptive occupant behaviors. Contrary to adaptive behaviors, they do not occur to restore comfort, but rather to enable an activity. Examples of non-adaptive occupant behaviors include use of equipment and domestic hot water (DHW). As

opposed to adaptive behaviors, modeling non-adaptive behaviors does not depend on environmental variables. Instead, the determinants of non-adaptive behaviors are the presence of occupants and their (time-based) needs to perform various activities.

2.1.3.1 Equipment and domestic hot water (DHW) use

The use of electrical and gas equipment and DHW enable occupants to perform a number of activities. Typically, in office environments people use a large number of electrical appliances to accomplish their day-to-day tasks. In households, a share of the cooking-related activities may be performed with equipment that consumes gas. DHW is required to satisfy basic comfort requirements for occupants in buildings, such as body care. Equipment and DHW use affect the energy performance of a building because of gas and/or electricity consumption, as well as the emission of longwave radiation and water vapor that impact the heat balance. OB models for equipment and DHW use typically concern the schedule of actual use. Two main approaches exist for simulating electrical appliances: bottom-up models, which explicitly model each appliance resulting in a total load by aggregation; and top-down models, which consider all appliances as a black box energy sink based on actual data to predict future use.

Concluding remarks

This section introduced the distinction in terms of modelling between occupant presence, adaptive and non-adaptive behaviors (Fig. 2.2).

n A

- d a

a d

p a

t p i

v t

e i

v b

e e

b h

e a

h v

a i o

v r i s o r s

Fig. 2.2: The categories of OB: occupant presence, adaptive behaviors (light use, shading device or blind operation, window and door operation, HVAC system control) and non-adaptive behaviors (equipment use and DHW use)

A meaningful representation of building energy and comfort performance depends on a high number of parameters. Such parameters concern the quality of input to the model, such as weather file or description of thermo-physical properties, and a number of sub-domains. This thesis focuses on OB modeling, which concerns the behavioral aspects of the building/occupant interaction, e.g. on the blind operation. However, to correctly capture the influence of OB on building performance, it is necessary that the simulation user trusts all input and sub-domains models. The focus of the present research is on the occupant presence, adaptive behavior and non- adaptive behavior models. In the following section, the existing formalisms for occupant presence and behavior modeling are presented. Where otherwise not made explicit, the notation OB models includes both occupant presence and behavior models.

2.2 Overview of widely used occupant behavior modeling approaches

All models are wrong, but some are useful. George E. P. Box

The existing OB modeling formalisms can be categorized according to their complexity (Polinder et al. 2013). In this study complexity is defined, following Zeigler, Kim, and Praehofer (2000), as the amount of detail in a model, which in turn depends on its size and resolution. Size refers to the number of components in a coupled model, while resolution refers to the number of variables in the model and their precision or granularity (Fig. 2.3).

size = number of components ...

resolution = number of states per component

size x resolution = detail = complexity

Fig. 2.3: The definition of complexity according to (Zeigler, Kim, and Praehofer 2000)

The following sections consider the different modeling formalisms with increasing complexity, from schedules to agent-based modeling.

Schedules, deterministic and non-probabilistic models

Occupants and their behaviors are typically modeled in BPS software in an over-simplified way by means of fixed profiles or schedules. Schedules are time-based fractions from 0 to 1 that act as multipliers of maximum quantities to define actual internal gains due to occupant presence, light use, equipment use, etc. For example, occupants are assumed to be present at their workspace every weekday between 8 am and 6 pm, with an hour lunch break at 12 pm. Schedules are defined independently of the predicted conditions during the simulation. Hence, they represent a simplified scenario where the building operation is very predictable according to day types. Schedules can either derive from standards or from observation-based statistically aggregated data. They can include deterministic rules, where actions are perceived as direct consequences of one or more drivers. This formalism is often used to model adaptive behaviors such as the use of shading devices and windows or doors, for which a schedule that depends solely on time would appear unreasonable (e.g., all windows are always, instantaneously opened when the indoor temperature reaches 26 °C). Such models are commonly referred to as deterministic models, and they average out the diversity of individuals, spaces/locations or time. Therefore, they represent environments where the modeled behavior is averaged and foreseeable. While the size of the model is not directly affected, deterministic models add granularity by specifying various behavioral triggers, hence increasing the model resolution and the complexity. Data-based models are determined by the training profile, which would typically embed information about environmental triggers. However, despite their deterministic nature (known inputs generate known outputs), literature refers to them as non-probabilistic models (Mahdavi and Tahmasebi 2015) to distinguish them from deterministic models. The main drawback of non-probabilistic models is their dependency on the used dataset.

Probabilistic models

In 1979, D. R. G. Hunt published a study named “The use of artificial lighting in relation to daylight levels and occupant presence” in the journal Building and Environment (Hunt 1979), in which he argued that the relationship between occupants undertaking an action and a predictor variable or variables was of a probabilistic nature, rather than a deterministic one. He

tentatively suggested that “the results outlined could form a basis for developing a method to predict the energy consumed by manually operated lighting systems”. In fact, this research turned out to represent the birth of stochastic OB modeling. Stochastic or probabilistic models aim to capture the variability of human behavior. To do so, these models view actions as occurring based on a probability function as a consequence of one or more predictor variables (see Fig. 2.4). An inherent feature of stochastic models is that they require a high number of runs to achieve reliable results (Macdonald, Clarke, and Strachan 1999), and cannot capture (e.g. time-) consistency. The size of this stochastic modelling is invariant as the models still only consider the interactions between the occupants and the building. The resolution increases due to the multiple runs. An overview of the main mathematical formulations for probabilistic models is given in Appendix A: Mathematical formulations for probabilistic models.

Outcome Y Data

Schedule/ deterministic model

Probabilistic/ stochastic model 0.0 0.2 0.4 0.6 0.8 1.0

051015 20 25 30

Predictor X

Fig. 2.4: Comparison of different models obtained after fitting hypothetical binary data (adapted from (Haldi 2010)). Schedules would typically use time as a predictor variable. The solid lines represent diversity in probabilistic models.

Implementation of probabilistic models in BPS tools The outcome of probabilistic models is typically a probability distribution. However, BPS tools are deterministic and require a fixed value of each variable in the model in order to run. Moreover, some OB aspects call for a binary representation (think of lights being switched ON or OFF), as opposed to a continuous variation depending on the predictor value.

Researchers have investigated a number of methods to incorporate probabilistic models in building simulation. A widespread solution is to simulate the building and HVAC model to compute the required predictor variable or variables (e.g., the workplane illuminance). Then, the OB probabilistic model estimates the probability of the occurrence of an event, such as turning the lights ON. In the meantime, a pseudo-random number [0,1] is generated and compared with the probability estimated by the model. If the estimated probability exceeds the random number, the action is undertaken. The building and HVAC model are then updated with the occupant behavior and time advances to the next time step (see Fig. 2.5). As important as the probability of an action to be undertaken (e.g., switching the lights ON) is the reversal of such action (e.g., switching the lights OFF). In the domain of OB modeling the reversal of an action is modeled in two ways: either by defining a hysteresis or deadband in which the action does not change, or by defining a specific function for the reversal of the action (this method is employed in Markov chains). However, some models fail to define the reversal of the action altogether.

Fig. 2.5: Discrete-time simulation algorithm of a behavioral occupant model (Gunay, O’Brien, and Beausoleil-Morrison 2013)

Another issue to consider when applying OB models is whether one is interested in a single mean behavior (standard occupant) or in a set of occupants. In the latter case, the variance of the coefficients of the OB model function must be considered, either by taking high/low values, or by sampling a set of occupants e.g. by means of Monte Carlo distribution.

Agent-based modeling (ABM)

Schedules, deterministic models, non-probabilistic models and probabilistic models represent the conventional simulation framework. A more complex simulation framework is defined by agent- based models (ABMs), which switch from group-level to individual-level behavior predictions. Agent-based models predict the influence of occupants by modeling individuals, their mutual interactions and the interactions with the building. In Fig. 2.4 it would be necessary to add multiple x-axes to correctly visualize ABMs. The huge amount of information typically required by these models (e.g., role of agents, relationship between agents, etc.) may not always be available. Despite this drawback, agent-based modelling has lately commanded the biggest share of publications in this niche (Parys, Souyri, and Woloszyn 2014). Agent-based models markedly increase the size of the model as each individual is separately modeled; the resolution is still based on stochastic modeling and the resulting complexity is very high. Nonetheless, agent-based behavioral models can be characterized by different levels of complexity, depending on the complexity of the sub-models that they include.

Concluding remarks

Section 2.2 provided an overview of the different available formalisms to model OB. The available modeling formalisms are characterized by different levels of complexity, depending on their size and resolution. Table 2.1 is a graphical representation of how modeling size, resolution and complexity are affected by changing the modeling strategy. The overall model complexity for comprehensive OB always derives from the complexity level of each sub-model. Table 2.1 offers a simplified impression, but in reality, there are many more nuances. For example, the size of probabilistic models depends on the number of predictor variables. Moreover, models may have different resolution according to the time-discretization used to define them.

Table 2.1: Overview of the most common occupant behavior modeling approaches according to size, resolution and complexity

Simulation Type of model Size Resolution Complexity framework Schedules    Deterministic    Conventional Non-probabilistic   

Probabilistic/stochastic   

Agent-based Agent-based stochastic   

Various reviews of existing OB models are available (Gunay, O’Brien, and Beausoleil-Morrison 2013; Parys, Saelens, and Hens 2011; Gaetani, Hoes, and Hensen 2016a). The first question to answer when performing a review is how to categorize the models. The most common categorizations are: according to complexity (Polinder et al. 2013), according to object of investigation (occupant presence or type of behavior) (Feng, Yan, and Hong 2015), and according to research approach (Gunay, O’Brien, and Beausoleil-Morrison 2013). Some authors have focused on one level of complexity or one object of investigation only, e.g. Parys, Souyri, and Woloszyn (2014) on agent-based modeling and Feng, Yan, and Hong (2015) on occupant presence models. In the current research, existing models for occupant presence, adaptive behaviors, non-adaptive behaviors, and OB as a whole are treated separately. Within each category, models were classified according to complexity, as the study deals with selecting the most appropriate level of model complexity.

2.3 Literature review of widely used occupant behavior models

In this section, a number of widely used models from literature are reviewed. It is important to note that the current modeling practice is to assume predefined input values, e.g. derived from (ASHRAE 2013). While such an approach is the most widespread (O’Brien et al. 2016), the focus of this section is on research into models that go beyond standard schedules, namely data- driven, non-probabilistic models (referred to as complexity level 0), probabilistic models (complexity 1), and agent-based models (complexity 2). A total of 80 different OB models were reviewed. As well as the level of complexity (0 – 2), and the references for each model, six other criteria are specified, namely: OB aspect; keywords; building type; location; pros; cons. The aspect of OB, building type and location were identified as the main variables to take into account when considering the case-specificity of each presented

model. Keywords provide information about the modeling approach, the existing implementation in BPS software, the validation status and the name of the model (where applicable). Lastly, pros and cons aim to highlight possible improvements to previous models, limitations to overcome, and general features and capabilities. This list does not claim to be exhaustive since new models are constantly being developed. Nevertheless, it gives a good impression of the complexity of the OB modeling research field, of some recurrent issues and of the vast number of aspects that ought to be considered. While the complete review can be accessed at Appendix B: List of existing OB models from literature, some key points are:

i) Many models are available and new models are continuously being proposed (see Fig. 2.6);

14 12 10 8 6

N of models 4 2 0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Year of publication

Fig. 2.6: Review of existing OB models: date of publication

ii) Models are rarely developed as a simulation framework (only 12 of the 80 reviewed models, or 15%): the implementation in BPS mostly takes place on a project-based level, without guidelines for future use or measures for public availability;

iii) Models are generally developed for a specific aspect of OB (with presence and window operation models being the biggest share of publications), but recently there has been an increase of models which address the whole spectrum of OB (see Fig. 2.7);

iv) Households and offices are the most investigated building types (see Fig. 2.7), and a large share of the investigated offices are individual offices;

v) Models are developed for specific locations, predominantly in the northern hemisphere, which might undermine their generalizability to other locations;

vi) Different models are characterized by different specific advantages; in contrast, a number of recurrent limitations are reported, e.g. complexity, case- specificity, lack of validation and calibration, heavy dependency on (outdated) time use surveys.

Other/Mixed 10 Commercial N of models Residential

0 S/S+L W/W+S/ Thermostat Loads DHW W+T

Non-adaptive Whole Presence Adaptive behaviors behaviors OB

Fig. 2.7: Review of existing OB models according to OB aspect: occupant presence, light use, shading device use, window operation, thermostat, loads, domestic hot water (DHW) and whole spectrum of OB

The findings of this overview are in line with the barriers identified by IEA-EBC Annex 66 (Definition and Simulation of Occupant Behavior in Buildings) (Yan et al. 2017). Some notable efforts exist to overcome the lack of integration of OB models in BPS software. For example, Gunay, O’Brien, and Beausoleil-Morrison (2016) provide EMS scripts to integrate a number of OB models in EnergyPlus. The topic of integration of OB models into BPS software has been extensively explored by Hong, Chen, and Belafi (2018), who propose obXML as a co-simulation platform for OB models. Their ready-to-use functional mockup unit (FMU), the obFMU, should solve the issue of limited OB-related functionalities in BPS tools.

Lindner, Park, and Mitterhofer (2017) attempted to implement 24 OB models in BPS and compared a number of methods, including co-simulation, to actuate this implementation. The authors pointed out a number of major problems, mostly connected with the models themselves: i) the contrast between the BPS tools’ deterministic nature and the OB models’ probabilistic nature; ii) the lack of reproducibility of BPS results, often based on a comparison between the probability of an action and a random number to actuate the behavior; and iii) improper behavior without a reversal function or hysteresis. For example, the models of Nicol (2001) and Li et al. (2015) predicted the very high number of 68 and 64 interactions with windows, respectively, between 6 am and 6 pm during a working day in March. However, this prediction resulted from the comparison with the random number rather than from the environmental conditions. Another issue concerns the required variables for implementation: for example, models such as (Page 2007; Newsham 1995; Reinhart 2004) are based on variables that cannot be computed without great effort (such as the workplane illuminance or CO2 concentration).

It has to be noted that the vast number of developed models reveals the vibrant nature of the research field, which seeks to continuously push the methodological boundaries of OB modeling practice. Typically, only a few of the research models will eventually achieve practical applications. The predictions of models characterized by different complexity levels have seldom been compared, and the differences among such predictions currently represent a significant knowledge gap. The few existing examples of inter-comparisons among models of different complexities are the focus of the following section.

2.4 Inter-comparisons of models with different complexity levels

Section 2.3 demonstrated that the existing models are difficult to compare, both because of their case-specific nature and because of the lack of standardized methods to report and compare results (Hong et al. 2015). Nevertheless, some comparisons among different models are available. For example, existing stochastic models have been researched (Haldi and Robinson 2009; Schweiker et al. 2012) to test for specific behaviors in offices and dwellings in order to define the most fitting probabilistic approach. In the current research, only comparisons of models with different complexities are taken into account, which to the best knowledge of the author represent a very small share of publications. Table 2.2 gives an overview of the considered comparisons. In most cases, such studies investigate differences between data-based, newly developed models and standard profiles. Each comparison study is described in detail hereafter.

Table 2.2: Considered comparison studies

Models considered for comparison

 = best performing model(s)  = other considered model(s)

Aim of simulation; Type of performance Non- Agent- Reference Schedules Probabilistic behavior indicator; building probabilistic based type

Systems control; daily occupant presence (Mahdavi and Occupant profile; (single, semi-   Tahmasebi 2015) presence closed, open-plan) office Occupant Annual and peak (Tahmasebi, Mostofi, presence, light energy demand for   and Mahdavi 2015) use and heating and cooling; equipment use office  Annual and peak (presence (Tahmasebi and Occupant energy demand for   distribution Mahdavi 2015) presence heating and cooling; (energy PIs) and peak office values) Daily occupant (Duarte, Van Den Occupant presence profile; Wymelenberg, and    presence (single, open-plan) Rieger 2013) office Window Design; energy opening and (D’Oca et al. 2014) demand for heating;   thermostat household adjustment Energy demand and (Langevin, Wen, and User behavior thermal acceptability;   Gurian 2014) office Design; energy (Chapman, Siebers, User behavior demand; office and   and Robinson 2014) household Blinds regulation, (Azar and Menassa Electric/gas demand; lighting/   2010) university equipment, DHW Behavior duration,  start/end time,  (variety (Yamaguchi, Tanaka, number of transitions, (behavior User behavior of and Shimoda 2012) probability duration, behavior distribution, number transitions) patterns) of different patterns Annual plug load,  (Mahdavi, Tahmasebi, peak plug load, and  (annual  Plug loads and Kayalar 2016) load fluctuations; (peak loads) electrical (peak loads) office energy use)

Description of comparison studies: which model performs best?

Mahdavi and Tahmasebi (2015) evaluated the probabilistic occupant presence models developed by Reinhart for Lightswitch-2002 (Reinhart 2004), and by Page et al. (2008) by

comparing them with a newly developed non-probabilistic model. The object of the investigation was eight workspaces (single-occupant presence, semi-closed individual, open-plan area) in an office area of the Vienna University of Technology. Notably, the authors used separate sets of data to train and evaluate the models. Data were collected over nine months (November 2011- July 2012) with a 1.6-minute time-step to generate 15-minute interval data. The predicted and actual occupant presence profiles were compared by means of a 100-run Monte Carlo simulation using 4 statistics (first arrival, last departure, duration and transitions errors) for 90 working days between April 2012 and July 2012. The overall goal of the study was to support building systems controls. The results show that Reinhart’s and Page et al.’s models performed quite similarly, but Reinhart’s model offered slightly better prediction of first arrival and intermediate transitions. Overall, the predictive capability of all models was found to be low and none of them performed below the threshold error value considered acceptable by the authors. The non- probabilistic model was found to perform best. The authors suggest that the random diversity in occupant presence patterns reproduced by probabilistic models may be crucial for other aims of simulation (e.g. design and sizing of building systems). However, they argue that they are not suitable to provide short-term occupant presence predictions based on past data such as is needed in building systems control. Their final conclusion is that non-probabilistic models that better fit historical data deliver a better predictive performance.

Tahmasebi, Mostofi, and Mahdavi (2015) applied occupant presence, lighting and plug load schedules from ASHRAE 90.1-2013 and Page et al.’s stochastic model (Page et al. 2008) to a small-sized reference office model from the U.S. Department of Energy (U.S. Department of Energy (DOE)). The aim of the study was to quantify the impact of stochasticity on annual and peak energy predictions for heating and cooling. The ASHRAE schedules were used as input for the stochastic model. The authors concluded that for the considered case the predictive ability of a simplified occupant presence modeling approach is analogous to a more complex stochastic approach. In a follow-up study, Tahmasebi and Mahdavi (Tahmasebi and Mahdavi 2015) analyzed the implications of using deterministic and stochastic presence models when predicting annual and peak energy demand for heating and cooling of a real office building in Vienna, Austria. The authors distinguished between the nature of the models (deterministic vs. stochastic), and also between the assumptions that were made while developing the model (generic assumptions vs. assumptions that rely on actual occupant presence information). The results show that observation-based stochastic models have a better predictive ability of the internal heat gains if compared with fixed profiles. However, when specifically considering building-level energy performance indicators, it is the assumptions rather than the nature of the model that play a decisive role. Typically, standard-based assumptions overestimate the actual occupant presence, resulting in higher cooling loads and lower heating loads.

Duarte, Van Den Wymelenberg, and Rieger (2013) evaluated new non-probabilistic occupant presence diversity factors for private and open plan commercial office buildings against ASHRAE 90.1 2004 profiles and Page et al.’s model (Page et al. 2008). The schedules are based on data collected in 223 private offices over about two years (November 2009-October 2011) in a multi- tenant eleven-story office building in Boise, Idaho. Although the overall trend throughout the day is similar, ASHRAE 90.1 2004 overestimated the occupant presence level (peaks of 95% as opposed to about 50% predicted by the newly developed schedules for private offices). In addition, when comparing a typical high occupant presence week and a typical low occupant presence week with Page et al.’s model (Page et al. 2008), the two models show similar characteristics, but the new data-based schedules do not register the great variation during the day found by the stochastic model. The authors hypothesize that this could be due to the restricted data set used to calibrate Page et al.’s model. Overall, the authors prove that the model resulting from measured data shows up to a 46% reduction in average day profile peaks for private offices and about a 12% reduction for open plan office spaces when compared to the ASHRAE values. Interestingly, they point out that energy modelers could conduct two sets of simulations – one with low and another with high occupant presence profiles – in order to produce a range of expected energy consumption during the lifetime of the building, as opposed to a single value. This method would allow energy modelers not to unnecessarily complicate the model inputs.

D’Oca et al. (2014) developed probabilistic user profiles for window opening and thermostat setpoint adjustment. The yearly heating consumption obtained by implementing their profiles in IDA ICE is compared with the one from deterministic schedules of European standard EN 15251:2007. The new probabilistic model consists of logistic regression formulas and it is based on field measurements collected from January to August 2008 of 15 naturally ventilated dwellings near Copenhagen. The used time-step is 10 minutes. The authors study 4 scenarios, namely: a deterministic scenario both for window opening and Tsp that should function as a reference; a scenario where window opening is treated in a probabilistic way while Tsp follows deterministic schedules; a probabilistic model for both behaviors; a probabilistic model which includes active, medium and passive users; a final model with 3 different thermostat adjustments. The three considered climates are: Athens, Stockholm and Frankfurt. When compared to the reference case, the last model showed the greatest discrepancy. Overall, it was pointed out how the deterministic approach generally underestimates the heating consumption, by the greatest magnitude in Athens (+61%). The authors are confident that their findings can be applied during the whole-building lifecycle, namely: design phase, operation phase, building retrofit, building management and building codes and policy.

Langevin, Wen, and Gurian (2014) present the Human and Building Interaction Toolkit (HABIT), a co-simulation tool for comfort and behavior predictions based on a field-validated, agent-based scheme. The tool is developed in MATLAB and implemented in EnergyPlus by means of the Building Control Virtual Test Bed (BCVTB) middleware. The object of the investigation is a three-story air-conditioned office building in Philadelphia, which was monitored for 1 year. The considered variables are: Tsp, heating, Tsp, cooling, heater and fan equipment energy per person, clothing adjustment, fans/heaters, and window and thermostat adjustment. The various behaviors were grouped into three scenarios, namely: “Base”, “Typical” and “Setpoint Float” scenarios. The overall finding is that by considering a realistic behavior, expected energy use during winter in cold climates increases by up to 15%. On the other hand, the authors point out that increasing the thermostat setpoints could counteract this rise and would significantly decrease the energy consumption in summer (up to 32%).

Chapman, Siebers, and Robinson (2014) propose a multi-agent simulation (MAS) approach to combine stochastic models into a single tool. This model-of-models integrates Page et al.’s presence model (Page et al. 2008), Fanger’s PMV model for metabolic gains calculation [77], Jaboob and Robinson’s unpublished activities model, Haldi and Robinson’s windows and shading devices model (Haldi and Robinson 2010; Haldi and Robinson 2009) and the Lightswitch2002 lighting algorithm (Reinhart 2004). The model is applied to a hypothetical house and a shoe box office; the results are compared with those obtained by means of default Design Builder schedules for each type. The results for the residential building show a decrease from 68.7 kWh/m2 heating demand to 59.0 kWh/m2 (-16%) using the MAS model. The discrepancy increases to -45% when considering the non-residential building (91.0 kWh/m2 and 62.4 kWh/m2 for schedules and MAS model, respectively). The window model represents the greatest contribution to the difference between results.

Azar and Menassa (2010) take a slightly different approach when comparing the traditional eQuest building energy estimating model with an agent-based model. The authors consider three categories of occupants (high, medium and low energy consumers) according to blind position, lighting/equipment schedules and hot water consumption. The reference building is a 1000 sq. ft. graduate student room accommodating 10 students for over 60 months and is located in Madison, Wisconsin, US. The base case consists of all 10 students belonging to the category “medium energy consumers”. The authors use an agent-based model to simulate the effect of “word of mouth”, presumably leading towards lower energy consumption. As a first step, the authors determined the share of electric and gas consumption directly influenced by occupants (79% and 13%, respectively). As far as it concerns electric use, the values of consumption predicted with the proposed method are – in the best-case-scenario – 21.6% lower than the eQuest average.

Yamaguchi, Tanaka, and Shimoda (2012) developed two occupant behavior models based on the Monte Carlo approach (just as Richardson, Thomson, and Infield (2008) and Widén and Wäckelgård (2010) models) and on Tanimoto, Hagishima, and Sagara (2008a) approach, and applied them to a household in Osaka, Japan. The modified version of Tanimoto’s model is named Roulette Selection. The time-use data were collected over 9 days only. The results were evaluated in terms of: duration time for behaviors per day, time at which the routine behaviors start and end, number of behavior transitions per day, probability distribution showing percentage of behaviors at each time step, and number of different patterns of occupant behavior transition in 500 simulations. The authors conclude that the Markov Chain model well replicates behavior duration and transitions. However, the Roulette Selection better approximates the variety of behavior patterns, even with limited time use data, but it performs worse in terms of predictive capability of behavior transitions and duration.

Mahdavi, Tahmasebi, and Kayalar (2016) compare the ability of a simplified deterministic linear regression model and a simple stochastic Weibull model in predicting the plug load fraction (actual plug load value divided by effective installed equipment power at an occupant’s work station) of an office building in Vienna, Austria. The authors used both high-resolution monitored data and Page et al.’s model to represent occupant presence. They found that although there is considerable diversity among occupants regarding the relationship between plug load fraction and presence probability, R2 values of a linear regression model for each occupant are high (always => 0.87). They suggest that office electrical energy use can thus be inferred from basic assumptions of occupant presence patterns and EPD. The proposed deterministic model performs well in predicting the annual electrical energy use for office equipment. However, because the model was developed using average reference-day presence and plug load profiles, it performs quite poorly with regard to peak plug loads and distribution of run period predictions. ASHRAE 90.1 schedules grant a better prediction of high peak values, while they largely overestimate annual values (with a relative error of 106.7%). The performance of the probabilistic model was assessed as mean values of a 100-run Monte Carlo simulation and in 3 different scenarios in terms of input occupant presence data. The probabilistic model is found to perform poorly in annual electrical energy use predictions, while it offers a good estimate (outperforming ASHRAE 90.1) of peak loads.

Other studies dealing with model complexity in occupant behavior

Other studies consider the issue of model complexity. However, their goal is not to compare the predictive performance of two or more models, hence they have not been included in Table 2.7.

Among them, Hong and Lin (2012) investigate the effect of occupant behavior on a private office with three levels of complexity: direct use of EnergyPlus, use of the advanced Energy Management System in EnergyPlus, and use of modified code of EnergyPlus. The different complexities are used to model different aspects of occupant behavior. Liao, Lin, and Barooah (2012) acknowledge the need for different resolutions for different aims. For this reason, they propose the Multiple Modules (MuMo) model, a stochastic agent-based model for occupant presence simulations over time, and a low-complexity occupant presence model for real-time estimations. The MuMo model is shown to have a similar predictive capability to Page’s model.

Concluding remarks

The comparisons presented in Section 2.4.1 are performed for different aims of simulation, performance indicators and building types (see Table 2.2). The fact that the publications are discordant on which models have a better predictive ability confirms that the capability of a model to predict reality strongly depends on both the considered case study and the performance indicators used. However, this observation is often implicit in the publications mentioned above, which tend to conclude – with some exceptions (e.g., Mahdavi and Tahmasebi 2015) – that a certain model simply has a better predictive ability (e.g., D’Oca et al. 2014). Moreover, some studies state that the models deemed to have a worse predictive performance overestimate the energy consumption, while other studies arrive at the opposite conclusion. Nonetheless, some conclusion can be drawn:

i) Standard profiles are largely considered not to be suitable for describing complex occupant behaviors; ii) More complex models do not always perform best (e.g. in (Mahdavi and Tahmasebi 2015) a simple non-probabilistic model showed better convergence than a probabilistic one when compared to measured data); and iii) Models derived from measured data always perform best when describing the investigated case study.

However, such conclusions are of little help when facing a practical choice of which model complexity to use.

2.5 Validity and application range of occupant behavior models

Sections 2.2 - 2.4 discussed the mathematical formalisms used in literature to model occupants and their behavior, the available OB models, and the studies aiming at comparing the predictive ability of models characterized by different levels of complexity. It was pointed out how existing models are often difficult to implement in a simulation framework, due to a number of reasons such as lack of clear documentation or difficulty in obtaining the required variables in BPS software. Moreover, often models are proposed for a single aspect of OB, and are derived from limited datasets, frequently in the northern hemisphere, and pertain to single-occupant office buildings. These points are closely related to another issue to consider when implementing existing OB models in a BEP model: their quality assurance, or the models’ validity and application range. It has been argued (Mahdavi and Tahmasebi 2016) that existing OB models have rarely undergone a rigorous evaluation process. Among the identified issues pertaining to this field of inquiry are: i) the insufficient empirical backing due to the rather limited amount of large-scale observational studies; ii) the complexity of conducting controlled field studies that cover physical, physiological, psychological and sociological parameters; iii) the absence of an agreed-upon framework for model documentation and evaluation procedures; iv) the methodological issue of developing and testing a model with the same dataset; v) the fault of extrapolating results from a single study to all populations, building types, locations and climates. An important aspect that had to be taken into account during the design of this doctoral research, and which is still true at the present time, is that models can only be safely employed in those cases for which they were developed, which obviously constitutes a notable limitation.

2.6 Concluding remarks

In conclusion, the search for fit-for-purpose OB modeling is a multi-faceted problem. The various available mathematical formalisms (Section 2.2), the huge amount of models presented in the literature (Section 2.3) and the substantial disagreement about their predictive ability (Section 2.4) render it difficult for the simulation user to pick a model over another a priori. This existing difficulty is the knowledge gap that is addressed in this thesis. However, it is important to note that, even if the simulation user were successful in identifying the fit-for-purpose model complexity, the resulting simulation outcome would still be subject to the reliability of the many interconnected models and sub-domains (Section 2.1) and to the trustfulness of the OB models themselves (Section 2.5).

This study attempts to offer a solution to the issue of model selection. In particular, the fit-for- purpose OB modeling (FFP-OBm) strategy presented in the following chapters aims at guiding the simulation user in the choice of a suitable model complexity for his/her case. Of course, the trustworthiness of the obtained results will depend on the validity of the overall simulation model, as well as of the selected OB model.

3 The fit-for-purpose approach: methods, concepts and development process

To pass freely through open doors, it is necessary to respect the fact that they have solid frames.

Robert Musil, The Man Without Qualities

This chapter focuses on the methods applied to develop, test and deploy the fit-for-purpose OB modeling (FFP-OBm) strategy. First, in Section 3.1, an overview of the methodology is given. Section 3.2 provides some theoretical concepts that apply to model selection in the context of OB. In addition, the requirements of a FFP-OBm strategy are outlined. Section 3.3 presents a high-level overview of the strategy and of the computational tools that enables it in this research. Moreover, it illustrates the scoping of the strategy and the role of case studies’ application in the strategy development. Section 3.4 provides key conclusions.

3.1 Overview of the methodology

The work described in this thesis concerns the research activities connected to the development, testing and deployment of a strategy to support BPS users in selecting the appropriate complexity of occupant behavior modeling. In this study, the term appropriate is used a synonym of fit-for-purpose. A fit-for-purpose model, as explained in Section 3.2.4, leads to the minimum overall error in the performance prediction, considering both the error due to model abstraction and the uncertainty in input parameters. Fig. 3.1 illustrates the methodology used to develop and test the fit-for-purpose occupant behavior modeling (FFP-OBm) strategy. The methodology consists of four different steps:

― Review of the theoretical concepts The state-of-the-art of model selection is reviewed and contextualized in the framework of OB modeling (Section 3.2). This process leads to the formulation of requirements for the proposed strategy;

― Develop the fit-for-purpose occupant behavior modeling (FFP-OBm) strategy This iterative process is the core of the thesis (Chapter 4 to Chapter 6). The application and evaluation steps of the methodology contribute to the development of the strategy;

― Apply the FFP-OBm strategy to case studies Case studies are used throughout this thesis to: i) develop the strategy (multiple variations of virtual buildings, see Section 4.4, Section 5.4 and Section 6.3); ii) test the developed strategy (mostly real-life buildings and applications, see Chapter 7);

― Evaluate the FFP-OBm strategy with a user group and incorporate feedback This step was essential to allow for an objective assessment of the usability and suitability of the developed strategy in practice. A number of practitioners helped ensure that the FFP-OBm strategy had potential for integration in simulation tools. The advice received from practitioners during the development and application of the strategy is embedded in the strategy as presented in this thesis.

The iterative development process is presented in further detail in Section 3.3. The methodological steps iteratively follow the life cycle of a simulation study as identified by Balci (1994).

Problem Statement

Review the fundamentals of the model selection state-of-the-art and contextualize Review of the theoretical them in the framework of OB modeling. concepts Formulate the strategy requirements

Develop a strategy for selecting the fit-for- Develop the fit-for-purpose purpose OB model complexity for the case occupant behavior modeling at hand (FFP-OBm) strategy

Test the developed strategy on a range of Improve the Apply the FFP-OBm case studies with varying purpose of FFP-OBm strategy strategy to case studies simulation

Present the FFP-OBm strategy to a group Evaluate the FFP-OBm of potential users; evaluate the suitability strategy with a user group and usability of the approach in practice and incorporate feedback and incorporate their feedback

Thesis Outcome

Fig. 3.1: High-level methodology to develop the FFP-OBm strategy

3.2 Theoretical concepts

Dicebat Bernardus Carnotensis nos esse quasi nanos gigantium humeris incidentes. Bernard of Chartres said that we are like dwarfs sitting on the shoulders of giants. John of Salisbury

The quest for appropriate modeling selection in the field of occupant behavior calls for a range of considerations spanning various fields of research. Nonetheless, the quest must begin with commonsense questions. In this thesis, the maxim that everything should be made as simple as possible, but not simpler is often followed. The theoretical concepts that are reviewed in this section are structured around five different questions:

- What can the occupants do? (Section 3.2.1)

- Which simulation output do I need? (Section 3.2.2)

- Does occupant behavior matter? (Section 3.2.3)

- Which model should I choose? (Section 3.2.4)

- What should a fit-for-purpose occupant behavior modeling (FFP-OBm) strategy look like? (Section 3.2.5)

The questions enable to determine when the FFP-OBm strategy is needed. For example, there may be simulation cases where the model complexity of a given OB aspect does not have an impact on the results. Alternatively, there may be cases where the model complexity is evident from the start of the simulation. In these cases, it is not necessary to enter the FFP-OBm strategy. A problem formulation should be as explicit as possible, starting with acknowledging whether the right questions are being asked (Kellner, Madachy, and Ra 1999).

What can the occupants do?

Participation will create chaos. Christopher Alexander, The Oregon Experiment, 1975

Occupants interact with buildings in various ways (Chapter 2). However, not all interactions are always available, depending mainly on design choices and building type. For example, nobody would expect to have the possibility to open windows in a movie theater. In contrast, doing so in an office environment has proven to increase workers’ satisfaction and productivity, while reducing sick building syndrome (SBS) symptoms (Burge 2004). Leaman and Bordass (1999) show that, indeed, the closer occupants are to operable windows, the more satisfied they are. Table 3.1 illustrates some typical building/occupant interactions available according to building type. The control granted to occupants over their built environment is a subject that often involves parties with conflicting objectives. Typically, a building operator may be interested in limiting the freedom of occupants so that the environment can be better controlled. Conversely, occupants are keen to override controls of their environmental conditions whenever they experience discomfort. Despite this desire, the level of automation in buildings is certainly rising, with apparent benefits to energy consumption (Aghemo, Blaso, and Pellegrino 2014). As such, numerous studies have

demonstrated the desire of occupants to maintain overrides of automated systems and have shown the benefits of participation on comfort (Galasiu and Veitch 2006; Galasiu et al. 2007). It is expected that occupants will be still granted a large amount of building/occupant interactions in the future. For the scope of this thesis, the FFP-OBm strategy should first assess whether a building/occupant interaction is available. If a given building/occupant interaction – corresponding to an OB aspect – is not available, it is pointless to seek how to model it. While this consideration may seem trivial, it is often forgotten in practice.

Table 3.1: Examples of building/occupant interactions available for building type

Building type (Presence) Adaptive Non-adaptive Commercial buildings Shops* (*)  Banks*   Bar/Pub/Restaurant*    Hotel*     Offices    Residential buildings    Educational buildings*    Civic and recreational   buildings*

* In these building types the visitors are considered (excl. staff) (*) One tick corresponds to behavior mostly available, three ticks correspond to behavior essential to the building type

Which simulation output do I need?

The initial step of the BPS user who approaches the FFP-OBm strategy should be the acknowledgement of the purpose of the simulation. Chapter 2 illustrated how different models (characterized by different input) lead to different types of output. In deterministic models a unique input leads to a unique output (Fig. 3.2). Deterministic scenarios or patterns are derived from input variations of deterministic models. In this sense, they add diversity to using a single schedule/deterministic model. When two or more simulation outputs from diversity patterns are combined, they generate a range of results (Fig. 3.2), as opposed to a single value. The range derives from the input variation and does not include any information about the probability of occurrence. Alternatively, in stochastic models unique input leads to different output for each

model run due to the random component of the modeled process. Multiple runs are used to estimate probability distributions (Fig. 3.2). When defining the aim of the simulation, it is a fundamental requirement that the BPS user clarifies the desired type of output. For example, if the simulation objective is to size a building HVAC system for the worst-case scenario, a range derived from diversity patterns (Fig. 3.2; Output from diversity patterns (a)) may be sufficient. In contrast, it would be difficult to assess that the yearly energy consumption of a given building will fall below a target value with a 95% confidence without recurring to stochastic models (Fig. 3.2; Output from stochastic models (b)). Again, if the aim of the simulation is to verify that the indoor illuminance falls within an accepted range, a scenario analysis may be sufficient (Fig. 3.2; Output from diversity patterns (c)). Therefore, the FFP-OBm strategy should include considerations regarding the desired simulation output. Some purposes of simulation must necessarily be solved with one particular modeling formalism.

Output from Output from Output from deterministic models diversity patterns stochastic models

PI PI PI (a) (a) (a)

Target Target Target value value value

PI PI PI (b) (b) (b)

Accepted range Accepted range Accepted range

PI PI PI (c) (c) (c)

Fig. 3.2: BPS tools yield different outputs depending on the inputs

Does occupant behavior matter?

The model complexity of different OB aspects is not always critical for the simulation results. For example, Ahn and Park (2016) propose a categorization of building types according to the

predictability of occupant presence and whether the HVAC system is individually or centrally controlled (Fig. 3.3). The authors conclude that the correlation between occupants and energy consumption is weak if the occupant presence pattern follows the random walk and the occupants are able to control the HVAC directly (left-upper corner in Fig. 3.3). Unfortunately, reality is much more complex. The authors interestingly draw attention to the fact that the relevance of OB for a simulation study's results depends on the degree of (un)predictaility, or uncertainty, of the various OB aspects. Moreover, the potential impact that different OB aspects may have on the simulation results is crucial. In this context, it is hard to generalize a priori whether or not OB is relevant for a given case.

Fig. 3.3: Different types of buildings for occupant behavior study (the arrows indicate that the location can vary) Adapted from (Ahn and Park 2016)

The conclusion that was drawn from these above observations is that it is a necessity to frame the relevance of OB by considering an uncertainty-impact matrix (see Fig. 3.4). In those cases where the potential impact of an OB aspect on the simulation results is low, such aspect shall be modeled using the lowest possible complexity. If the potential impact is identified as high but the degree of uncertainty is low, the available knowledge should be used to model this particular OB aspect. The cases that should be investigated by means of the FFP-OBm strategy are those characterized by a high degree of uncertainty and a high potential impact on the simulation results (quadrant highlighted in Fig. 3.4).

USE AVAILABLE INVESTIGATE KNOWLEDGE MODEL COMPLEXITY OB Impact OB

USE LOWEST USE LOWEST MODEL MODEL COMPLEXITY COMPLEXITY

OB Uncertainty

Fig. 3.4: Uncertainty-impact matrix applied to OB model selection

However, matters are complicated further. Firstly, various OB aspects may be interrelated, and this interrelation should be considered while modeling (let us think of occupant presence, which acts as a trigger for most behaviors). Secondly, while the degree of uncertainty related to an OB aspect can be assessed epistemologically, this may not be the case for the level of impact. The axis that separates high impact from low impact often cannot be drawn a priori. As an example, consider the case of blind operation. It may appear that this aspect of OB is highly uncertain in a given building. However, the building recently underwent renovations, especially with concerns to the performance of its windows, rendering the use of blinds less important as a means of containing solar gains during the cooling season. Now, the simulation user is interested in assessing the cooling energy use of the mentioned building. How is it possible to categorize the potential impact of blind operation on the energy performance as high or low? Answering such a question implies careful consideration of various factors, including the window-to-wall ratio according to orientation, the blinds’ characteristics, and the incoming solar radiation. At the same time, it would be pointless to investigate the appropriate model complexity of an irrelevant OB aspect. Therefore, the FFP-OBm strategy must include a method to assess whether an OB aspect is relevant or not for the results. Moreover, it should allow the user to input further information about OB aspects whose epistemic uncertainty (the uncertainty derived by lack of knowledge) can be reduced.

Which model should I choose?

Sections 3.2.2 to 3.2.3 presented the multiple factors that ought to be considered when deciding if the model complexity of an OB aspect should be investigated or not. In many cases, the lowest model complexity or available knowledge can safely be applied. However, there are some cases in which: i) the building/occupant interaction corresponding to an OB aspect is present; ii) the PI of interest does not inform model selection; and iii) the OB aspect is uncertain and has a potentially high impact on the results. In these cases, the appropriate model complexity should be chosen. The problem of selecting the appropriate model complexity does not specifically concern building energy simulation, but rather simulation in general, which has led some researchers to state that “the choice of the best model is more of an art than a science” (Brooks and Tobias 1996). Again, one of the pillars of modeling is the acknowledgement of the objectives of the model (S. Robinson 2011): poorly understood modeling objectives can result in excessively complex models. This may cause errors in the simulation results, as well as unnecessary expenditure of time and money. In regard to building energy simulation, a similar problem existed in well-researched sub-domains such as modeling of airflow (Djunaedy, Hensen, and Loomans 2003), lighting (Ochoa, Aries, and Hensen 2012), or systems (Trčka and Hensen 2010). However, for these sub-domains, it is now widely acknowledged that different complexity levels should be used for different aims of simulation. In the area of OB modeling, possibly due to its relatively recent development, a similar common understanding has not yet been reached. Some principles that support model selection are presented below.

Parsimony Numquam ponenda est pluralitas sine necessitate. Plurality must never be posited without necessity. William of Ockham

The first three simple principles of modeling proposed by Pidd in his “Rough Guide to Modeling” (Pidd 1999) concern model complexity: i) model simple; think complicated; ii) be parsimonious; start small and add; iii) divide and conquer; avoid megamodels. Generally, many studies (e.g. (Astrup, Coates, and Hall 2008; Pitt and Myung 2002)) advocate the use of parsimonious models, i.e., the simplest among competing models. Simpler models are preferable as they are easier to implement, validate, analyze, and adjust when the conditions change. Moreover, they lead to faster simulation studies. In practice, and in opposition to the principle of parsimony, a general increase in model complexity is observed. According to (Chwif, Barretto, and Paul 2000) this trend can be explained as a consequence of technical and non-technical factors. Among the non-

technical factors, the authors name: the “show-off” factor: the more complex, the more impressive; an “include-all syndrome” that leads modelers to include all available information; and the progress of computing power that enables time-efficient, complex simulation. The technical factors are: lack of understanding of the real system; inability to correctly model the problem (conceptual model), or the tendency to just model “as close to reality as possible”; the inability to translate or correctly code the conceptual model into a computer model; unclear simulation objectives. This last factor has been identified as the chief contributor to the growth of complex models (e.g., (Salt 1993)).

Trade-off between approximation error and input uncertainty This second principle connects with the principle of parsimony. According to (Zeigler, Kim, and Praehofer 2000), a more complex model can represent reality more accurately, although the author of this study agrees with Salt (1993), who states that “it is quite possible to create a model that is at the same time finely detailed and wildly inaccurate”. This is because simple models introduce approximation errors (i.e., give a supposedly worse approximation of reality), while complex models introduce uncertainty due to the estimation of (the larger number of) input parameters. The goal of the simulation user should be to minimize the overall potential error by finding a compromise solution (Fig. 3.5). The optimal predictive ability of a model is nevertheless very case specific. Underfitting and overfitting occur when the model selection moves from the optimum towards too simple or too complex models, respectively. It has to be noted that the resulting potential error from underfitting and overfitting may be comparable, depending on how far from the optimum a model is; however, in the case of overfitting additional time and cost efforts have to be taken into account. PREDICTION ERROR

Fit-for-purpose model

MODEL COMPLEXITY

Fig. 3.5: Model uncertainty vs. complexity. Adapted from (Alonso 1968)

This principle can be applied to the field of OB model selection in a twofold manner, i.e. by considering:

― the uncertainties related to the BPS model itself, such as other scenario uncertainties (e.g., the weather) and design parameter uncertainties (both decided parameter uncertainties, also called physical uncertainties, and undecided parameter uncertainties connected to the design aspects that are yet to be settled/decided) (Rezaee et al. 2015). These uncertainties should be compared with the OB-related uncertainties. If the model uncertainties are larger than the OB-related uncertainties, there is no value in increasing the OB model complexity.

― the number of uncertain parameters present in the OB model, which typically increases as the model becomes more complex (see Table 3.2). The degree of uncertainty of these parameters clearly correlates with the phase of the building lifecycle, i.e. the more advanced the phase, the more data that is supposedly available. Moreover, the time/effort put into gathering the data is of course relevant. Nevertheless, this second aspect is slightly more complex in the field of OB modeling; more complex OB models do not necessarily result in a decreased approximation error, even if input parameters are free from uncertainty (see for example (Tahmasebi and Mahdavi 2015)).

Table 3.2: Typical required input according to model complexity for widespread adaptive behavior modelling approaches; the uncertainty of the required input depends on available data

Type of model Required Input Schedules t Deterministic models threshold indoor/outdoor variables Bernoulli models presence (y/n); outdoor variables; p Poisson models presence (y/n); outdoor and/or indoor variables; (time-varying) parameter λ; p Markov discrete-time presence (y/n); outdoor and/or indoor variables; meaningful time step; p Markov discrete-event presence (y/n); outdoor and/or indoor variables; meaningful time step; triggering event; p Agent-based stochastic N of people; location (prob); occupants’ ID; seniority (rank among agents); awake-status; outdoor and/or indoor variables; comfort- related variables; meaningful time step; triggering event; temperature threshold (prob); p

Goodness-of-fit Ultimately, the selected model should be able to explain the observed data well, it should generalize to more data, and – as mentioned – it should be as simple as possible. In the field of OB modeling, the commonly used selection techniques to guarantee goodness-of-fit are: p-value, maximum likelihood estimation, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and k-fold cross-validation (Yan and Hong 2018). While goodness-of-fit is not the focus of this thesis, all models that are connected to the strategy should have undergone rigorous validation and evaluation using these techniques.

What should a FFP-OBm strategy look like?

In summary, a FFP-OBm strategy should start by clearly acknowledging the purpose of the simulation (WHY) and its requirements, which typically depend on the user of the simulation strategy (WHO) or the stakeholder he/she represents. An analysis of the case study at hand (WHAT) will allow assessment of which building/occupant interactions corresponding to OB aspects are available, and their degree of uncertainty. Their uncertainty is strongly correlated with the phase in the building lifecycle (WHEN), which also determines the uncertainties connected to the building model. Fig. 3.6 provides a summary of the influential factors and the range of choices to be included in the FFP-OBm strategy. Moreover, the strategy should allow assessment of whether an OB aspect is influential or not for the results, and lead to a reduction in the epistemic uncertainty of influential OB aspects by collecting further data. The iterative development process followed to achieve the actual FFP-OBm strategy is presented next.

3.3 Iterative development process

The FFP-OBm strategy was developed iteratively, following the principle that each simulation problem should start with the identification of the purpose and end with the FFP model for each OB aspect. As mentioned in Section 3.1, the iterative development process consists of three macro-steps: develop, apply, and evaluate. In this section each step is explored in further detail. The development step is addressed in terms of scope of the strategy (Section 3.3.1), overview of the strategy (Section 3.3.2), and software implementation (Section 3.3.3). The application to case studies is presented in Section 3.3.4, while the evaluation is discussed in Section 3.3.5.

WHOWHAT WHY WHEN

Policy maker SCALE OBJECTAim of simulation PHASE IN LIFECYCLE Impact evaluation of proposed System engineer measures Predictive building systems control Conceptual Design

Building Commercial Developed Design building stock Performance Indicator Technical Design Office Total energy consumption Energy consumption WWR = 60% Pre-construction O&M Illinois yearly ... Region Chicago, Illinois (sub-)hourly

? BUILDING OB MODEL OCCUPANT MODEL SCENARIO

Quasi-steady state Schedules/profiles Average occupant simulation Determinisitc models Set of multiple Nodal networks occupants Non-probabilistic Dynamic simulation models Individual occupant Dynamic simulation + Agent-based models ... + complexity - + complexity detailed airflow - + complexity - + complexity ......

Fig. 3.6: Fit-for-purpose framework: policy maker vs. system engineer

Scope

The FFP-OBm strategy is envisioned to be a supporting tool for decisions concerning the model complexity of any possible aspect of OB, in any building, for any purpose of building energy and comfort performance simulation. However, it is neither possible nor necessary, nor desirable to test such a broad spectrum of applications within the framework of a doctorate thesis. Secondly, issues connected with the state-of-the-art of OB modeling pose further limitations. Finally, to manage the scope of the research, a BPS tool was selected over other available ones, which de facto excluded the testing of the strategy in other relevant BPS tools. This section concerns the scope of the strategy as developed in this thesis. The categories presented in Fig. 3.6 help structuring the scope definition.

WHAT

― Space-scale: Building level; floor level; ― Object: Office buildings; various building vintages; various climates. Each building type is characterized by a different degree of building/occupant interaction (see Table 3.1). The available building/occupant interactions have an instrumental role in the potential influence of OB on building performance. While it

would be interesting to consider a variety of building types, the work presented here focuses on applications in office buildings. This choice derives from the greater availability of higher complexity OB models for office building applications compared to other building types (see Chapter 2). Nonetheless, it is acknowledged that the influence of OB on e.g. residential buildings may be even higher than on office buildings. In this respect, a significant amount of work has been carried out on residential buildings in the framework of MSc final project theses supervised by the author at Eindhoven University of Technology (Van Eck 2016; Song 2016; Muroni 2017).

WHY

― Aim of simulation: Product development; Design choice among alternatives; Performance assessment; Energy performance contracting; The author understands that a number of design questions may not be covered by the strategy in its current developmental stage. For example, assessing the potential for energy flexibility of a building, which typically requires shorter than monthly or weekly time-steps and a high degree of diversity, may require a slightly different approach. However, such questions would be addressed before entering the FFP-OBm strategy, i.e. in the “Which output do I need?” phase. ― Performance indicator: Energy: Heating energy; Cooling energy; Heating peak load; Cooling peak load; Total energy; Comfort: Weighted overheating hours (WHO); Daylight autonomy; Glare performance; ― Time-scale: Yearly; Monthly or Weekly.

WHEN

― Phase in lifecycle: Conceptual design; Developed design; Commissioning; Retrofitting. Different building design stages are typically characterized by different simulation purposes and objectives (Fig. 3.7). Moreover, in some phases of the building lifecycle, dynamic simulation may not be the preferred tool to solve a given question (CIBSE 2015). Besides the phases in the building lifecycle indicated above, the FFP-OBm strategy was also applied to the R&D process of innovative solar shading elements (Section 7.1).

Performance contracting Post-occupancy Model calibration Operational Controls improve- Reduction of Fault detection In-use building In-use assessment rating − evaluation − for long-term building design evaluation − energy saving opportunities identification − ment − sensing units to capture real-time operation − and diagnostics ssioning O&M Retrofitting As-built building regulations compliance constructionNew building assessment rating As-built building performance As-designed building performance As-designed building regulation compliance gn Pre-Construction Commi Emissions Refined lighting, Assessment of Ventilation Size HVAC Refine control − calculation Detailed technical analysis; HVAC sizing; energyuse prediction − envelope and equipment systems − passive systems − studies − system − strategies Available input data and degree of certainty Orientation (small Detailed analysis Material adjust- Lighting strategy Evaluation of HVAC choice and Design refinement; comparison among HVAC alternatives; analysis loads − adjustements) − of glazing (glazing type, shading and/or type blinds, blind and control) − ment in overheating areas − − loads reduction strategies − selection Shape Orientation WWR Insulation of Thermal mass Space usage Air change rate Energy supply Yearly energy Peak heating and Design assessment Comparison among conceptual design alternatives; loads calculation − − − − envelope and glazing − − − − type − demand by end use − cooling demand Conceptual Design Developed Design Technical Desi Pre-design assessment

Preparation and Brief

CODE COMPLIANCE CODE DESIGN OPERATION

Fig. 3.7: Typical energy performance-related questions at different design stages, grouped per different purposes (adapted from (Morbitzer 2003))

BUILDING MODEL The simulation experiments carried out within this research were mostly enabled by the BPS tool EnergyPlus (Crawley et al. 2001). The reason to favor EnergyPlus over other BPS tools is that most of the available OB models were coded in EnergyPlus at the commencement of this research project (e.g., Gunay, O’Brien, and Beausoleil-Morrison 2016). However, mostly thanks to the efforts carried out within IEA EBC Annex 66, a number of other software today allows for the implementation of most available OB models through obFMU (Cowie et al. 2017; Hong, Chen, and Belafi 2018). Only dynamic simulation was considered as a building model. A detailed airflow model was employed for those cases with operable windows during commissioning (e.g., Section 7.2).

OB MODEL Different modeling formalisms of increasing complexity were identified in Chapter 2. In the framework of this thesis, however, not all modeling complexities were implemented in the simulation tool. In particular, agent-based modeling (ABM) was not implemented. This choice originates from: i) the scarce documentation and validation of available ABMs for OB; ii) the reasoning being that doing so would not be crucial for the outcome of the strategy. The assumption that more complex models yield a better approximation of reality is to date not verified in the field of OB.

OCCUPANT SCENARIO The diversity among individuals was considered in this thesis according to model complexity. In fact, two sets of occupants were employed when formulating the diversity patterns (Chapter 5), which are high/low variations of simple schedules or IF-THEN models to mimic diversity. Conversely, an average occupant was employed with stochastic models. This choice was motivated by the scarce documentation regarding occupant diversity within stochastic models. Nevertheless, it is important to consider different occupant profiles in stochastic models as a next complexity step in OB modeling. Individual occupants typically pertain to the sphere of data-driven, non-probabilistic models or agent-based models. In Section 7.2, where data-driven models are used, individual occupants are implemented in the software.

Overview

Section 3.2.3 clarified how one of the crucial tasks of the FFP-OBm strategy is to distinguish between influential and non-influential OB aspects. Following the principle of parsimony, this

thesis iteratively outlines three increasingly complex sensitivity analysis tests to achieve this objective. The strategy can hence be reduced to four steps (Fig. 3.8):

- Impact Indices Method (Chapter 4) This is the first step of the strategy. It allows estimation of the potential impact of various OB aspects in one simulation run, and the possibility to filter out the non- relevant ones. In terms of performance indicators, the method is only currently implemented for heating and cooling energy but could be extended to other performance indicators. If it is not possible to take a decision based on the results of the Impact Indices Method, the BPS user is advised to proceed to the next sensitivity analysis level.

- Diversity Patterns Method (Chapter 5) The second step of the strategy requires the formulation of high/low scenarios, or diversity patterns, for all uncertain OB aspects, thereby increasing the required number of simulations. The Diversity Patterns Method hence requires more effort than the Impact Indices Method, both in terms of computational demand and from the BPS user (e.g., the formulation of scenarios). Running two or more simulations yields a range of results, as opposed to a single value. In some cases, the given range would be enough to make a decision. If the range does not allow a decision to be made, the BPS user is advised to proceed to the next sensitivity analysis level.

- Mann-Whitney U test (Chapter 5) The third step of the strategy consists of applying a statistical test to the range of results obtained with the Diversity Patterns Method. The Mann-Whitney U test is a statistical test of the null hypothesis that allows distinguishing among influential and non-influential OB aspects.

- Dealing with influential OB aspects (Chapter 6) The fourth step of the strategy deals with the OB aspects identified as influential in the previously applied sensitivity tests. First, the BPS user is advised to evaluate whether the epistemic uncertainty connected to the influential OB aspect(s) can be reduced. Then, if it is not possible to reduce the uncertainty, the strategy investigates whether a higher model complexity can be applied. Fig. 3.8 shows a schematic representation of the FFP-OBm strategy. A more detailed flowchart illustrating all the steps is presented in Section 6.2.

Simulation Purpose

Sensitivity Test 1 Impact Indices Can make Method decision? Chapter 4 Chapter

Sensitivity Diversity Can make Test 2 Patterns Method decision? Time / Effort Time /

no Chapter 5 Chapter Sensitivity Test 3 Mann-Whitney U Can make Test decision?

yes Dealing with Reduce influential uncertainty/ Can make OB aspects increase decision? complexity

no Chapter 6 Chapter Can still increase yes complexity or reduce uncertainty?

The available information does not lead to a reliable answer

FFP OB Model

Fig. 3.8: Schematic representation of the FFP-OBm strategy and related thesis chapters

It is worth noting that if a decision cannot be made even with the most refined, available modeling formalism, then the simulation does not serve its intended purpose. If the purpose of the simulation was to ensure that a building design would meet a target performance value, for example, the most refined modeling formalism may still lead to a performance outside of the target. In this case, the design must be revised. Conversely, if the purpose of the simulation was to choose among design alternatives, it may be that the most refined modeling formalism does not identify a clearly better alternative. Hence, it must be accepted that the alternatives perform in a similar way. In this sense, even the FFP model is not instrumental in achieving a unique solution.

Implementation

The FFP-OBm strategy can be implemented in a variety of BPS tools. Within this research, the tools used are the EnergyPlus and Matlab software, as illustrated in Fig. 3.9.

- Impact Indices Method

The EnergyPlus software was used for the building model, and one simulation was run using standard OB default schedules/deterministic models already present in EnergyPlus. The sole tool that is needed for the implementation of the Impact Indices Method is a post-processing tool, such as Matlab or Excel. The decision-making block supposes the BPS user’s intervention. In those cases where decision-making is not possible, the BPS user is directed towards the following step of the strategy.

- Diversity Patterns Method In this research, this step is carried out using Matlab to override the original .idf file input in EnergyPlus and write new .idf files with all possible pattern combinations. Again, a post-processing tool is required to assess whether a decision can be made based on the range of results.

- Mann-Whitney U test No further simulation is required for this step of the strategy, whose input is the simulation output of the previous step. The Mann-Whitney U test is implemented in Matlab. Given the outcome of the Mann-Whitney U test, it may or may not be possible to make a decision.

- Refine modeling approach In this research, the refined OB modeling is implemented by using available data or with higher complexity models. The higher complexity models are implemented in EnergyPlus by means of the Energy Management System (EMS) using the EnergyPlus Runtime Language (ERL). For the higher model complexity models, a feedback loop may exist for each simulation time step between the simulation results (sensors) and the response of the modeled OB aspect (actuator). Moreover, rather than running each .idf file once, stochastic models must be run an appropriate number of times to reach meaningful results (see Appendix F: Method to determine the appropriate number of runs nRuns for stochastic models).

Input/OB Run Process Output Sub-model simulation(s) results

Simulation Can make yes purpose decision? Method Impact IndicesImpact no

Can make yes decision? Method

Diversity Patterns Diversity no

Can make yes decision? U Test Mann-Whitney no

Can make yes FFP-OBm decision? model approach

Refine modeling no

Can still The available information does yes increase complexity n or reduce uncer- not lead to a tainty? reliable answer

Fig. 3.9: Schematic representation of the inputs, computational tools, and outputs obtained when implementing the FFP-OBm strategy

Application

As mentioned in Section 3.1, two types of case studies are used throughout this research with the scope of developing, testing and deploying the FFP-OBm strategy. During the development phase, a number of virtual buildings with different characteristics in terms of building envelope and use scenarios were modeled. Doing so allowed us to work on a pool of buildings rather than on a single building, and to preliminarily evaluate whether the strategy was leading to results that were in line with building physics principles. Moreover, each step of the strategy includes a reliability test, which consists of comparing the case study’s results according to the strategy to other available methods. Further, case studies have been used to seek confirmation for a number of hypotheses:

- different buildings are affected differently by OB; - different performance indicators are affected differently by OB; - different aspects of OB affect the results differently;

- the fit-for-purpose model complexity is case-dependent; - the results derived from the Impact Indices Method (one simulation required) are in line with those derived from one-at-a-time (OAT) sensitivity analysis (multiple simulations required); - applying a higher complexity model to non-influential results does not lead to significant changes in the simulation output; - the FFP-OBm strategy can be used to support informed design decision-making; - the Impact Indices Method can be used to support Energy Performance Contracting; - the results of a FFP-OBm strategy increase the reliability of a BPS software results.

Table 3.3 presents an overview of the case studies explored in this research. Case studies 1 – 3 were used to develop the strategy, while case studies 4 – 9 are applications of the strategy. It is important to note that one of the strengths of this research lies in its range of case studies. More specifically, in the fact that the case studies that were explored to develop the strategy are different from those that were used to test its reliability and usability.

Table 3.3: Case studies explored throughout this thesis

ID Case Study Case Study impression Investigated purpose; PIs description 1 Testing of sensitivity of different PIs to DOE Medium different OB aspects; office building Energy performance assessment; Heating energy and heating peak load 2 Testing of Mann-Whitney U test as a 5 x 5 x 3 m3 suitable statistical test; test implementation South-facing of higher model complexity to OB aspects cubicle office; with different influence; 16 building Energy performance assessment; Heating variants and cooling energy; weighted overheating hours (WOH)

3 Development and testing of Impact Indices Six-story office Method; reliability check by comparison building; 16 with OAT sensitivity analysis results; building Energy performance assessment; Heating variants and cooling energy

4 Testing of Impact Indices Method;

reliability check by comparison with OAT ABT office sensitivity analysis results; building Energy performance assessment; Heating

and cooling energy

5 Testing of Impact Indices Method; reliability check by comparison with OAT Strukton office sensitivity analysis results; building Energy performance assessment; Heating and cooling energy 6 Testing of Impact Indices Method; City reliability check by comparison with OAT hall/Music sensitivity analysis results; theater Energy performance assessment; Heating building and cooling energy

7 5 x 5 x 3 m3 Testing of FFP-OBm strategy; comparison South-facing of OB uncertainties and BPS model cubicle office; uncertainties; 16 building Design choice among overhang and blinds; variants heating and cooling energy 8 Testing of FFP-OBm strategy; Adaptive R&D support of adaptive façade elements; blinds total energy; daylight autonomy; glare performance

Testing of FFP-OBm strategy; TU Vienna Heating energy; peak heating load; thermal office building comfort indicator

Evaluation

A group of practitioners was contacted to act as a user group so that the actual usability and applicability of the strategy would be ensured. The names and affiliation of the practitioners are reported in Table 3.4. Meetings were organized each semester in various locations in The Netherlands, and the status of the strategy development and application, including the users’ group feedback, was presented by means of oral presentations. Each presentation was followed by a Q&A session.

Table 3.4: User group: contact persons and affiliations

Contact person Affiliation Ed Rooijakkers Halmos B.V. Adviseurs Ronald Hennekeij HOMIJ Technische Installaties Paul Korthof HOMIJ Technische Installaties Steven Mast Smits van Burgst Wim Plokker VABI Software

3.4 Concluding remarks

Chapter 3 discussed the activities connected to the development, testing and deployment of the FFP-OBm strategy. Such activities have been categorized into four different steps, each named by an action, i.e. review, develop, apply, evaluate. The review of the FFP-OBm theoretical concepts has been addressed in Section 3.2, while the remaining steps have been described at a high-level in Section 3.3. Such steps are further detailed in the coming chapters. It is important to note that while the thesis follows the convention of delivering the research process and the results in a sequential manner, the actual research was developed through iterative improvements. These iterations have contributed to the refinement of the FFP-OBm strategy presented in this thesis.

4 The fit-for-purpose approach: Impact Indices screening Method

Le simple est toujours faux. Ce qui ne l’est pas est inutilisable. What is simple is false, what is not is unusable.

Paul Valéry, Oeuvres II

This chapter discusses the first step of the fit-for-purpose approach: The Impact Indices (II) Method. First (Section 4.1), the underlying concept of the II Method is presented. The impact indices are presented in Section 4.2. Then (Section 4.3), the required steps for the method are discussed, namely the absolute values check, the computation of the impact indices, and the estimation of the variation magnitude and likelihood. The method is tested in Section 4.4 with a number of variants of a six-story office building. Section 4.5 provides a reliability check to ensure that the impact indices led to meaningful results. The method is discussed in Section 4.6, while Section 4.7 provides some conclusions. It is important to note that the II Method has currently been developed only for two energy-related PIs: heating and cooling energy use. A similar approach could be investigated for further energy and comfort-related indicators.

NB: The content of this chapter is largely inspired by an original research article published in the Journal Applied Energy under the title: Isabella Gaetani, Pieter-Jan Hoes, and Jan L. M. Hensen (2018). Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run. https://doi.org/10.1016/j.apenergy.2018.03.108 (Gaetani, Hoes, and Hensen 2018)

4.1 Introduction

The Impact Indices (II) Method has been developed with the aim of providing a first screening to identify when certain aspects of OB may be non-influential to the simulation results concerning building heating and cooling energy. As a first step, a literature review of the effects of different OB aspects on building energy performance was conducted (Section 4.1.1). Following the literature review, research gaps were identified (Section 4.1.2). The II Method is proposed as a possible solution to the need for a quick screening method that enables the discerning of non-influential and influential OB aspects (Section 4.1.3). The remainder of the chapter is structured as follows: Section 4.2 describes the impact indices for blind operation, equipment and light use, occupant presence (Section 4.2.1), and temperature setpoint setting (Section 4.2.2). The required steps to carry out the II Method are presented in Section 4.3. Section 4.4 presents the virtual experiment that was conducted to verify that the presented II Method could be successfully applied in a number of building variants. The reliability of the II results was tested by comparison with OAT sensitivity analysis and the results are presented in Section 4.5. The method is discussed in Section 4.6 and conclusions are provided in Section 4.7.

Literature review

Different aspects of OB affect the building heat balance in a completely dissimilar way. For example, the use of equipment influences the amount of internal heat gains, while adjusting the blinds has an effect on the incoming solar gains. Moreover, the effect that the very same behavior may have on building heating and cooling demand is varied, as it depends on the behavior as well as on the building itself and on climate. A number of studies come to this conclusion by investigating the effect of various aspects of OB on the energy demand of different buildings.

Hoyt et al. (2014) studied the effect on energy consumption of reducing the heating setpoint and increasing the cooling setpoint. The Medium Office DOE reference model (Deru et al. 2011) was used by the authors as a case study and was modeled in EnergyPlus, with two construction vintages (new construction and Post-1980 construction) and in 7 ASHRAE climate zones. According to their study, increasing the cooling setpoint from 22.2 °C to 25 °C results in an average of 29% cooling energy savings. Reducing the heating setpoint from 21.1 °C to 20 °C results in an average of 34% terminal heating energy savings. However, the results show a high

variability across different building models and climates.

Ghahramani et al. (2016) also analyze the influence of temperature setpoint on the energy use of the DOE reference office buildings, considering three sizes, three construction categories and all United States climate zones. The authors discovered that for extreme temperatures (i.e., outside the range - 20 to 30 °C), choosing the highest cooling setpoint for outdoor temperature above 30 °C, and the lowest heating setpoint for outdoor temperature below - 20 °C, led to the lowest energy consumption, regardless of climate, size, or construction. Within the range of -20 to 30 °C, the optimal setpoint depends on the building size. Within a range of observed outdoor temperatures (9 – 14 °C for small buildings and 8 – 11 °C for medium buildings) the setpoint selection is negligible. Great variability in potential savings was observed in respect to climate, building size and construction.

Lin and Hong (2013) demonstrated that the effect on the energy use in a single-occupant office building of occupant presence-controlled light, equipment, and HVAC operation, as well as temperature setpoint and cooling startup control, varies with the climate.

Sun and Hong (2016) implemented stochastic occupant presence-related measures to a two- story office building modeled in EnergyPlus version 8.4, and verified the impact that the measures had on the energy savings for the climates of Chicago, Fairbanks, Miami and San Francisco. Two standards, ASHRAE 90.1 – 1989 and 90.1 – 2010, were evaluated. The effect of single measures was shown to be highly dependent on building vintage and climate, while the overall savings of all measures were similar across vintages.

Azar and Menassa (2012) performed a sensitivity analysis to determine the effect of OB parameters on energy performance in typical office buildings of different sizes and in different weather zones. The authors found that the OB parameters generally had a significant impact on the energy performance. They concluded that the influence of various occupant presence behavioral parameters varies according to size and weather conditions.

Research gap

Although most authors found significant sensitivity of the investigated office buildings to OB, it is worth noting that the characteristics of OB can differ according to building type (Ahn et al. 2017). For example, in the residential sector the impact of different behaviors on energy use may be even higher. In fact, influential factors such as age, function and socio-economic status, may play an important role in adding diversity to energy-related behaviors (Guerra Santin,

Itard, and Visscher 2009). All mentioned studies investigated the effect of OB by means of OAT sensitivity analysis or with global sensitivity analysis supported by statistical methods (e.g., (Ghahramani et al. 2016)) for sampling and ranking. The common conclusion to be drawn is that since the effect of various aspects of OB depends on climate, building size, building vintage, etc. general guidelines suitable for practitioners cannot be derived. The available methods to define the potential influence of OB on building performance are complex and time-consuming, as they require the formulation of a high number of scenarios. Moreover, most available methods do not provide information on the specific contribution of different aspects of OB. There are currently no effective methods to quantify and rank the impact of uncertainties arising from different OB aspects.

The Impact Indices (II) Method

The first step of the fit-for-purpose OB modeling strategy concerned filling this research gap (Section 4.1.2), i.e. developing a quick screening method that allows the filtering out of those OB aspects that are non-influential for the building energy use. The underlying hypothesis is that the lowest model complexity is sufficient for those aspects that prove to be non-influential. The Impact Indices (II) Method seeks to provide an effective and efficient method to estimate the potential influence of various aspects of OB (i.e., blind operation, equipment and light use, occupant presence and temperature setpoint setting) on building cooling and heating energy demand. It is important to note that while only a number of OB aspects and PIs were investigated, a similar approach could be adapted to other OB aspects and PIs. The author builds upon and improves the common practice of simulation use by proposing a one-simulation-run approach. In fact, performing sensitivity or uncertainty analysis typically requires a high number of simulations (or scenarios), a practice which requires high time and cost expenditure. The method proposed here consists of a number of indicators (impact indices) that allow for the quantification of the influence of various aspects of OB without requiring the formulation of scenarios, but instead using the already available information provided by one simulation run. The indicators are developed in the form of ratios among the various contributors of the building energy balance, which were obtained by the simulation run. In cases in which one or more aspects of OB are found to be influential and are characterized by a high likelihood of variation, the simulation user is advised to proceed to the next step of the FFP-OBm strategy. Instead, the lowest model complexity is indicated for the OB aspects for which low risk is calculated (i.e., non-influential and/or characterized by very low likelihood/magnitude of variation). The method proposed here can also be used for other practical applications, such as energy

performance contracting (EPC). In this case, it helps to quickly establish the potential for low- risk EPC and determine whether there is need to integrate OB-related clauses for some OB aspects within the contract (see Chapter 7.3).

4.2 Impact indices

Impact indices for blind operation, equipment use, light use and occupant presence

The impact indices for blind operation, equipment use, light use and occupant presence are all developed in a similar manner. The indices’ definition is based on the building heat balance and borrows from the concept of skin-load dominated buildings vs. internal-load dominated buildings (Fig. 4.1). Simply put, the heat balance of skin-load dominated buildings is more likely to be highly affected by e.g. blind use, which directly affects the solar gains and ultimately the role of the façade as an interface between indoor and outdoor environment, while a variation in internal loads is expected to only have a marginal effect. Instead, the amount and distribution of internal loads is especially critical in internal-load dominated buildings. The concept can be better understood by considering an analogy: shading devices are likely to be highly influential in e.g. a greenhouse, while the heat released by a person in the greenhouse is probably negligible because the indoor environment is primarily affected by the outdoor conditions. While this is intuitively evident at a qualitative level, the II Method attempts to offer a quantitative base for this intuition. The goal was to develop a tool for decision-making, for example regarding OB model complexity.

Skin-load dominated Internal-load dominated

in-between area

Fig. 4.1: Skin-load dominated buildings are mostly affected by what happens at the interface with the outdoor environment, e.g. shading devices and window operation. Internal-load dominated buildings are supposedly mostly affected by OB aspects that influence the internal gains

4.2.1.1 Impact indices for heating

Alongside heat gains from occupant presence, light use and equipment use, BPS tools also calculate thermal exchanges through windows, interzone air flow, infiltration and ventilation. They also calculate the effect of the walls, floors and ceilings/roof of the zone, and the impact of the delay between heat gains/losses and loads on the HVAC equipment serving the zone (EnergyPlus 2019). Hence, assuming that no cooling occurs during the heating mode, the heat balance can be written as

, , , ,, , , , ,

, ,, (4.1)

where is the HVAC input sensible heating [J], ,,,,,,, [J] is the heat removal due to conduction and radiation through windows, interzone air transfer, infiltration, conduction of opaque surfaces and other, and

,,,,,,,,,,,,, is the heat addition due to people, lights, equipment, conduction and radiation through windows, interzone air transfer, infiltration, conduction of opaque surfaces and ‘other’ unclassified gains. The terms , ,

, , , and , , , , , , , can be written as , and ,, respectively. Equation 4.1 can be simplified by dividing both terms for as

1 , , (4.2)

In order to evaluate the potential effect of blind operation, equipment use, light use and occupant presence during the heating season, the following impact indices were developed

,,, , 1 (4.3) ,,, , 1 (4.4) ,,, , 1 (4.5) ,,, , 1 (4.6)

In this case study, the impact indices are calculated over the whole season. As such, they describe the potential impact of different aspects of occupant behavior on the heating (or cooling, as presented in Section 4.2.1.2) demand of a building in a given climate. In principle, should one

be interested in the effect of occupant behavior over a shorter time scale, it is possible to calculate impact indices for any time step, as long as a balance such as described in Equation 4.2 exists. In Section 7.3, for example, the impact indices are calculated on a monthly basis. The impact indices aim to quantify the weight of a given source of heat gain within the heat balance. For example, in ,, the heat gains due to equipment are subtracted from the total heat gains contributing to the heating load. If the equipment heat gains represented 0% of the total heat gains, the ratio would be 1 and the resulting impact index would be 0 (see Fig. 4.2a). Hence, the minimum value of the impact index is 0, which would occur if a type of behavior has no potential influence. The higher the specific impact index, the more likely blind operation, equipment use, light use or occupant presence are to have an impact on the building heating energy use. The high-end cap depends on the ratio between heat losses and heating demand. The term – 1 was added to allow comparison with the impact indices developed for cooling, which are presented next.

(a) (b) (c)

- - - - 1 = 0 - 1 > 0 - 1 >> 0

Heat Gains Considered Heat Gain

Heat Losses Heating Energy

Fig. 4.2: Visual representation of the II concept (heating). Balance (a), lower impact index (b), and higher impact index (c)

4.2.1.2 Impact indices for cooling

Similar to the heating mode, the heat balance in the cooling mode can be written as

, , (4.7)

where is the HVAC input sensible cooling [J], from which, dividing both terms by ,

1 , ,. (4.8)

Hence, the impact indices for blind operation, equipment use, light use or occupant presence in the cooling season are defined as follows:

,,, , 1 (4.9) ,,, , 1 (4.10) ,,, , 1 (4.11) ,,, , 1 (4.12) where the term (1 – ) allows for a comparison with the impact indices developed for heating. Also in this case, the higher the impact index, the more likely it is that blind, equipment, light use or occupant presence will have an impact on the building cooling energy use. The minimum possible value of these impact indices is 0. Values higher than 1 indicate that the second term of the equation is negative, i.e. the total heat losses are greater than the total heat gains minus the heat gains from the considered source (see Fig. 4.3).

(a) (b) (c)

- - - 1- = 0 1- > 0 1- >> 0

Heat Gains Considered Heat Gain Heat Losses Cooling Energy

Fig. 4.3: Visual representation of the II concept (cooling). Balance (a), low impact index (b), and higher impact index (c)

Impact indices for temperature setpoint setting

An evaluation of the impact of setting the temperature setpoint calls for a different methodological approach. Here, the building energy signature is evaluated during the occupied hours when the system is active in the heating and cooling mode. The ∆T between outdoor and indoor temperature is reported on the x-axis, with the underlying assumption that if the cooling or heating energy of a building is highly correlated with the ∆T, then the building is sensitive

to the temperature setpoint, and vice versa. The R2 values and the slope of the equation approximating the data points are then analyzed and the results are used to create the impact index. A high R2 value means that there is a strong relationship between energy use and ∆T. Instead, the slope of the equation is an indication of the amount of energy needed to respond to a given change of outdoor temperature. Fig. 4.4 is an illustrative example of how the total cooling energy use of two buildings over the season can be differently correlated to the ∆T between outdoor and indoor temperature. In Fig. 4.4 (right) the temperature setpoint setting is expected to have a higher impact on the cooling energy use than in Fig. 4.4 (left). The data is relative to two of the buildings presented in Section 4.4. However, the purpose of Fig. 4.4 is purely illustrative.

20 y = 1.2475x + 7.7258 R2 = 0.7545 15

-5 -30 -20 -10 0 10 dT (outdoor - indoor)

Fig. 4.4: A building whose cooling energy use is barely correlated to the ∆T between outdoor and indoor temperature (left), vs. a building which shows high correlation (right)

It is worth mentioning that because of their intrinsic nature, impact indices for blind operation, equipment use, light use and occupant presence evaluate the relative effect (expressed in percentage variation) on heating and cooling energy use of the mentioned aspects of OB. Instead, the impact index for temperature setpoint evaluates the correlation between the absolute value of the energy use and ∆T.

Impact indices limitations

It is important to note two simplifications related to the proposed formulas: i) the impact indices are based on the components of the building’s sensitive heat load. As such, at present, the results

should be restricted to buildings that do not have significant latent loads; ii) heat gains do not entirely contribute to reducing the heating demand or increasing the cooling demand; for example, for buildings characterized by particularly high gains/losses ratios and low time- constant (i.e. for those cases where the utilized gains are suspected to be extremely low) it is advised to perform an OAT sensitivity analysis to determine if the impact index has overestimated the potential effect of OB. The second simplification is slightly more subtle and worth further discussion. The II Method is based on the values reported by the software in the building heat balance. However, the building heat balance necessarily makes a number of simplifications such as using the gross heat gains and deriving some terms from other columns to obtain a balance (this is the case, for example, with the “other” component in “Opaque Surface Conduction and Other” calculated by EnergyPlus (EnergyPlus 2019)). As a result, the II Method could lead to an overestimation of the potential impact of an OB aspect as it considers the gross heat gains rather than the net heat gains. In other words, the more net and gross heat gains differ, the more the II Method overestimates the OB potential impact. The fraction of gains that contributes to reducing heating and/or increasing cooling demand hence depends on the building and on the heat gains description itself (e.g., heat gains with a high radiant component are more likely to be lost through the envelope than heat gains with a high convective component). One fast technique to calculate the share of a given heat gain that is indeed used to reduce heating or to increase cooling demand is to compare the results to those obtained by setting the heat gain of interest to zero. While this technique is a simplification since a relationship exists among the various components of the heat balance, it is a straightforward way to establish where heat gains contribute to heating or cooling demand. Another technique is to derive the utilized gains fraction for each simulation time step, for example from the simplified formula , , ∙,, where is the building energy need for heating [J], , is the total heat transfer for the heating mode [J], , is the dimensionless gain utilization factor [-], and , are the total heat sources for the heating mode.

The simulation user could then integrate the obtained values for , over the period of interest to find the utilized gains fraction. In the following sections this topic is taken into account and addressed.

4.3 Methodological steps

The steps required to carry out the II Method are illustrated in Fig. 4.5 and described hereafter.

― Step 1: Relevance of investigated PI

Before evaluating the impact indices, the building energy end uses must be analyzed by means of a preliminary simulation run. This step is essential to understand whether heating and cooling energy represent a significant enough share of the overall uses to justify further investigation. In fact, it is possible that heating and/or cooling energy represent only a negligible share of the building’s total energy consumption; in which case – if the simulation purpose is an assessment of the impact of OB on the overall building energy performance – this investigation is not prioritized. It is important to note that the II Method currently only covers heating and cooling demand; however, as mentioned, it can be expanded in the future to cover other PIs. In this case, the first step should concern whether the investigated PI is relevant.

― Step 2: Definition of heating/cooling mode If heating and/or cooling represent a significant share of the total energy consumption, the heating and/or cooling mode may be defined. A number of methods are available in literature to perform this operation (International Organization for Standardization 2005). For the case at hand, using a seasonal definition according to climate alone would compromise the scope of the study because building and operation parameters play a significant role in defining heating and cooling seasons. Hence, it is proposed to also use the preliminary year-long simulation to obtain specific heating and cooling seasons for the investigated building. However, for some buildings, heating and cooling may be present during the same day, thus it could be difficult to clearly distinguish heating and cooling seasons. For such buildings, an alternative method to calculating heating and cooling season is to simply consider the total occupied hours when heating and cooling are present. Such hours are defined in this thesis as heating mode and cooling mode.

― Step 3: Impact indices calculation Once heating and cooling modes are assessed and separately considered, the impact indices can be calculated (see Section 4.2).

― Step 4: Verification of II results As mentioned in Section 4.2.1, because the impact indices for equipment, lighting, and blind use and occupant presence are based on the relative weight of the heat gains on the sensible heat balance, a few verifications are in place. The impact indices should be weighted for a utilized gain fraction (see Section 4.2.3) in those cases where the fraction of heat gains that does contribute to increasing the cooling demand or reducing the heating demand is suspected to be very low. Alternatively, the simulation user

could perform an OAT sensitivity analysis to verify the impact on the results of changing the input. However, doing so would increase the number of simulations.

― Step 5a: Estimation of sensitivity to OB aspect The sensitivity of the results to the different OB aspects, or the potential impact of OB aspects on the results, can now be estimated. While the impact indices provide information concerning the potential influence of OB on the energy performance of a building, the actual impact is subject to a number of factors. The effect on the energy demand will depend on the description of the heat gain (e.g., the accurate description of convective vs. radiant heat ratio), on the building itself, and on the likelihood of a given aspect of OB to vary and the magnitude of this variation.

Start

Are heating/ Step 1 cooling demand relevant?

yes no

Define heating/ Step 2 cooling mode

Calculate II for Step 3 uncertain OB aspects

Step 4 Verify II results

Estimate PI Estimate likelihood Step 5a Step 5b sensitivity to OB and magnitude of aspect n variation of OB aspect n

Step 6 high risk Estimate risk

Step 7 Take further action low risk

End

Fig. 4.5: Methodological steps of the Impact Indices Method for initial OB sensitivity screening

― Step 5b: Estimation of likelihood and magnitude of variation For this reason, the likelihood and magnitude of variation of an aspect of OB is estimated in this important step of the methodology. Estimating the likelihood and magnitude of variation of an OB aspect may be complicated at times, but it is required to draw sound conclusions on the estimated risk. For example, the cooling demand of a building may turn out to be highly influenced by the blind operation (i.e., the

, may be very high), but the building manager may know from experience/data that in the building under investigation the blinds are seldom operated. Hence, because the likelihood of variation of blind operation is extremely low, the resulting risk that the blind operation will affect the cooling demand of such a building is also low. However, because this risk would be high if occupants were to operate the blinds more actively, the building manager could consider investing in automatic blinds as an energy saving measure. It is worth noting that the estimation of likelihood and magnitude of variation essentially means quantifying the uncertainty related to an OB aspect.

― Step 6: Estimation of risk The actual risk of a building’s heating/cooling demand of being highly influenced by a given OB aspect is estimated in this step. The risk estimation can be done qualitatively, or by using widespread semi-quantitative tools. Among such tools, the risk matrix (see Fig. 4.6) is handy as it allows consideration of both the sensitivity expressed in terms of impact indices and the likelihood and magnitude of variation in a single indicator, similarly to the uncertainty-impact matrix presented in Fig. 3.4. The risk estimation can lead to two outputs for each OB aspect: either an aspect is considered to be “risky” (i.e., influential and having high likelihood of variation), or an aspect is considered to be “non-risky” (i.e., low influence and/or low likelihood of variation). If low risk is detected, the simulation user is led to the end. In fact, for a non-risky OB aspect, the lowest model complexity is appropriate and no further consideration should be given.

― Step 7: Take further action Instead, if high risk is detected, further action should be taken. Depending on the purpose of the simulation, different actions may be appropriate. As mentioned, the II Method could be used for EPC. In this case, “further action” could involve inserting OB-related clauses in the contract to reduce the risk of EPC failure or collecting operational data to provide a more accurate assessment of the risk. Alternatively, in other cases “further action” corresponds to advancing to the next step of the FFP- OBm strategy.

Risky

Extensive Potentially risky Non-risky

Major

Medium OB ImpactOB

Minor

No impact

Highly unlikely Unlikely Possible Likely Very likely OB Likelihood/magnitude of variation Fig. 4.6: An example of risk matrix

The developed II Method is demonstrated in the following section, as are the results of its reliability test.

4.4 Virtual experiment to test the proposed method

A virtual experiment was developed to evaluate the effectiveness and significance of the newly proposed impact indices. The test was performed on 16 building variants in two locations (described in Section 4.4.1). A OAT sensitivity analysis was performed (see Section 4.5) as a reliability check for the impact indices results.

Description of the case study

A six-story office building with dimensions of 24 x 16 x 18 m3 was modeled in EnergyPlus version 8.3 and used for the virtual experiment. Each floor of the building comprises 12 office rooms with dimensions of 3.4 x 6.1 x 2.7 m3 and two service rooms aligned on the northern and southern sides of a corridor (Fig. 4.7). Large windows of 2.4 x 1.2 m2 are situated on the external walls of the office rooms. The building is occupied according to ASHRAE Standard 90.1 schedules (ASHRAE 2013).

Fig. 4.7: Building geometry (a) and plan of a typical floor layout (b) (in white the occupied zones)

Heating and cooling are provided by means of an ideal system, which keeps the indoor temperature within the setpoint limits (Tsp, heating = 21ºC and Tsp, cooling = 24ºC) between 7 am and 10 pm during weekdays, and between 7 am and 6 pm on Saturdays. During the remaining hours, temperature setbacks are set at 15.6 ºC and 26.7 ºC for heating and cooling season, respectively. In order to study the influence of occupant behavior on a larger set of buildings, 16 building variants were modeled. The variants include two floor heights (III and VI floor), as well as two construction types, two climates and two extreme values of equipment power density (EPD) and light power density (LPD). Table 4.1 illustrates the characteristics of the investigated building variants.

Table 4.1: Characteristics of the investigated building variations

Thermal insulation Power Density

Wall R- Window U- Visible Building g-value Lights Equipment Climate Floor value value Transmittance ID [-] [W/m2] [W/m2] [m2K/W] [W/m2K] [-] AMS1 AmsterdamIII 4 1.1 0.29 0.48 5.38 5.38 AMS2 Amsterdam III 4 1.1 0.29 0.48 16.14 16.14 AMS3 Amsterdam VI 4 1.1 0.29 0.48 5.38 5.38 AMS4 Amsterdam VI 4 1.1 0.29 0.48 16.14 16.14 AMS5 Amsterdam III 1.3 3 0.73 0.75 5.38 5.38 AMS6 Amsterdam III 1.3 3 0.73 0.75 16.14 16.14 AMS7 Amsterdam VI 1.3 3 0.73 0.75 5.38 5.38 AMS8 Amsterdam VI 1.3 3 0.73 0.75 16.14 16.14 ROM1 Rome III 4 1.1 0.29 0.48 5.38 5.38 ROM2 Rome III 4 1.1 0.29 0.48 16.14 16.14 ROM3 Rome VI 4 1.1 0.29 0.48 5.38 5.38 ROM4 Rome VI 4 1.1 0.29 0.48 16.14 16.14 ROM5 Rome III 1.3 3 0.73 0.75 5.38 5.38 ROM6 Rome III 1.3 3 0.73 0.75 16.14 16.14 ROM7 Rome VI 1.3 3 0.73 0.75 5.38 5.38 ROM8 Rome VI 1.3 3 0.73 0.75 16.14 16.14

Virtual experiment results and observations

4.4.2.1 Energy end uses analysis

First, the energy end uses were analyzed for the 16 building variants (Step 1). Fig. 4.8 reports

the absolute value of total primary energy consumption for each variant, as well as the share of energy used for lights, equipment, cooling and heating. The primary energy consumption of the 16 building variants is within the range of 199.1 GJ (AMS1) – 825.3 GJ (AMS8). It is important to note that heating and cooling supplied by the ideal system are considered as district heating and cooling within EnergyPlus. The primary energy is calculated from the site energy with the standard EnergyPlus conversion factors (i.e., 3.167, 1.056, and 3.613 for electricity, district cooling and district heating, respectively). Heating and cooling energy use were considered relevant if they represented > 5% of the total primary energy use. Hence, the impact of OB on heating energy was evaluated for all variants except AMS2, ROM1, ROM2, ROM4; the impact of OB on cooling energy was evaluated for all variants except AMS5, AMS7, and AMS8.

Light Energy Use [GJ]Equipment Energy Use [GJ] Cooling Energy Use [GJ] Heating Energy Use [GJ]

Fig. 4.8: Total primary energy use [GJ] and energy end uses of the 16 building variants

4.4.2.2 Length of heating and cooling season

The results for the length of heating and cooling season (Step 2) for the building variants described in Table 4.1 are reported below (Table 4.2). In this case, the investigated building variants are virtual buildings and the HVAC system controls could thus be optimized so that no cooling and heating occur during the same day. Therefore, it was possible to simply use a preliminary year-long simulation to obtain specific heating and cooling seasons. Outside of such seasons, the cooling and heating are set to zero, respectively.

Table 4.2: Length of heating and cooling season for all investigated building variants

Building ID Heating season Cooling season

AMS1 1/01 – 30/04 | 9/10 – 31/12 1/05 – 8/10 AMS2 - 1/01 – 31/12 AMS3 1/01 – 31/05 | 1/10 – 31/12 1/06 – 30/09 AMS4 1/01 – 31/03 | 21/10 – 31/12 1/04 – 20/10 AMS5 1/01 – 31/05 | 1/09 – 31/12 - AMS6 1/01 – 23/05 | 1/09 – 31/12 24/05 – 31/08 AMS7 1/01 – 30/06 | 1/09 – 31/12 - AMS8 1/01 – 23/05 | 1/09 – 31/12 - ROM1 - 1/02 – 23/12 ROM2 - 1/01 – 31/12 ROM3 1/01 – 31/03 | 1/11 – 31/12 1/04 – 31/10 ROM4 - 1/01 – 31/12 ROM5 1/01 – 31/03 | 27/10 – 31/12 1/04 – 26/10 ROM6 1/01 – 30/04 | 27/10 – 31/12 1/05 – 26/10 ROM7 1/01 – 30/04 | 14/10 – 31/12 1/05 – 13/10 ROM8 1/01 – 30/04 | 14/10 – 31/12 1/05 – 13/10

4.4.2.3 Calculated impact indices

The impact indices for heating and cooling were calculated (Step 3) for each building variant following the approach presented in Sections 4.2.1 and 4.2.2. The impact indices for blind operation were calculated only with respect to cooling energy use because blind operation is here considered for thermal purposes only, rather than also for visual purposes. Table 4.3 contains all impact indices results. Generally speaking, Table 4.3 shows a high variability of impact indices according to building vintage, climate, considered aspect of OB and performance indicator of interest. A varied effect

of OB on the analyzed building variants is hence expected. For example, the impact indices describing the effect of equipment use on cooling energy use are higher in Amsterdam, where summers are milder, than in Rome, where most of the cooling load is likely related to weather conditions. An analysis of the II results allows estimation of the sensitivity of heating/cooling demand to various aspects of OB (Step 5). For instance, the impact indices for equipment use, heating are very low for AMS5, AMS7, ROM5, ROM7, indicating a low potential effect of such OB aspects on the heating demand of the mentioned building variants.

Table 4.3: Impact indices results for all investigated building variants and aspects of OB

II Equipment II Occupant II Blind

Use II Light Use Presence Operation II Tsp Setting Building Cooling Heating Cooling Heating Cooling Heating Cooling Cooling Heating ID

AMS1 0.76 1.09 0.52 0.74 0.27 0.42 1.15 0.48 0.13

AMS2 1.11 - 0.76 - 0.13 - 0.44 0.58 -

0.89 0.45 0.61 0.31 0.32 0.18 1.51 0.47 0.29 AMS3 AMS4 1.10 2.81 0.75 1.91 0.13 0.33 0.57 0.53 0.07

AMS5 - 0.15 - 0.10 - 0.06 - - 0.58

AMS6 1.10 0.61 0.75 0.41 0.13 0.08 1.66 0.63 0.48

AMS7 - 0.13 - 0.09 - 0.05 - - 0.62

AMS8 - 0.45 - 0.31 - 0.06 - - 0.53

0.46 - 0.31 - 0.16 - 0.89 0.60 - ROM1 ROM2 0.69 - 0.47 - 0.08 - 0.43 0.57 -

ROM3 0.37 0.95 0.25 0.64 0.13 0.36 0.79 0.14 0.31

ROM4 0.83 - 0.57 - 0.10 - 0.52 0.66 -

ROM5 0.22 0.31 0.15 0.21 0.08 0.12 1.29 0.75 0.51

ROM6 0.51 1.63 0.35 1.11 0.06 0.18 0.95 0.72 0.31

0.22 0.23 0.15 0.16 0.08 0.09 1.27 0.72 0.54 ROM7 ROM8 0.51 1.12 0.35 0.76 0.06 0.13 0.97 0.72 0.38

4.4.2.4 Verification of impact indices results: equipment use for heating (,)

As an example, the impact indices results are verified (Step 4) for the case of equipment use for heating. This step is dashed in the method flowchart because the choice of verification is up to the simulation user, who will decide based on his/her building and on the values of the impact index. For the sake of simplicity, only the impact indices obtained for equipment use in heating mode (,) were verified in all building variants. The utilized gains fraction (UGF) was

first calculated (see Fig. 4.9). This operation was performed by setting the equipment gains to zero for each case and comparing the obtained results with the base case. By doing so, it was possible to derive the ratio of equipment gains that was indeed used to offset the heating demand. This ratio is named the utilized gains fraction.

100%

80%

60%

40% UGF [%]

20%

0% AMS1 AMS3 AMS4 AMS5 AMS6 AMS7 AMS8 ROM3 ROM5 ROM6 ROM7 ROM8 Building ID

Fig. 4.9: Utilized Gain Fraction (UGF) for investigated building variants (equipment use, heating)

Fig. 4.10 shows the original , alongside the recalculated II weighted by the UGF. It is evident that the initial assessment, i.e. the fact that equipment use is non-influential for heating demand in the cases of AMS5, AMS7, ROM5 and ROM7, does not change. This conclusion necessarily depends on the determined sensitivity limits. As expected from Fig. 4.9, Fig. 4.10 also shows that the magnitude of the overestimation provided by the standard II differs according to building variant.

3.00 2.50 2.00

II 1.50 1.00 0.50 0.00 AMS1 AMS3 AMS4 AMS5 AMS6 AMS7 AMS8 ROM3 ROM5 ROM6 ROM7 ROM8 Building ID

II,EQ,H RECALC II,EQ,H

Fig. 4.10: Comparison between original II and recalculated II by weighting for UGF given in Fig. 4.8

The value for the sensitivity limit should be set according to the requirements of the party involved in the decision-making process. For example, it can be assumed that all cases with a II < 0.2 are negligible. All other cases are considered influential, and hence should proceed to the next steps of the FFP-OBm strategy (see Table 4.4). It is worth mentioning that while presence may have a negligible effect on cooling/heating demand because of its related heat gains, whether people are present or not may also affect other aspects of OB. The required accuracy when modeling presence is hence subject to this consideration. It has to be noted that because the virtual experiment includes a set of fictitious buildings, the steps of the II Method involving the estimation of likelihood and magnitude of OB variation are not addressed here since they involve information coming from knowledge. The complete II Method was applied to existing buildings. The results of these applications are presented in Chapter 7.

Table 4.4: Impact indices results assessment: crossed cells are considered non-influential as II<0.2

II Equipment II Lights II Presence Blinds II Tsp Building Cooling Heating Cooling Heating Cooling Heating Cooling Cooling Heating ID

AMS1 - - - - AMS2

AMS3

AMS4 - - - - - AMS5

AMS6 - - - - - AMS7 - - - - - AMS8 - - - - ROM1 - - - - ROM2

ROM3 - - - - ROM4

ROM5

ROM6

ROM7

ROM8

4.5 Reliability test

Description of the one-at-a-time (OAT) sensitivity analysis

A OAT sensitivity analysis is carried out to evaluate whether the developed impact indices yielded significant results. The OAT sensitivity analysis is performed in a simplified manner by modifying OB-related parameters as shown in Table 4.5. Blinds are operated according to façade orientation; in particular, they are lowered in the East façade between 6 am and 2 pm, in the South façade 9 am – 6 pm, and in the West façade 1 pm – 9 pm. The blinds are modeled as roller blinds with thickness = 0.01 m, conductivity = 1 W/mK, and solar transmittance = 0.05. These amendments are for illustrative purposes; the actual variation in OB may differ according to the specific case and designated use of the building.

Table 4.5: Amendments made to operation parameters to perform OAT sensitivity analysis

Operation parameters Amendment made

Tsp, heating [C] ±1ºC

Tsp, cooling [C] ±1ºC

Equipment Power Density (EPD) [W/m2] ±50%

Lighting Power Density (LPD) [W/m2] ±50%

Occupant presence density [occupant/office] ±50%

Blind Operation Closed according to façade orientation

As mentioned earlier, the OAT sensitivity analysis is used as a reliability test of the presented approach. The effect of applying the variations presented in Table 4.5 heating and cooling energy is considered to be the ground truth, or the actual influence that different aspects of (simplified) OB have on heating and cooling energy. An example of the results obtained by means of the OAT sensitivity analysis is reported in Fig. 4.11. Fig. 4.11 shows that applying the changes presented in Table 4.5 results in a positive and negative variation in the energy use if compared to the base case. For example, increasing the heating temperature setpoint of AMS7 by 1 °C results in a 9% increase of the heating energy (i.e. 14.33 GJ), while decreasing it by 1 °C reduces the heating energy by 9% (or 13.05 GJ). Likewise, decreasing e.g. the EPD is expected to have an increasing effect on the heating energy, while decreasing the EPD should have the contrary effect. For the sake of simplicity, all results of the OAT analysis are reported in Table 4.6 as mean values of the absolute increase/decrease in energy resulting from the OB variations (in the example case, corresponding to |13.69| GJ).

20 40%

10 9% 20% 0% 0 -9% 0% [%] [GJ] - 1°C BASE + 1°C -10 -20%

-20 -40%

Abs Value % Change

Fig. 4.11: Influence of changing the temperature setpoint on heating energy (AMS7)

Table 4.6: OAT sensitivity analysis results: mean values of absolute energy variations

Effect of Effect of Effect of Effect of Light Blind Effect of Tsp Equipment Use Occupant Use [%] Operation Setting [GJ] [%] Presence [%] [%] Building Cooling Heating Cooling Heating Cooling Heating Cooling Cooling Heating ID AMS1 24% 33% 16% 22% 13% 14% -16% 3.39 2.58 AMS2 36% - 26% - 7% - -6% 5.50 - AMS3 24% 15% 17% 10% 14% 6% -16% 3.46 5.29 AMS4 40% 61% 29% 37% 8% 6% -7% 6.39 3.27 AMS5 - 6% - 4% - 2% - - 11.93 AMS6 27% 20% 20% 13% 5% 3% -41% 8.13 9.94 AMS7 - 5% - 3% - 2% - - 13.69 AMS8 - 16% - 10% - 2% - - 11.71 ROM1 15% - 10% - 8% - -12% 8.13 - ROM2 25% - 17% - 5% - -6% 4.99 -

ROM3 12% 24% 9% 18% 7% 11% -10% 5.64 3.03

ROM4 26% - 18% - 5% - -6% 7.48 -

ROM5 6% 9% 4% 6% 3% 4% -36% 11.49 7.43

ROM6 16% 30% 11% 19% 3% 4% -28% 13.69 5.46

ROM7 6% 7% 4% 5% 3% 3% -33% 12.08 8.66 ROM8 15% 24% 10% 15% 3% 3% -26% 14.25 6.78

The effect of applying blinds is reported solely as energy reduction as it is only calculated for cooling energy (and the blinds have a beneficial effect on cooling energy use, as expected).

Table 4.6 shows that the effect of changing the EPD by ±50% causes a relative variation in cooling energy of 6% (ROM5) to 40% (AMS4). ROM5 is a skin-load dominated building, where heating and cooling together cover 67% of the primary energy use, while AMS4 is an internal

load dominated building (heating and cooling together cover 20% of the total primary energy use). The relative variation of heating energy is 6% (AMS5) – 61% (AMS4). In contrast, changing the LPD by ±50% has a 4% – 29% effect on cooling and 3% – 37% effect on heating. Presence has a smaller influence on cooling and heating, causing a variation of 3% – 14% and 2% – 14%, respectively. The maximum cooling reduction due to blind operation is registered in ROM5 (-36%), while the minimum occurs in AMS2 and ROM4 (-6%). In absolute terms, the temperature setpoint has a higher effect on buildings characterized by a low thermal insulation. In particular, the largest effect of varying the cooling setpoint occurs in ROM5 – ROM8, while a variation in heating setpoint has most influence in AMS5 – AMS8. To determine if the impact indices led to a satisfactory estimation of the potential impact of OB, the impact indices results are plotted against those obtained by means of the OAT analysis for each aspect of OB (cooling and heating energy need are shown separately) (Fig. 4.12 – Fig. 4.16).

Fig. 4.12: II reliability test: comparison with OAT sensitivity analysis results for the impact of equipment use on cooling (left) and heating (right) energy. Each dot represents a building variant Heating energy variation due to ±50% LPD [%] ±50% due to variation energy Heating

Fig. 4.13: II reliability test: comparison with OAT sensitivity analysis results of the impact of light use on cooling (left) and heating (right) energy

Fig. 4.14: II reliability test: comparison with OAT sensitivity analysis results for the impact of occupant presence density (PD) on cooling (left) and heating (right) energy

Fig. 4.15: II reliability test: comparison with OAT sensitivity analysis results for the impact of temperature setpoint on cooling (left) and heating (right) energy

Fig. 4.16: II reliability test: comparison with OAT sensitivity analysis results for the impact of applying the blinds on cooling energy

Fig. 4.17 shows the results of the II compared to the weighted II for the case of ,. While in both cases a very high R2 value is obtained, it is evident that weighting the II results in an even better comparison with the OAT, as expected.

0.70 0.70 0.60 0.60 0.50 0.50 0.40 0.40 0.30 0.30 OAT [%] OAT [%] 0.20 0.20 y = 0.1986x + 0.0441 2 0.10 0.10 y = -0.0659x + 0.4095x + 0.0181 R² = 0.947 R² = 0.9897 0.00 0.00 0.00 1.00 2.00 3.00 0.00 1.00 2.00 3.00 II Weighted II

Fig. 4.17: Comparison of reliability test for standard II (left) and weighted II (right)

4.6 Discussion and methodological limitations

The impact indices, calculated by means of a single simulation run, have a significant correlation with the results of the OAT sensitivity analysis. In particular, the R2 values for both heating and cooling reliability tests with regard to equipment use, light use and occupant presence were always >90%. This outcome reveals that >90% of the effect on cooling and heating energy due to variations in the mentioned aspects of OB can be explained by the impact indices. When analyzing the effect of blind use and temperature setpoints, approximately 85% of the variations could be explained. These results demonstrate how a method based on a single simulation run can yield significant estimates of the potential effect of equipment use, light use, blind use, occupant presence and setting of the temperature setpoint on heating and cooling energy. A number of simplifications made in this study ought to be pointed out: i) the considered building variants are purposely extreme variations (e.g., EPD and LPD = 16.14 W/m2 may not be representative of common office buildings); ii) the aspects of OB are modeled as static variations rather than stochastic implementations of occupants’ diversity; the variations presented in Table 4.2 are intentionally exaggerated for the purposes of this study; iii) the choice of supplying energy by means of an ideal system could correspond to the initial phases of the building design, but is not representative of a more advanced stage; iv) there exist some

limitations with the II themselves, as highlighted in Section 4.2.3. Nevertheless, the proposed initial screening method proved to be efficient and effective in providing information about the potential effect of a number of OB aspects on heating and cooling energy demand. Among the envisioned applications of the method is screening of non-influential OB aspects to determine the FFP-OBm complexity, and investigating OB-related risks for energy performance contracts.

4.7 Concluding remarks

The first step of the FFP-OBm strategy, namely the II Method, is presented in this chapter. The II Method was developed to quickly forecast the potential influence of blind operation, light and equipment use, occupant presence, and temperature setpoint setting on building cooling and heating energy demand. The method seeks to fill the existing gap for a fast, simple method to estimate the impact of various OB aspects on building energy performance, which is found to be dependent on many factors. The impact indices were defined, applied to 16 building variants, and tested by comparing their results with those of OAT sensitivity analysis. The outcome of this testing was positive and confirmed the capability of the II to forecast OB influence. The possible limitations of the method were pointed out, and particularly concerning the fact that the indices are based on the values of the building’s sensible heat balance provided by the software. As such, they may be misleading in the presence of significant latent loads and for cases where the gross and net heat gains differ considerably. In this study, the indices are instrumental in filtering out the non-influential aspects of OB for further analysis, thereby avoiding the need to create scenarios for the non-influential OB aspects in the next step of the strategy. While the screening method was developed to test the sensitivity of building heating and cooling demand to a number of OB aspects, it remains possible to propose similar screening methods that look at different performance indicators as well, such as overall energy use or comfort performance.

5 The fit-for-purpose approach: the Diversity Patterns Method

Charles M. Schulz, Peanuts

This chapter discusses the second step of the fit-for-purpose approach: The Diversity Patterns Method. The introduction (Section 5.1) explains the standing of the Diversity Patterns Method within the overall FFP- OBm strategy. Section 5.2 provides the theoretical basis for the method, which is based on the application of diversity patterns and on the statistical Mann-Whitney U test. The diversity patterns aim at quantifying the sensitivity of a building/performance indicator to uncertain OB aspects, while the Mann-Whitney U test allows for the distinguishing of influential and non-influential OB aspects, thereby refining the screening presented in Chapter 4. Then (Section 5.3), a virtual experiment is presented, with the scope of testing the Diversity Patterns Method. Section 5.4 describes a reliability test conducted to evaluate the validity of the results achieved in Section 5.3. The method is discussed in Section 5.5, while conclusions are provided in Section 5.6.

NB: The content of this chapter is largely inspired by an original research article published in the Journal of Building Performance Simulation under the title: Isabella Gaetani, Pieter-Jan Hoes, and Jan L. M. Hensen (2018). On the sensitivity to different aspects of occupant behavior for selecting the appropriate model complexity in building performance predictions https://doi.org/10.1080/19401493.2016.1260159 (Gaetani, Hoes, and Hensen 2016b)

5.1 Introduction

The Impact Indices Method presented in Chapter 4 yielded two possible outcomes: either an OB aspect was categorized as trivial for the results, in which case it is recommended to use the lowest model complexity possible. Alternatively, if an OB aspect was categorized as having a potential influence on the results, the Diversity Patterns Method, presented hereafter, should be applied. Put simply, the Diversity Patterns Method seeks to further refine the Impact Indices Method. In particular, a number of steps were defined to test the actual sensitivity of a building/performance indicator to different aspects of OB. The Diversity Patterns Method also extends the Impact Indices Method in terms of performance indicators. In fact, it can be applied to any performance indicator or purpose of simulation, whereas the Impact Indices Method is currently designed as a fast screening method to test the sensitivity to OB of heating and cooling energy demand only. While the effort of applying the Diversity Patterns Method is surely higher than the effort required by the Impact Indices Method, it is still lower than applying higher complexity models in terms of expertise and computational demand. Moreover, as often declared within this thesis, it follows the simple precept that everything should be made as simple as possible, but not simpler.

5.2 Methodological steps

A method is proposed to accomplish a twofold objective: i) to quantify the sensitivity of results to different aspects of occupant behavior; and ii) to distinguish influential from non-influential aspects. The overall hypothesis is that a higher model complexity might be needed for the influential aspects of occupant behavior. As the influential aspects presumably depend on the building, climate, purpose of simulation (performance indicator) and use scenario (Tahmasebi and Mahdavi 2016; Gaetani, Hoes, and Hensen 2016a), this hypothesis highlights how the appropriate model complexity might be derived from the object – and objective – of the simulation.

Quantify the sensitivity of results to different OB aspects

The sensitivity of a building/performance indicator to occupant behavior is quantified by applying the diversity patterns themselves. The diversity patterns are (high/low) possible variations of uncertain aspects of OB that the Impact Indices Method categorized as non-trivial.

As such, they are implemented by means of schedules or other built-in deterministic software assumptions. The reason behind formulating such diversity patterns is to preliminarily test the influence of different aspects of OB on a building/performance indicator. Implementing diversity patterns is considered to be the same model complexity as required by standard schedules and assumptions, or complexity 0. However, the diversity patterns add diversity to standard schedules and IF-THEN models by considering a set of occupants. Typically, if the building/performance indicator is at all influenced by the OB aspects (as presumed, given the preliminary screening by means of impact indices), one expects the resulting value to move from a single value to a spread of values. An effective way to represent such a distribution is by means of boxplots (see Fig. 5.1). Once the patterns are defined, they are implemented automatically to the EnergyPlus .idf file by means of code written in MATLAB and available at https://data.4tu.nl/repository with DOI 10.4121/uuid:3e03a5ac-b1c9-42a4-b7f4-6ace04c23b90.

Fig. 5.1: Boxplot representing the simulation results from diversity patterns. In this case, the median value of the boxplot corresponds to the result obtained by means of a single simulation run (standard approach)

Depending on the purpose of the simulation, implementing diversity patterns may be sufficient to make a decision (see Section 3.2.2). For example, the purpose of the simulation may be to ensure that a given threshold of overheating hours is not exceeded. In this case, if the whole spread of values obtained by applying the diversity patterns is under such a threshold, the simulation user has accomplished his/her task and can make an informed decision. In the example in Fig. 5.2, the performance indicator of Design A always falls below the maximum threshold value when applying diversity patterns. In this case, the informed decision of preferring Design A to Design B can be made without increasing model complexity.

The diversity patterns find their origins in available, simple examples of sensitivity analysis to OB (e.g., (Hong and Lin 2012a)). However, in contrast with available literature, it is preferred to abstain from defining a priori whether a diversity pattern is associated with “conscious” or “wasteful” behavior. In fact, while for certain OB aspects this categorization may be obvious

(e.g., a higher cooling Tsp will always yield to energy savings), in other cases it may be more complex (e.g., use of equipment contributing to both increasing electricity consumption and offsetting heating demand).

Fig. 5.2: Spread of performance indicator of two design alternatives

Moreover, previous research (Gaetani, Hoes, and Hensen 2016a) suggested that diversity patterns should not be implemented one-at-a-time, but rather in all possible combinations. In fact, considering only the one-at-a-time variations may lead to severe underestimations of the sensitivity to OB. Fig. 5.3 shows the influence of various operation parameters on the heating energy demand and heating peak load of an EnergyPlus mid-size reference office building located in Chicago, Illinois. This figure illustrates well how considering each operation parameter singularly, rather than in all possible high/low combinations, would lead to underestimating the potential influence of OB. For this reason, it is proposed to test all possible combinations of diversity patterns of uncertain OB aspects.

(a) (b)

All All Presence Rate Presence Rate Presence Schedule Presence Schedule LPD LPD Light Use Sch. Light Use Sch. EPD EPD Equipment Use Sch. Equipment Use Sch. Tsp, c Tsp, c Tsp, h Tsp, h HVAC Sch. HVAC Sch.

-100 -50 0 50 100 150 200 -100 -50 0 50 100 150 200 Percentage change of heating energy use [%] Percentage change of heating peak load [%] High Value Low Value

Fig. 5.3: Influence of operation parameters on heating energy consumption (a) and heating peak load (b) of the EnergyPlus Mid-size office reference building (for further details see (Gaetani, Hoes, and Hensen 2016a))

Distinguish influential from non-influential aspects

After investigating a number of statistical methods, the Mann-Whitney U test was deemed appropriate to accomplish the second objective, i.e. to determine whether an aspect of occupant behavior is relevant for the results. The Mann-Whitney U test is a nonparametric statistical test that is used to assess whether two independent groups are significantly different from each other. Here, the two groups are characterized by all combinations with the same pattern for an uncertain aspect of OB. In particular, all results characterized by a certain type of OB, referred to as pattern A, are compared with those with the same type of behavior, referred to as pattern B. The test was initially designed by Wilcoxon for two samples of the same size, and it was further developed in 1947 by Mann and Whitney to cover different sample sizes. Its strength in comparison to sensitivity analysis methods that are traditionally used in BPS (Tian 2013) is that this test is able to process correlated and non-correlated inputs, whose variation does not follow a uniform or normal distribution. Moreover, in contrast to the t-test, it does not compare mean scores but median scores, which renders it much more robust against outliers and heavy tail distributions. Please refer to Appendix C: Mathematical formulation of Mann-Whitney U test for the mathematical principles related to the Mann-Whitney U test. In practice, the test is applied to the results through a MATLAB code (available at https://data.4tu.nl/repository) to allow a quantitative assessment of whether an OB aspect can be defined as influential or non-influential. There is a statistically significant influence of the behavior on the results if the statistical p-value < 0.05. The sensitivity analysis allows the

identification of those aspects of OB that are responsible for the spread in the results, so that more attention can be directed towards modeling such aspects. Fig. 5.4 is a schematic representation of the steps that must be followed to apply the Diversity Patterns Method.

yes

Definition of Run simulations and diversity patterns Can make Identify influential Start assess potential OB no End for uncertain OB decision? OB aspects influence aspects

Fig. 5.4: Schematic of the Diversity Patterns Method

The proposed method is tested by means of a virtual experiment, the details of which are given in Section 5.3.

5.3 Virtual experiment to test the proposed method

First, the case study is illustrated (Section 5.3.1). Second, the diversity patterns for the uncertain aspects of OB are defined (Section 5.3.2). Next, the performance of the building variants and the potential OB influence are assessed (Section 5.3.3). Then, if a decision cannot be taken, or if the range in the performance indicator shows an effect of OB due to the patterns worthy of further investigation, a sensitivity analysis by means of the Mann-Whitney U test takes place to identify the influential aspects of OB (Section 5.3.4).

Description of the case study

5.3.1.1 Building

A testbed made of 16 different variants of a cubicle office was defined using EnergyPlus version 8.3 (Fig. 5.5 and Table 5.1). Different buildings, climates, purposes of simulation and use scenarios were defined to verify the hypothesis that various aspects of OB are influential in different cases. Therefore, the appropriate OB model complexity depends on the case at hand. The buildings’ characteristics, as well as the choice of climates, will necessarily have an impact on the sensitivity of the results to certain aspects of OB. For example, a relatively small openable window fraction may result in a lower-than-expected sensitivity to window operation.

Case 1 Case 2 Case 3 Case 4

Fig. 5.5: Impression of the case study building variants. Each variant is investigated for two climates and two lighting and equipment power densities

The office dimensions are 553 m3, and the south-facing wall faces outside, while all other walls, ceiling and floor are assumed to be adiabatic, as if the office was surrounded by other cubicles in thermal equilibrium with it. An operable window equipped with an external shading device is placed on the external wall. Two climates are considered: Amsterdam, the Netherlands, and Rome, Italy. Two variations of window-to-wall ratio (WWR) are defined, namely 40% and 80%. The fraction of window that can actually open is set as 10% of the total window area, which corresponds to 0.6 m2 for WWR=40% and 1.2 m2 for WWR=80%. The other variations concern the power density of lights and equipment, and the construction of wall and window, which resulted in a total of 16 building variants (see Fig. 5.5 and Table 5.1). Heating and cooling are provided by means of an ideal system, whose size was capped based on the results of preliminary simulation runs. As for the phase in the building lifecycle, this study investigates the conceptual design phase only, when no data about the actual building performance is available.

5.3.1.2 Performance indicators

Heating and cooling energy and weighted overheating hours are the selected performance indicators. Weighted overheating hours (WOH [h]) are calculated with the simplified formula

∙ 0 (5.1)

where h is occupied hour i, Top is the operative temperature [°C] at hour i, n is the number of occupied hours, and Tmax is the maximum allowed temperature. Tmax is here assumed to be 28°C, corresponding to Class D temperature summer limits in actively cooled buildings (Boerstra, van Hoof, and van Weele 2015). For comfort-related performance indicators, a maximum acceptable value is typically defined. A threshold of 500 h is taken for illustrative purposes. The order of magnitude of this figure is based on the daily limits for weighted exceedance. According to (CIBSE 2013), the number of WOH shall be less than or equal to 6 in any one day in the cooling

season, or equal to 109 (weekdays May 1st – September 30th) 6 = 654 h. It is supposed that when no-adaptive Tmax are considered, as in the case of active cooling, the limit should be more stringent.

Table 5.1: Characteristics of the investigated building variants

Thermal insulation Power Density

Wall R- Window U- g- Visible Building WWR Lights Equipment Climate value value value Transmittance ID [%] [W/m2] [W/m2] [m2K/W] [W/m2K] [-] [-] 1 Amsterdam 40 4 1.1 0.29 0.48 15 10 2 Amsterdam 40 4 1.1 0.29 0.48 5 3 3 Amsterdam 40 1.3 3 0.73 0.75 15 10 4 Amsterdam 40 1.3 3 0.73 0.75 5 3 5 Amsterdam 80 4 1.1 0.29 0.48 15 10 6 Amsterdam 80 4 1.1 0.29 0.48 5 3 7 Amsterdam 80 1.3 3 0.73 0.75 15 10 8 Amsterdam 80 1.3 3 0.73 0.75 5 3 9 Rome 40 4 1.1 0.29 0.48 15 10 10 Rome 40 4 1.1 0.29 0.48 5 3 11 Rome 40 1.3 3 0.73 0.75 15 10 12 Rome 40 1.3 3 0.73 0.75 5 3 13 Rome 80 4 1.1 0.29 0.48 15 10 14 Rome 80 4 1.1 0.29 0.48 5 3 15 Rome 80 1.3 3 0.73 0.75 15 10 16 Rome 80 1.3 3 0.73 0.75 5 3

Definition of diversity patterns for uncertain OB aspects

A number of aspects related to OB were assumed to be uncertain, namely: occupant presence, HVAC use, equipment and light use, heating and cooling temperature setpoint, blind and window operation. All aspects are considered, regardless of how they would perform in the Impact Indices Method. The reasoning behind such a choice is that, for illustration purposes, non-influential aspects should also be kept to verify whether the method is working. In this thesis, diversity patterns are defined as perceptual structures to describe different attitudes in various aspects of OB. For the OB aspects mentioned above, diversity patterns were defined as in Table 5.2. Most of the variations are as in (Hong and Lin 2012b). As mentioned, it is preferable to avoid classifications in terms of the environmental attitude expressed by patterns (e.g., conscious behavior vs. wasteful behaviors), but rather just formulate patterns for the two extreme ends of a given OB aspect. Nevertheless, in the example provided in Table 5.2, Pattern

A can be ascribed to rather energy-efficient, active behavior, and Pattern B can be ascribed to more wasteful, passive behavior. Clearly, the assumptions made in formulating the diversity patterns will have a great impact on the results of the sensitivity analysis, as further clarified in Section 5.5. Where possible, the user of this methodology should corroborate his/her assumptions with data, so that the diversity patterns are representative for possible variations of OB. In this case, the purpose of implementing diversity patterns is to investigate the sensitivity of different building variants to standardized variations in OB.

Table 5.2: Diversity patterns for uncertain aspects of occupant behavior

Type of behavior Pattern A Pattern B Presence Mon-Fri 10-12 am and 1-4 pm Mon-Fri 8-12 am and 1-6 pm

HVAC use ON when occupied Always ON with Tsetback (15.6°C when heating, 26.7°C when cooling) Equipment use 90% when occupied; 30% when non- 100% 10am-4pm or 8am-6pm according occupied to presence; 60% before arrival and after departure Light use ON when occupied; daylighting control ON when occupied + lunch break; no daylighting control Heating setpoint [°C] 18 23 Cooling setpoint [°C] 26 22 Blind use Close if occupied, cooling and high Always open solar on window

Window operation Open if occupied, Tin > Tsp, cooling and Always closed

ΔTin-out > 2°C

The diversity patterns are not implemented one-at-a-time in the simulation model, nor Pattern A is directly compared to Pattern B, but rather all possible combinations of Pattern A and Pattern B are considered. This operation is carried out for the reasons mentioned in Section 5.2.1 and to mimic the actual behavior of people in practice, whereby a conscious attitude regarding one OB aspect does not necessarily imply such attitude in different OB aspects. In this example eight OB aspects were considered in each of the two patterns, hence the total number of combinations that are investigated are 2 (number of patterns) 8 (number of OB aspects) = 256 scenarios. Out of these 256 scenarios, 64 scenarios are characterized by Tsp,heating = 23°C and

Tsp,cooling = 22°C, which would lead to the HVAC system constantly looping between the two setpoints. Therefore, the 64 scenarios where Tsp,heating > Tsp,cooling are excluded. The total number of investigated scenarios is thus 256 – 64 = 192 scenarios. The daylighting control is implemented in the model using the EnergyPlus option Fraction Replaceable under the Lights specification. If Fraction Replaceable = 0 there is no dimming control, while if Fraction Replaceable = 1 there if full dimming control. Conversely, Pattern B for blind use and window use is implemented by means of the Shading Control Type option under

the WindowShadingControl specification and by means of the ZoneVentilation:WindandStackOpenArea specification, respectively. The Shading Control Type is set to OnNightAndOnDayIfCoolingAndHighSolarOnWindow and the maximum temperature and temperature difference are defined in the Zone Ventilation specification. It should be noted that occupant presence is not only considered in terms of internal gains due to occupant presence, but also acts as a trigger for most other adaptive actions (see Table 5.2).

Run simulations and assess potential OB influence

The results in this section are given for the impact of diversity patterns on performance indicators. Then, those cases where a decision could not be taken (e.g., WOH exceed the set threshold) are advanced to the next step, i.e. the sensitivity analysis by means of Mann-Whitney U test to define influential vs. non-influential aspects (Section 5.3.4). The impact of occupant behavior diversity patterns is evaluated for the aforementioned performance indicators and building variants. This step is taken to establish whether OB as a whole has an effect on the performance indicators. In cases where the effect of OB is negligible, it is assumed that there is no point in modeling it in further detail. In contrast, in cases where there is a visible effect of OB, a further sensitivity analysis by means of the Mann-Whitney U test is undertaken. Fig. 5.6 shows the range of cooling energy use due to OB. A significant variation in all building variants can be seen.

Fig. 5.6: Variation in cooling energy use due to diversity patterns for uncertain aspects of occupant behavior in building variants 1-16 (see Table 5.1)

Ultimately, it will depend on the purpose of the simulation whether a given variation is considered acceptable or not. Fig. 5.7 represents the impact of occupant behavior on heating energy use. For all building variants located in Rome (9 to 16), the heating energy demand was lower than 10 kWh/m2y regardless of OB. Depending on the purpose of the simulation, the relative variation may be considered important or not. In this example all building variants are considered to be sensitive to OB.

Fig. 5.7: Variation in heating energy use due to high/low patterns for uncertain aspects of occupant behavior

An analysis of the impact of OB on weighted overheating hours (Fig. 5.8) reveals that all buildings located in Rome, and two buildings located in Amsterdam (building 7 and 8, characterized by WWR=80% and low thermal insulation) exceed the threshold of 500 h. In summary, it is assumed that the effect of OB on results needs to be further examined for: cooling energy in all buildings, heating energy in all buildings, and WOH for buildings 7, 8 and 9 – 16. For these cases, a sensitivity analysis with the Mann-Whitney U test was applied to determine which aspects of OB are statistically significant for the results.

Fig. 5.8: Variation in weighted overheating hours due to high/low patterns for uncertain aspects of occupant behavior

Identify influential OB aspects

In this section, the results of the sensitivity analysis by means of Mann-Whitney U test are given and briefly commented on according to investigated performance indicator.

Sensitivity analysis of cooling energy use to occupant behavior As expected, the results of the Mann-Whitney U test show that cooling energy use depended on the HVAC use for all building variants, and did not depend on the heating setpoint temperature in any of the building variants. The results for all types of behavior are shown in Fig. 5.9. Only buildings 4, 8 and 16 (variants with low thermal insulation and low power density for both Amsterdam and Rome WWR=80%) were not influenced by occupant presence. This result can be explained as the cooling demand of such variants is highly dependent on solar heat gains and thermal exchange through the building envelope. Internal heat gains – which depend on presence – are relatively less important. In contrast, equipment use was only relevant for the results in building variants 1 and 9, characterized by low WWR, high thermal insulation and high PD. The results are relatively more sensitive to light use than equipment use due to the higher power density of lights, with only 4 variants (4, 8, 14 and 16) not being affected by this aspect of occupant behavior.

According to expectations, the cooling temperature setpoint was a decisive factor for cooling energy use in almost all buildings. The only building variant that was not sensitive is variant 15, located in Rome, with high WWR, low insulation and high-power density. As expected, in comparison to low WWRs, higher WWRs were affected to a greater extent by window opening due to the larger opening area. Of all the buildings with a WWR of 40%, only building variant 11 in Rome, characterized by low thermal insulation and high PD, showed sensitivity to window operation. As for the sensitivity to blind use, the only building variants that were not affected are 1 and 9. These variants are both characterized by low WWR, high thermal insulation and high-power density. In these variants the windows are characterized by a very low solar heat gain coefficient (SHGC), which weakens the effect of blinds. The use of blinds is shown to have a greater effect on building variants characterized by higher SHGC.

Fig. 5.9: Sensitivity of cooling energy use to various occupant behavior aspects for all building variants; building variants with 1-p>0.95 are considered sensitive (black-filled bars)

The results of the sensitivity test for heating energy and WOH are reported in Appendix D: Mann-Whitney U test for heating energy and WOH.

5.4 Reliability test

A higher model complexity was applied to the influential and non-influential aspects for two building variants to test the effect of changing model complexity for aspects of OB that showed a different sensitivity. This operation functions as a reliability test for the results obtained by the method. In fact, increasing model complexity of OB aspects identified as non-influential is supposed to have a negligible effect compared to increasing model complexity of influential OB aspects. Only the cooling energy of two building variants and three adaptive behaviors (light, blind and window operation) were selected for this purpose as illustrative examples. Building variants 1 and 16 were chosen as they show a different sensitivity of cooling energy use to light use, window operation and blind use (see Fig. 5.9). As a reminder, the hypothesis is that the non-influential aspects of OB can be modeled with the lowest complexity, while adding complexity to the influential aspects will provide further insights into the performance indicator compared with the simplistic diversity patterns. Instead of applying a higher model complexity only to the aspects of OB that are influential for the given building/performance indicator combination, both variants are considered. There are two main reasons for applying higher complexity models to both variants rather than only to the sensitive one: i) to test whether the Mann-Whitney U test leads to reliable results, (i.e. to verify that changing model complexity of a non-influential aspect does not have an impact on the performance indicator); ii) to inspect existing interrelations among aspects of OB (i.e., to understand whether a non-influential aspect of occupant behavior can be ignored when adding complexity). A higher model complexity is initially implemented for each aspect of OB one-at-a-time, and then simultaneously to investigate possible interrelations between the various aspects. It has to be noted that the initial number of scenarios due to the combinations of diversity patterns for all uncertain aspects of OB changes when performing this operation. In fact, if one aspect is modeled stochastically, the number of scenarios reduces from 192 to 96. If two aspects are modeled by means of a stochastic model, the resulting number of scenarios is 48, while if all three considered aspects are modeled in this way, there will be only 24 scenarios. The implemented occupant behavior models are well-established stochastic models taken from literature: Reinhart’s Lightswitch-2002 model (Reinhart 2004) for light use, Haldi and Robinson’s window operation model (Haldi and Robinson 2009), and Haldi and Robinson’s blind operation model (Haldi and Robinson 2010) (see Table 5.3). These models were developed for cellular offices in climates different than those considered in the case study, and there is no evidence that their combined use leads to representative results. However, they have been widely used in conjunction and for a number of buildings and climates (Gunay, O’Brien, and Beausoleil-

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Morrison 2016; Gilani et al. 2016) and represent the current state-of-the-art in OB modeling research. The occupant behavior models are here implemented in the building model by means of the EMS feature of EnergyPlus, as in (Gunay, O’Brien, and Beausoleil-Morrison 2016).

Table 5.3: Stochastic models implemented for different types of behavior

Type of behavior Stochastic model Pictogram Light use Lightswitch-2002 (Reinhart 2004)

Blind use Haldi and Robinson (Haldi and Robinson 2010) Window operation Haldi and Robinson (Haldi and Robinson 2009)

A detailed description of the models can be found in Appendix E: Stochastic models implemented in Chapter 5. The models were run an appropriate number of times (Feng, Yan, and Wang 2016) to take their stochasticity into account. A detailed description of the method used to determine the minimum number of runs can be found in Appendix F: Method to determine the appropriate number of runs nRuns for stochastic models, respectively.

Implementation of stochastic models to building variant 1

The cooling energy of building variant 1 is shown to be sensitive to light use, while it is not sensitive to window operation or blind use (see Fig. 5.9). Modeling light use by means of Reinhart’s Lightswitch-2002 model (see Appendix E) causes the distribution in the results to change radically (Fig. 5.10). As expected, adding model complexity to the other considered aspects of OB leads to negligible differences in the results. Combinations of aspects have been considered to investigate the interactions among behavior; while some effect is noticeable, for the case under investigation, modeling the lights’ operation alone causes the greatest variation.

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Fig. 5.10: Effect of implementing stochastic models for light use, blind and window operation on the cooling energy of building variant 1

Implementation of stochastic models to building variant 16

The Mann-Whitney U test identified the cooling energy of building variant 16 as dependent on blind and window operation, while the results were not sensitive to light operation (see Fig. 5.9). Fig. 5.11 shows how applying a stochastic model to window and blind operation (see Appendix E) leads to a great variation of the performance indicator. Adding further model complexity to the light use has a marginal influence on the results. Fig. 5.11 also shows how adding model complexity to different influential aspects of occupant behavior has a diverse impact on the variation of the performance indicator. In this case, the blind operation model has a much stronger impact than the window operation model. This result could be due to the fact that the patterns already gave a similar representation of window operation to the one obtained by means of the stochastic model. Another plausible explanation is that, in spite of both aspects being classified as “influential” in the Mann-Whitney U test, blind operation has a stronger influence on the cooling energy.

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Fig. 5.11: Effect of implementing stochastic models for light use, blind and window operation on the cooling energy of building variant 16

5.5 Discussion and methodological limitations

The results in Sections 5.3.3 – 5.3.4 confirm that different buildings and performance indicators show a dissimilar sensitivity to occupant behavior. The Mann-Whitney U test proved to be a suitable method to determine the aspects of OB that are influential for the results. In the considered case, all performance indicators were sensitive to HVAC use. The sensitivity to all other aspects of OB changed according to building variant and performance indicator. Generally speaking, blind operation appeared to be more relevant in buildings with high SHGC for all performance indicators. Light and equipment use had a greater effect for buildings with a use scenario characterized by higher power density. Building variants with bigger window areas were more sensitive to window operation. Although macro-trends are visible, it would have been impossible to establish a priori which aspects of OB were influential for the results. The current methodology is proposed to test the influence of uncertain OB aspects and distinguish influential and non-influential OB aspects for any case study at hand. An analysis of different climates and building variants is expected to lead to the conclusion that performance indicators will have different sensitivity to various OB aspects.

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In Section 5.4 the effect of applying a higher model complexity on the range of cooling energy use was investigated both for influential and non-influential aspects. Fig. 5.10 and Fig. 5.11 clearly show the ineffectiveness of increasing the model complexity of non-influential OB aspects. This study makes a number of simplifications that ought to be pointed out. First, the results of the sensitivity analysis strictly depend on the definition of diversity patterns, which should represent a plausible spectrum of the uncertainty of the considered aspect of OB. For example, all results were sensitive to HVAC use as there was a fundamental difference between Pattern A, in which the system is switched off when the building is unoccupied, and Pattern B, where the system is always on and a setback temperature is used. Moreover, the modeled triggering conditions for window opening in this case is Open if occupied, Tin > Tsp, cooling and ΔTin-out > 2°C (Table 5.2). This assumption may be valid for office buildings, where window opening behavior is mainly influenced by thermal discomfort (Haldi and Robinson 2009). However, it precludes de facto the sensitivity of energy-related performance indicators to window operation in the heating season. The user of this method should be aware of the realistic spectrum of uncertainty in his/her case to ensure that the modeling assumptions do not inhibit the significance of the results. Second, as the diversity patterns are implemented in the form of schedules or deterministic built- in software functions, it was stated that implementing stochastic models is equivalent to increasing model complexity. While this is certainly true, in reality, model complexity is continuous rather than discrete, as a category (e.g., stochastic models) can be characterized by different complexities according to the model’s size and resolution (Gaetani, Hoes, and Hensen 2016a). Third, while the scientific community agrees that fixed schedules may not be representative of actual behaviors, it has not reached agreement concerning the need for higher model complexity. Hence, while it is reasonable to state that higher complexity models offer a better approximation of reality, it is not yet proven that their predictions indeed lead to more realistic results (e.g., Mahdavi and Tahmasebi 2015b). The reliability of such models in this context is subject to the evaluation and validation process undertaken by the single models. Finally, selecting the fit-for-purpose model is not only about model complexity. In fact, different models have been developed for different building types, climates, performance indicators etc. If there is no evident match between the investigated case and the available models, all suitable models of a given complexity should be implemented. As pointed out in the introduction to this chapter, this study represents the second step towards achieving a FFP-OBm strategy. The case study explored in this chapter was a single-occupant office building during a fictional design stage. However, the methodology can be applied to whole buildings (as was shown in Chapter 4) and to other phases of the building lifecycle. Chapter 7 provides a range of applications in different stages of the building lifecycle to verify that the

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FFP-OBm indeed leads to efficient decision-making and improved predictive ability over traditional modeling techniques.

5.6 Concluding remarks

The Diversity Patterns Method is the second step of the FFP-OBm strategy. A practical approach to test the sensitivity to uncertain OB aspects and to identify the most influential OB aspects was introduced and tested in the conceptual design phase for eight building variants of a cellular office in Amsterdam and Rome using EnergyPlus version 8.3. The same approach can be used for whole buildings and in different phases of the building lifecycle. The Mann-Whitney U test proved to be a suitable statistical method to perform a sensitivity analysis in this context. The results highlighted how different buildings and PIs are influenced by the various OB aspects in a dissimilar way. A deeper analysis of two building variants confirmed the findings of the Mann–Whitney U test. In fact, increasing the model complexity of OB aspects that appear to be non-influential for the results has a marginal influence on the distribution of the performance indicator. It can be concluded that, for the investigated case, adding model complexity to those aspects of OB that the Mann–Whitney U test identified as non-influential might constitute an unnecessary time expenditure, depending on the purpose of the simulation. In cases where higher accuracy of the results is required, it might be necessary to model all aspects of OB that could be interrelated. Indeed, a small effect of such interrelation is visible, but is negligible if compared to the effects obtained by refining the modeling approach of the influential aspects.

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6 The fit-for-purpose approach: overall view

This idea that there is generality in the specific is of far-reaching importance. Douglas R. Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid

This chapter presents the overall fit-for-purpose approach. First (Section 6.1), the possible outcomes of a positive Mann-Whitney U test are discussed, namely epistemic uncertainty reduction and model refinement. The decision-making process at higher model complexity is also addressed. Then (Section 6.2), the overall view of the FFP-OBm is provided. Section 6.3 presents the results of a practical demonstration of the strategy that was achieved by means of a virtual experiment. The overall strategy is discussed in Section 6.4, alongside some concluding remarks.

NB: The content of this chapter is partly inspired by a conference paper presented in San Francisco during BS2017 and published in the conference proceedings under the title: Isabella Gaetani, Pieter-Jan Hoes, Jan L.M. Hensen, 2017, Introducing and testing a strategy for fit-for-purpose occupant behavior modeling in a simulation-aided building design process, Proceedings of the 15th International IBPSA Conference (Building Simulation 2017), San Francisco (Gaetani, Hoes, and Hensen 2017)

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6.1 Dealing with influential OB aspects: possible outcomes

In Chapter 5, the highest level of sensitivity analysis by means of the Mann-Whitney U test was introduced and its significance was tested. While the proposed approach yielded meaningful results, the question of how to proceed when an OB aspect is identified as influential for the building/performance indicator, remains open. This section aims at answering this question, thereby guiding the choice of model complexity for aspects that are considered influential. Broadly speaking, there are two possible outcomes for a positive Mann-Whitney U test: either the simulation user can i) reduce the epistemic uncertainty of the OB aspect(s); or he/she could ii) improve the representation of the OB aspect(s) by refining its modeling formalism. Both approaches have their limits. The first approach inevitably involves time and cost expenditure, and may be unfeasible depending on the stage of the building lifecycle. In contrast, the second approach is subject to the reliability and trustworthiness of the models, and relies on the availability of the information needed to apply, and possibly derive, the model. Section 6.1.1 and Section 6.1.2 discuss these two options. Section 6.1.3 illustrates the decision-making process at a higher complexity level.

Epistemic uncertainty of influential OB aspects

If an aspect of OB appears to be influential, it should be verified whether the epistemic uncertainty connected with the OB aspect can be reduced. This operation allows the simulation user to ensure that the range of the diversity patterns previously assumed is not too extreme and indeed represents the case at hand. Typically, such uncertainty could be reduced by gaining further information and analyzing operational data, where possible. During the design phase, reducing epistemic uncertainty may mean collecting or accessing data from similar buildings to act as a benchmark. Moreover, the epistemic uncertainty could be reduced by limiting the degrees of freedom of people concerning influential OB aspects, such as imposing a maximum possible variation of the temperature setpoint. Finally, specific design choices could also contribute to reducing uncertainty, e.g. if the blind operation is found to be influential, the building designer may choose to apply automated solar shading systems rather than manual ones to reduce the uncertainty connected to this behavior (however, manual overrides should still be considered). Nonetheless, epistemic uncertainty cannot always be reduced. In some cases, one or more aspects of OB may be identified as influential, but there may be no possibility to decrease the uncertainty connected with such behavior(s). In those cases, an alternative is to refine the modeling approach

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with the aim of achieving a more representative/realistic modeling formalism. This is the topic discussed in the following section (Section 6.1.2).

Influential OB aspects’ model complexity

Standard schedules, simple IF-THEN models, and diversity patterns were used until this point to describe occupant presence and their adaptive and non-adaptive behaviors. However, there may be cases for which these modeling assumptions do not help decision-making, and the simulation user will need to look for a different, more refined modeling formalism. Section 6.1.1 illustrated that the first action to take, once one or more OB aspects are found to be influential, is to review – and possibly narrow – the extreme assumptions made when applying the diversity patterns. When this option is also discarded, and again a decision cannot be taken, the simulation user is advised to refine the modeling technique that was adopted to represent influential OB aspects. If available, or achievable with reasonable effort, a model based on the building’s operational data should be used for the relevant OB aspects. In all other cases – and typically during the design phase, when the building is still not built – a different approach should be taken. While some alternatives are present, such as using synthetic population data (e.g. (Andrews et al. 2016)), a possible approach would be to increase the model complexity. Doing so would mean assuming that higher complexity OB modeling indeed yields a better representation of reality by reducing the approximation error (Zeigler, Kim, and Praehofer 2000). While it is arguable whether the currently available higher complexity models do indeed offer a better approximation of reality (e.g., (Tahmasebi and Mahdavi 2016)), one can assume that models will undergo a thorough validation and applicability process (Mahdavi and Tahmasebi 2017) in the near future, thus increasing their trustworthiness. In this research, it is assumed that the higher complexity models do contribute to achieving a better representation of reality if compared with standard schedules or extreme diversity patterns. This assumption amounts to supposing that the uncertainty due to the estimation of unknown parameters in higher complexity models is zero. In the future, it can be expected that – according to the phase in the building lifecycle and to the available data –, it will be possible to filter out models for which the uncertainty due to estimation of unknown parameters outweighs the decrease in approximation error achievable by increasing the model complexity.

Decision-making at higher model complexity

The FFP-OBm strategy is a tool for decision-making. It is hence important that the fit-for-

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purpose OB model complexity resulting from the strategy enables the most effective and efficient decision-making process possible. Most importantly, it should enable the simulation user to make a decision, more specifically the right decision. However, it may not always be possible to make a decision based on the results of the FFP-OBm. This section discusses decision-making at higher model complexity by means of an example. Let us say the aim of the simulation user is to choose between two design alternatives: Design A and Design B. As an example, the performance indicator of interest should be minimized (for example, energy use intensity), i.e. the lower the better. Fig. 6.1 shows a few possible scenarios. It is supposed that the screening method was conducted, one or more OB aspects turned out to be influential, and they were modeled with a higher model complexity (i.e. diversity patterns or higher). Doing so led to a spread of values of the PI, as opposed to a single value typically obtained by schedules and built-in deterministic models. In Fig. 6.1a, the boxplots representing the spread of PI in Design A and Design B are characterized by the same median value. However, the boxplot of Design B is much smaller than the one of Design A, indicating a higher robustness to OB. In this case, if the spread and value of PI in Design B is acceptable, the simulation user would opt for Design B. Instead, in Fig. 6.1b, the PI for Design A is always lower than in Design B, regardless of OB. Thus, the simulation user would opt for Design A without hesitation. It may be, however, that two designs behave as in Fig. 6.1c: while the median value of Design A is indeed lower than the one of Design B, the two boxplots show a strong overlap. Moreover, Design A appears less robust than Design B to OB, and its highest values are well above those of Design B.

(a) (b) (c)

Fig. 6.1: Examples of decision-making solutions under uncertainty

Let us imagine that the simulation user is already modeling the influential OB aspects with the most refined model available. In this case, it is not possible for him/her to make a conclusive decision. The simulation user is advised to evaluate the two design alternatives on a different performance indicator of interest (for example, comfort indicators where energy indicators were

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first considered). If even then it is not possible to make a decision, the simulation user can conclude that decision-making under uncertainty for the investigated alternatives does not lead to definitive outcomes.

6.2 Overall view

The methods presented in Chapter 4 and 5 (namely the Impact Indices Method and the Diversity Patterns Method including the Mann-Whitney U test), form, together with the options discussed in Section 6.1, the core of the fit-for-purpose occupant behavior modeling (FFP-OBm) strategy. In this section, the overall strategy is presented, starting with the problem at hand and ending with the fit-for-purpose occupant behavior model for each OB aspect. Fig. 6.2 is the resulting FFP-OBm strategy. The strategy is composed of a number of steps based on the methods presented in Chapter 4, Chapter 5 and Section 6.1. A detailed description of each step follows hereafter.

― Start: Problem definition The starting point for each study must be the acknowledgement of the objectives and scope of the study itself. Besides building, climate and phase in lifecycle, it is important that the user of the strategy is aware of the purpose of the study (e.g., choice among alternative designs, or energy and comfort performance prediction). In turn, the purpose of the study commands the performance indicators (PIs) and the temporal granularity of the required results. A use scenario (e.g., lighting power density) must also be envisioned, where possible.

― Step 2: Choice of building model complexity Once the purpose of the study is clear, the building model complexity is chosen. In fact, building performance related problems may be solved with a range of methods, spanning from rules-of-thumb, to steady-state calculations, to transient (sub-)hourly dynamic simulation. While it is important to highlight that the building model complexity should derive from the purpose of the study, a deep analysis of such a choice goes beyond the scope of this chapter. During this step, the user of the strategy must become aware of the uncertainties connected to the model, such as those in the thermophysical properties (Rezaee et al. 2015). If no information about the model’s uncertainty is available, an uncertainty analysis should be conducted. This step is necessary to put uncertainties related to OB into perspective. Supposing it is concluded that the problem should be solved by means of BPS, the

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remaining steps of the strategy, introduced below, should be followed.

Building OB Models Database Climate Start Purpose of Simulation cmin = c0 = Diversity Patterns, Use Scenario schedules, IF-THEN models, non- Phase in Lifecycle probabilistic models

c1 = Probabilistic models

c2 = Agent-based models

Choose building Step 2 model complexity

PI = Heating and/or PI = n Cooling Energy Use PI = x

Step 3 Impact Indices Method Screening Method x Screening method n (based on 1x simulation run) Chapter 4 Chapter

yes Can make decision?

Define Diversity Patterns Step 4 cn+1 = for uncertain OB aspects cn+1

n = flowchart iteration = 0 cn = OB model complexity for iteration n = cmin

Run simulations and process c = Update Diversity Patterns for n = n+1 n+1 results cmin uncertain OB aspects Chapter 5 Chapter

yes yes Can make decision? yes

Step 5 no

Can I reduce Identify influential OB aspects Step 6 n = 0 yes uncertainty of influential OB with Mann-Whitney U Test aspects?

no no Step 7

Can I change Step 9 Decision n = Check on possible model model complexity of Step 8 no Decision n-1 complexities c influential OB aspects?

yes OB Models

Chapter 6 Chapter database

Increasing c does not help decision-making

cFF P = cn-1 End

cFF P = cn FFP Model complexity cFFP = cn

Fig. 6.2: Fit-for-purpose occupant behavior modeling (FFP-OBm) strategy overview

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― Step 3: Screening method This step is supposed to be a fast screening method to avoid running an unnecessary number of simulations, for example by formulating scenarios for OB aspects that are non-influential for the results. The Impact Indices Method (see Chapter 4) is used in the strategy to test the influence of a number of OB aspects on building heating and cooling energy demand. However, similar screening methods could be developed for different OB aspects and performance indicators, such as comfort indicators. While further screening methods have not been developed in this thesis, they are material for future research (see Section 8.3.1). In some cases, the information obtained by the screening method could be sufficient for decision-making; the simulation user would then be directed to the end and the lowest OB model complexity would be identified as fit-for-purpose. Instead, if the simulation user is not able to make a decision based on the screening method’s results, he/she is directed towards the next step.

― Step 4: Definition of diversity patterns In this step, the diversity patterns are defined. Applying diversity patterns to a building model equates to testing the possible sensitivity of the results to a particular aspect of OB. This step is highly relevant as the sensitivity of the results to the various aspects of OB will be strictly related to the assumptions made here. The diversity patterns are (high/low) possible variations of uncertain aspects of OB, and are implemented by means of schedules or other built-in deterministic software assumptions. The reason behind formulating such diversity patterns is to perform preliminary testing on the influence of different aspects of OB on a performance indicator. All possible combinations of the diversity patterns are to be investigated (see Section 5.2). The patterns cause results to move from single values to a range of values (in case the selected PI is sensitive to the modeled aspects of OB). The range is strictly dependent on the assumptions made while defining the diversity patterns, which should be realistic and possibly agreed upon with all involved parties. Once the simulations are run and the results are processed, the simulation user can assess whether the given range allows him/her to make a decision, or whether further investigation is needed to check if refining the modeling technique might help decision- making.

― Step 5: Identification of influential OB aspects In case a decision cannot be made based on the initial range of the results due to the patterns, for the flowchart iteration n = 0, a refined sensitivity analysis takes place to identify the decidedly influential aspects of OB. This step is a further refinement of

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the results obtained with the Impact Indices Method, and it allows relevance to be ascribed only to the aspects that truly affect the results for a specific case. As presented in Chapter 5, this sensitivity analysis is achieved by means of the statistical Mann- Whitney U test (Gaetani, Hoes, and Hensen 2016b). This nonparametric test is used to assess whether two independent groups are significantly different from each other. Here, the two groups are characterized by all scenarios with the same pattern for an uncertain aspect of OB. For example, all scenarios formulated in Step 4 characterized by “low” occupant presence are compared to those characterized by “high” occupant presence. The aim of the sensitivity analysis is to ultimately assess which aspects of OB are responsible for the spread in the results so that more attention can be drawn to modeling such aspects.

― Step 6: Epistemic uncertainty reduction Once the influential aspects are identified, the user must check whether their uncertainty can be reduced, i.e. whether more realistic patterns can be formulated (see Section 6.1.1 for more details). If the epistemic uncertainty can be reduced, the diversity patterns are updated with the new information and simulations are run again.

― Step 7: Check on possible model complexities c If the uncertainty of the influential aspects cannot be reduced, the next step is to check whether the modeling formalism can be refined (see Section 6.1.2). This check is reliant on an OB modeling database that should be integrated in the software, and where possible modeling complexities should be filtered out according to the case under investigation (e.g., in the conceptual design phase it should not be possible to implement an agent-based model, as the uncertainty due to estimation of input parameters would be too high). The models within the database are also filtered out according to the uncertainty analysis that was conducted in Step 2. For example, if the purpose of the simulation is a design decision among alternatives, and the decided parameter uncertainty is higher than the scenario uncertainty due to OB, there is no sense in increasing the model complexity.

― Step 8: Increasing complexity If a different model complexity can be used, the simulations are run again with the

higher complexity models for the influential OB aspects (cn+1). If not, the user is led to

the end. In this case, the fit-for-purpose model complexity (cFFP) is the previous one, i.e. the diversity patterns. OB modeling does not help the user to make a conclusive decision. It is important to note that the levels of complexity reported in Fig. 6.2 are

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a simplification; in reality, a category (e.g., stochastic models) can be characterized by different complexities according to the model’s size and resolution.

― Step 9: Decision-making As for the second and further iterations, if the user is still unable to make a decision, the strategy will evaluate if the current run led to the same decision if compared to the previous. If not, it might be necessary to increase complexity for the influential OB aspects again, otherwise the user is led to the end via the box “increasing complexity does not help decision-making”.

― END: Fit-for-purpose occupant behavior model complexity The user is led to the end in the following cases: i) a decision could be taken; ii) increasing model complexity does not help decision-making; and iii) influential OB aspects modeling formalism/complexity cannot be changed. In the second and third cases, the user may have to evaluate other performance indicators, change design to achieve the desired results, or limit the freedom of occupants concerning influential OB aspects. In fact, in cases ii and iii, the problem does not concern OB model complexity, or no alternative OB modeling formalism is available.

Section 6.3 provides a demonstration of the steps mentioned above by means of a simple, virtual case study for illustration purposes.

6.3 Virtual experiment to demonstrate the fit-for-purpose approach

Problem definition

6.3.1.1 Building

Four virtual office buildings are used as a case study. The four buildings are cubicles with dimensions of 5 x 5 x 3 m3, they are characterized by different window-to-wall ratios (WWR) and different wall constructions for the southern façade facing the outdoor environment (see Fig. 6.3 and Table 6.1). Floor, ceiling, and all facades but the southern one are assumed to be adiabatic, as if the cubicle was surrounded by other spaces in thermal equilibrium with it.

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Case 1 Case 2 Case 3 Case 4

Fig. 6.3: Impression of the investigated building variants

Table 6.1: Case study buildings

Case 1 Case 2 Case 3 Case 4 WWR = 40% x x WWR = 80% x x LOW TI x x HIGH TI x x

The wall construction named low thermal insulation (LOW TI) has wall R-value 1.3 m2K/W, window U-value 3 W/m2K, g-value 0.73 and visible transmittance 0.75. The wall construction named high thermal insulation (HIGH TI) has wall R-value 4 m2K/W, window U-value 1.1 W/m2K, g-value 0.29, and visible transmittance 0.48. The heating, ventilation and air- conditioning (HVAC) system is supposed to be ideal, but was capped in size following preliminary simulation runs.

6.3.1.2 Climate

The virtual office buildings are located in Amsterdam, the Netherlands.

6.3.1.3 Use scenario

The use scenario for lights and equipment is assumed to be the ASHRAE default 10.76 W/m2. In the base-case scenario, the daily schedules for occupant presence, light use and equipment use are assumed to be standard ASHRAE (ASHRAE 2013). Heating and cooling are provided by means of an ideal system, which keeps the indoor temperature within the setpoint limits (Tsp, heating = 21°C and Tsp, cooling = 24°C) between 7 am and 10 pm during weekdays, and between 7 am and 6 pm on Saturdays. During the remaining hours, temperature setbacks are set as 15.6°C and 26.7°C for the heating and cooling season, respectively.

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6.3.1.4 Purpose of the simulation and performance indicators

The purpose of the simulation is to identify the best performing shading strategy for the four offices among: i) no shading, ii) fixed overhang (0.5 m depth), and iii) manual shading device. In this example, the yearly energy for cooling and for heating is the performance indicator to be optimized.

6.3.1.5 Phase in the building lifecycle

This design question may typically concern a rather advanced stage of the design process. No real data or information is available as this is a virtual experiment.

Results and discussion

― Step 2: Choice of building model complexity It was decided to answer the problem by means of dynamic simulation as the design question is typical of a rather advanced stage of the building design. The four building variants were modeled using the software EnergyPlus version 8.7. To become familiar with the uncertainties connected to the model, a preliminary uncertainty analysis on the decided parameters was conducted considering the uncertainty due to thermophysical properties’ variations as given in (Hopfe 2009) and reported in Appendix G: Thermophysical properties variations considered for the uncertainty analysis in Chapter 6. The distribution assigned is a normal distribution. The Latin hypercube sampling (LHS) method was used to generate a near-random sample of 200 building variants derived from a multidimensional distribution of thermophysical parameter values. First, the three shading options were investigated for the four building variants in a traditional fashion. As for blind operation, the shading devices were modeled with the function OffNightAndOnDayIfCoolingAndHighSolarOnWindow integrated in EnergyPlus. This choice was dictated by the findings of (O’Brien et al. 2016), where the majority of survey respondents stated that thermal and radiation considerations motivated their modeling strategy for blind operation. It is important to note that modeling the blind operation in such a manner does not actually represent manually operated blinds, but it is indeed a typical manner to do so in practice. Fig. 6.4 shows the impact of the two shading strategies on the four investigated buildings. It can be

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noted that, as expected, the shading strategies were much more effective in limiting the cooling energy in the two variants characterized by poor thermal insulation and high U-value, g-value and visible transmittance (Case 1 and Case 2). In Cases 1 and 2, the designer would be inclined to choose to apply blinds, as they seem to have been be the most efficient strategy. Instead, a decision cannot be made as easily for Cases 3 and 4, where all solutions performed very similarly.

Fig. 6.4: Energy demand for cooling and heating of the four investigated offices with changing shading strategy; state-of-the-art OB modeling (lowest complexity)

As a next step, the influence of the uncertainty of the decided parameter was investigated for all variants (Fig. 6.5).

Fig. 6.5: Influence of the uncertainty of thermophysical properties on the energy demand for cooling and heating

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Generally speaking, it was evident that the uncertainty of the thermophysical properties was especially pronounced in poorly performing buildings (Case 1 and Case 2). For both Case 1 and Case 2 the preferred shading strategy remained manual blinds, while for Case 3 and Case 4 all solutions again showed a similar performance.

― Step 3: Screening method The Impact Indices screening Method presented in Chapter 4 was applied to the current case study to quickly evaluate whether some aspects of OB are non-influential for the results. The hypothesis is that if some OB aspects are identified as non-influential, they can be modeled with the simplest model complexity. This operation was performed solely with respect to cooling energy, which covers the largest share of the demand, as illustrated in Fig. 6.4. Fig. 6.6 shows the impact indices for occupant presence, light use, equipment use and blind operation (cooling energy). The results for blind operation are represented with a dotted line in the Base and Overhang cases as these solutions do not actually include shading systems.

B B Case 1 Case 2 Case 3 Case 4 Case1Case2Case3Case4 B

0.00 0.75 0.00 0.75 0.00 0.75 0.00 0.75 0.00 0.75 1.50 II II II II II

Fig. 6.6: Impact indices for occupant presence, light use, equipment use and blind operation of the four investigated offices with changing shading strategy

Fig. 6.7 shows the impact indices for temperature setpoint setting on cooling energy. The impact indices for temperature setpoint setting are also shown separately because of the different nature of the indices themselves (see Chapter 4).

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Fig. 6.7: Impact indices for temperature setpoint setting (II, Tsp) of the four investigated offices with changing shading strategy

To put the results concerning the II, Tsp into context, Fig. 6.8 shows an OAT sensitivity study that correlates the impact indices with the effects of increasing or decreasing the cooling setpoint by 2 degrees Celsius.

100% y = 0.6345x + 0.25 80% R² = 0.8498

60%

40%

20% baseline when ± 2°C Average Change from 0% 0 0.2 0.4 0.6 R2 Value (II Tsp)

Fig. 6.8: II reliability test: comparison between II Tsp values and OAT sensitivity analysis results for the impact of increasing/decreasing the cooling setpoint by 2 degrees Celsius

Generally speaking, Fig. 6.6 highlights how the three investigated shading options in Case 3 and Case 4 (the building variants with high thermal insulation) were similarly influenced by OB, apart from the influence of blind operation, which of course was only present in the solutions with blinds. Alternatively, Case 1 and Case 2 (the building variants with low thermal insulation) present a marked difference between the potential influence of OB on Base and Overhang case, and the potential influence of OB on the

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solution with blinds. Both in Case 1 and Case 2, the solution with manual blinds seems less robust to OB. In line with expectations, all variants with higher window U-value and g-value (Case 1 and Case 2) showed a higher potential influence of blind operation compared with variants with lower window U-value and g-value (Case 3 and Case 4). Fig. 6.8 shows a strong correlation between temperature setpoint impact indices and OAT sensitivity analysis, which confirms the significance of the II for temperature setpoint setting. As far as influential and non-influential aspects, the only seemingly non-influential aspect was occupant presence, which may nevertheless be taken into consideration as a trigger for OB. Light use and equipment use are generally identified as influential. All OB aspects for the four analyzed cases are therefore taken to the next step (Step 4).

― Step 4: Definition of diversity patterns No information is available about possible OB characteristics, therefore the diversity patterns to test the potential influence of OB are standard and defined as in Table 6.2. It is important to note that the diversity patterns defined in Table 6.2 should be agreed upon with all interested parties. Ideally, the agreed diversity patterns should represent (high/low) possible variations of uncertain aspects of OB. In this illustration, for example, there is a decisive difference among the patterns developed for equipment use (where the original schedule is simply modulated) and the patterns developed for light use (where the introduction of daylight control resembles a design choice). The patterns proposed for light use are therefore more extreme than those proposed for equipment use. Moreover, Pattern A for blind operation assumes that the blinds are not operated at all and are always left in open position. This assumption is in line with findings from (O’Brien et al. 2016) but may nevertheless be considered extreme. Moreover, the assumption of keeping the blinds open at all times corresponds as a matter of fact with the shading strategy “no shading”. It is thus expected that the distribution corresponding to the shading strategy “manual shading device” will enclose the distribution corresponding to “no shading”. These choices are very important as they influence the final outcome of the analysis. All pattern combinations resulted in a total 6 of 2 = 64 – 16 (Tsp,heating > Tsp,cooling) = 48 combinations when blinds are present, and 24 otherwise.

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Table 6.2: OB diversity patterns (adapted from (Lin and Hong 2013))

Pattern A Pattern B Occupant Presence Mon-Fri 10-12 am Mon-Fri 8-12 am and and 1-4 pm 1-6 pm Equipment Use 90% when occupied; 100% when present; 30% when non- 60% before arrival occupied and after departure Light Use ON when occupied; ON when occupied + daylight control lunchbreak; no daylighting control Tsp, heating 18°C 23°C Tsp, cooling 22°C 26°C Blind Operation Always open Close if high solar on window

Simulations were run and the results are shown in Fig. 6.9. Fig. 6.9 shows how considering the potential influence of OB generally led to greater uncertainty than decided parameter uncertainty. In particular, it is now not evident which shading strategy should be chosen for all cases. For example, in Case 1 the solutions with overhang and manual blinds have the same median value for cooling plus heating energy (ca. 60 kWh/m2y), but the solution with overhang presents a much smaller uncertainty and it is hence more robust to OB. It would therefore seem reasonable to choose the solution with overhang. For Case 3 and Case 4 an investigation of the potential influence of OB did not lead to greater insights into the performance of the buildings. Instead, there seems to be potential for the application of blinds in Case 2. As a decision could not be made using the results, Case 2 was selected to test the remaining steps of the FFP-OBm strategy.

Fig. 6.9: Influence of OB on energy demand for cooling.

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― Step 5: Identification of influential OB aspects In the first flowchart iteration, the sensitivity analysis by means of Mann-Whitney U test is used to identify the influential OB aspects. The influence of various aspects of OB is verified only for cooling energy as it covers the vast majority of the demand. The results of the sensitivity analysis for the three shading strategies are reported in Fig. 6.10. An aspect of OB is considered influential if the p-value < 0.05. The Mann-Whitney U test shows that all shading solutions are influenced by lighting switch on/off behavior and by the cooling temperature setpoint. The solution with manual blinds is significantly dependent on the operation of the blinds. It is worth noting that equipment use, which scored higher than light use in the initial impact indices screening, does not turn out as influential in this analysis. This is because of the assumptions made for the patterns, as highlighted at Step 4.

1 1 1

0.8 0.8 0.8

0.6 0.6 0.6

0.4 0.4 0.4 1-p-value 1-p-value 1-p-value

0.2 0.2 0.2

0 0 0

Fig. 6.10: Mann-Whitney U test results: 1 - p-value. Darker bars represent influential behaviors

― Step 6: Epistemic uncertainty reduction As the virtual buildings are supposed to be in the design stage, it can be assumed that it is not possible to reduce the epistemic uncertainty of the influential OB aspects in any way. Therefore, the simulation user would proceed to the next step of the strategy; namely, to assess available modeling complexities.

― Step 7: Assessment of available model complexities c This step implies checking whether a better representation of the influential aspects of OB exists. In other words, whether a model derived by operational data or higher complexity models are available. It is important to reiterate that the assumption that higher complexity models lead to more realistic results is to date not verified. However, it can be presumed that the database of OB models coupled with the strategy introduced here will, in the near future, only include validated models, whose validity range is well defined and specified.

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― Step 8: Increasing complexity For the purpose of this study, the discrete-time Markov model proposed by Haldi and Robinson (Haldi and Robinson 2010) was used to mimic the manual operation of the blinds. The discrete-event Markov model Lightswitch-2002 (Reinhart 2004) was used to predict the light switch on/off behavior. The models are fitted as a logistic function, so that the probability P that the blinds or lights at time step t are in state S is equal to: 1 , (6.1) , ∑ 1

where are the parameters of the model and x are the predictors. The models were implemented in EnergyPlus version 8.3 using the Energy Management System (EMS) as in (Gunay, O’Brien, and Beausoleil-Morrison 2016).

― Step 9: Decision-making Fig. 6.11 shows the results of applying a higher complexity model for light use in Case 2, NoShading and Overhang, and a higher complexity model for lighting and blind use in Case 2, Blinds. Assuming that the results of the higher complexity models are more representative than the OB patterns (e.g., it is never the case that people simply do not operate the blinds and leave them always open), the decision-maker can finally choose to apply blinds as the most efficient shading strategy for Case 2 (see Fig. 6.11).

Fig. 6.11: Effects of increasing model complexity for light and blind operation

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― End: Fit-for-purpose occupant behavior model complexity The strategy enabled the identification of the fit-for-purpose model complexity for the 4 investigated cases. In particular, in two cases (Case 3 and Case 4) the FFP-OBm strategy allowed us to quickly determine that increasing model complexity of OB would be useless. For these two cases (Case 3 and Case 4), the FFP-OBm complexity was the lowest, and therefore used simple schedules and built-in controls. In Case 1, applying an overhang is deemed to be a more robust and equally efficient shading strategy compared to manual blinds. For Case 1, the FFP-OBm complexity was the diversity patterns for all OB aspects except for occupant presence, for which simple schedules sufficed. In Case 2 the potential of manual blinds was identified and confirmed by a more complex OB modeling formalism. The FFP-OBm complexity for light use and blind operation (and, ideally, cooling temperature setpoint setting) use was identified as stochastic models.

6.4 Discussion and concluding remarks

Chapter 6 aimed at i) presenting the remaining aspects of the FFP-OBm strategy that were not illustrated in previous chapters (Section 6.1); ii) providing an overall view of the FFP-OBm strategy in terms of operational flowchart (Section 6.2); and iii) demonstrating each step of the FFP-OBm strategy by means of a simple, virtual case study (Section 6.3). In particular, Section 6.1 illustrated the two options following the identification of influential OB aspects by means of the highest-level sensitivity analysis (Mann-Whitney U test): namely reducing the epistemic uncertainty of the influential OB aspect(s), and changing the model complexity. Moreover, the criteria that enable decision-making at a higher complexity level are presented. The overall flowchart presented in Section 6.2 is divided into 6 categories: start, Impact Indices Method, Diversity Patterns Method, Mann-Whitney U test application, dealing with influential OB aspects, and end. The essential prerequisites to start a well-posed problem were identified, as well as the expected end results. The numerous decisions within the flowchart can easily be automated, should this method become mainstream. The method is partly based on scenarios (typically, in the Diversity Patterns Method part), therefore the robustness of the method to the assumptions should be assessed (see for example Section 7.1.4). Section 6.3 successfully illustrated how the presented method is far from being theoretical, but it can instead be readily applied in simulation-aided design processes. Four variants of a single- occupant office building were studied to apply the method on a range of different buildings.

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Indeed, decision-making concerning the best performing shading strategy was possible for all four variants. Case 1 (smaller windows, leakier envelope) was best performing with an overhang, which showed similar median value of cooling energy demand to manual blinds, but was more robust to OB. Case 2 turned out to be best performing if manual shades were applied, while this choice did not really affect the cooling energy demand of Case 3 and Case 4 (higher thermal insulation of envelope and glazing). The method identified the FFP-OBm complexity for various OB aspects in all cases. In particular, all cases but Case 3 and Case 4 turned out to have a different FFP-OBm complexity. This result underlines the importance of the method and challenges the application tout court of higher complexity models. It is important to note that only an energy-related PI was considered in this study as an example. In reality, a thorough assessment of different shading alternatives must necessarily include also comfort indicators. The proposed FFP-OBm strategy is developed as an attempt to support the simulation user who is interested in appropriately representing OB in BPS. The strategy has high potential to be fully automated and become embedded in BPS tools, possibly not requiring any effort or additional expertise from the simulation user. The proposed strategy is a step towards achieving fit-for-purpose modeling of OB in BPS. The validity of the strategy is subjected to the validity of the models which are included in the database.

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7 Applications

I do not have data. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.

Sir Arthur Conan Doyle, Sherlock Holmes

This chapter focuses on applications of the fit-for-purpose approach to actual case studies, with the aim of testing the usability of the proposed strategy. Moreover, building energy data of existing buildings allow for an evaluation of the physical correctness of the FFP-OBm strategy outcomes. The case studies are explored for different phases of the building lifecycle. First (Section 7.1), the benefits of considering OB modeling during the R&D process of an innovative shading system are assessed. Then (Section 7.2), a double-blind study on a university building is presented. Thirdly (Section 7.3), the II Method is applied to energy performance contracting (EPC). Section 7.4 concludes the chapter with some reflections on the presented results.

NB: The content of this chapter is partly inspired by a conference paper submitted to BS2019 under the title Isabella Gaetani, Remco van Woensel, Pieter-Jan Hoes, Jan L.M. Hensen, 2019, Occupant behavior modelling to support the development of adaptive facade control strategies (Section 7.1) and by an original research journal paper in preparation under the title Isabella Gaetani, Farhang Tahmasebi, Pieter-Jan Hoes, Ardeshir Mahdavi, Jan L.M. Hensen, 2019, A real-life application of the fit-for-purpose occupant behavior modeling framework (Section 7.2)

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7.1 Product development and performance assessment (R&D phase)

The first application case study presented in this chapter concerns the R&D of an innovative, adaptive solar shading element. Adaptive façade elements traditionally adapt to their surrounding environment. However, to fully exploit the potential of adaptive architecture, it is advisable that these adaptive elements also react to occupant presence (Cole and Brown 2009). In this way, it would be possible to optimize the building operation towards comfort when people are present and towards energy use minimization when people are absent. BPS tools have strong potential to be used to support the development of innovative, adaptive solar shading systems (Loonen et al. 2017). The advantages of already using such tools in the R&D phase are: i) virtual rapid prototyping to evaluate different future-oriented systems/materials and identifying promising alternatives for further refinement and product development; ii) exploration of high-potential control strategies that maximize the performance of adaptive building envelopes during operation; iii) virtual testing of the robustness of adaptive façade systems to changing boundary conditions (Loonen et al. 2017). Meanwhile, the assumptions made in the BPS model are determinants of the outcome of the simulation. For this reason, it is essential that the assumptions are as conscious and informed as possible. For example, if a simulation user is interested in supporting the R&D of adaptive façade elements that also react to occupant presence, an appropriate representation of occupant presence is critical. However, no currently available method supports the choice of OB models during the R&D of adaptive façade elements. This case study applies the FFP-OBm strategy to adaptive façade elements currently under development by industrial partners. In the following sections, the fit-for-purpose occupant presence model complexity is investigated for the application to R&D of adaptive façade elements.

Problem definition

Purpose The aim of this case study was to evaluate six shading system control strategies in order to: i) assess which control strategy delivers the best building performance; ii) evaluate the performance of the best shading strategy. A variety of performance indicators (PIs) are considered, related to both the energy and the comfort performance of the shading system. The selected PIs are:

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 Annual primary energy demand; considered as a sum of heating, cooling and lighting energy demand [kWh/m2], taking into account the conversion factors reported in Table 7.1.

Table 7.1: Conversion factors site energy to primary energy

Conversion Ref. factor COP 3.00 (van der Aa 2012; Beck et al. 2010)

ηheating 0.95 (Beck et al. 2010)

ηcooling 0.70 (Beck et al. 2010; van der Aa 2012)

ηelectrical 0.39 (Beck et al. 2010; van der Aa 2012)

 Daylight Autonomy (DA); calculated in two reference points (see Fig. 7.1) and expressed in occupied time fraction when the indoor illuminance is > 500 lux (Walkenhorst et al. 2002);

21 1

Fig. 7.1: Sensor points for lighting model

 Daylight Glare Index (DGI); calculated according to (Hopkinson 1972) and expressed in occupied time fraction when DGI > 31, corresponding to intolerable glare. The DGI is calculated for three viewpoints, namely “Facing Window”, “Facing Corner” and “Facing Wall” (see Fig. 7.2).

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Fig. 7.2: Sensor point and viewpoints for the calculation of DGI

While the author is aware of the debate concerning the appropriateness of the visual comfort PIs (Reinhart, Mardaljevic, and Rogers 2006; Wienold 2009; Wienold and Christoffersen 2006, Wienold et al. 2019), the reported PIs are considered to be established metrics.

Shading strategies characteristics All strategies were applied to an automated solar shading system developed by industrial partners. The system is an internal roller blind with a metalized fabric, whose thermal properties are reported in Table 7.2.

Table 7.2: Thermal properties of the roller blind

Thickness Thermal Sol. Sol. Vis. Trans. Vis. Refl. [m] conductivity Trans. Refl. [W/mK] 0.0004 0.2 0.078 0.648 0.077 0.632

The six evaluated control strategies correspond to state-of-the-art controls or controls that are under development by the company. The strategies are described below.

 Strategy 1 two positions available (fully up or fully down); irradiance sensor on the roof; lowering threshold 300 W/m2;  Strategy 2 two positions available (fully up or fully down); irradiance sensor on the façade; lowering threshold 200 W/m2;  Strategy 3 two positions available (fully up or lowered to eye height); irradiance sensor on the façade; lowering threshold 200 W/m2;  Strategy 4 twenty positions available (full height discretized in 20 identical parts); sun-tracking feature to avoid direct sunlight;

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 Strategy 5 same as Strategy 4 but includes an energy mode (see Table 7.3) when occupants are absent;

Table 7.3: Shades control in energy mode

Heat gain through façade Heat loss through façade Heating demand Shades Up Shades Down Cooling demand Shades Down Shades Up

 Strategy 6 same as Strategy 5 but includes overcast sky feature, whereby if the room is occupied and the vertical solar irradiance on the façade is below 120 W/m2, the shades are fully raised.

A graphical representation of the six shading strategies is given in Fig. 7.3. Two additional strategies, namely “fully up” and “fully down” were added to the analysis for the sake of benchmarking.

Fig. 7.3: Graphical representation of the six considered shading strategies

Building characteristics A two-occupant cubicle office building with dimensions of 4.5 x 6 x 3 m3 (see Fig. 7.4) was considered to evaluate the performance of the shading strategies within a building application.

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The building is a modified version of the reference office proposed by Task 56 of the Solar Heating and Cooling Program of the International Energy Agency (Antoni et al. 2018). The cubicle was assumed to be surrounded by similar offices in thermal equilibrium with it, hence all surfaces but the wall facing outside were assumed to be adiabatic. A large non-operable window was located on the external wall. The thermo-physical properties of the building follow the references of Task 56 (Antoni et al. 2018), and are reported in Appendix H: Thermophysical properties of the Task 56 reference building.

2.00m

3.00m

4.20m

6.00m

4.50m

Fig. 7.4: The cubicle office

Climate The office is located in Amsterdam, The Netherlands.

Use scenario The light power density was set to 10.9 W/m2. The heat emitted by lights was assumed to be 13% radiant. The lights were dimmed according to illuminance levels and occupant presence (all lights are off if nobody is present). The power density of appliances was set to 7 W/m2 with 30% radiant heat. The reference schedule for occupant presence is given in Fig. 7.5, while the reference schedule for equipment use followed the ASHRAE standard (ASHRAE 2013).

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Fig. 7.5: Reference schedule for presence

An ideal load air system was used to keep the indoor temperature between 21°C (heating setpoint) and 25°C (cooling setpoint). During weekends only, the setpoints were changed to setback temperatures of 18°C and 28°C, respectively.

Choice of building model complexity and preliminary results

The building was modeled in EnergyPlus version 8.7. The solar shading control strategies were implemented by means of the Energy Management System (EMS) feature of EnergyPlus (please refer to Appendix I: EMS code for modeling the innovative solar shading controls investigated in Section 7.1 to access the EMS code). While the author is aware that other available software may be more specifically tailored for daylight analysis (e.g., Radiance (Larson and Shakespeare 1998)), this choice was made in light of the recent improvements to EnergyPlus in terms on illuminance and glare calculations (EnergyPlus 2019), and to simplify the case study. Because of the complexity of the strategies and the necessity to use advanced features of EnergyPlus, the results of a preliminary evaluation run are reported below. An analysis of such preliminary results serves to check that all strategies are behaving as expected and to allow troubleshooting when necessary.

Annual primary energy demand Fig. 7.6 shows the strategies’ energy performance when standard assumptions about occupant presence are made.

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120 y] 2 100

20 Primary Energy [kWh/m 0 Always S1 S2 S3 S4 S5 S6 Always Closed Open Shading strategy

Heating Cooling Lighting

Fig. 7.6: Energy performance of the six strategies with standard occupant presence schedules

The heating energy demand was hardly affected by the strategy, with a discrepancy of 2.6 kWh/m2y between the highest (S2) and lowest (S1) demand among the strategies. This result is in line with the fact that heating energy demand was the lowest contributor to primary energy demand. More pronounced differences could be seen in the cooling demand, where the maximum discrepancy among strategies rose to 10.1 kWh/m2y (S6 had the lowest demand, while S4 had the highest). The biggest difference among strategies was to be found in the lighting energy demand: 11.5 kWh/m2y (demand of S4,5 – S3). It is worth noting the effect of implementing “Energy Mode”, which reduced the cooling energy demand from 53.3 to 43.8 kWh/m2y in S4 and S5, respectively. The “Overcast Sky” feature resulted in a reduction of lighting energy demand of 10.7 kWh/m2y from S5 to S6. Overall, it seems that the strategies worked as intended. The best-performing strategy in terms of energy was S6, while the worst-performing strategy was S4. S4 demanded circa 27% more primary energy than S6.

Daylight autonomy Fig. 7.7 shows the strategies’ daylight autonomy performance when standard assumptions about occupant presence were made. S4 and S5 delivered the same performance, as expected, because the difference among the two strategies concerned only the unoccupied hours. The benefit of the “Overcast Sky” feature is evident when comparing the performance of S6 with S4 and S5. Out of all strategies, S3 guaranteed the best DA (87% of occupied hours > 500 lux), followed by S6 (81% of occupied hours > 500 lux), while S4 and S5 only guaranteed an illuminance of 500 lux in 63% of the occupied hours.

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0.8

0.6

0.4 lux[%]

0.2

Occupied time fraction with DA > 500 Always S1 S2 S3 S4 S5 S6 Always Closed Open Shading strategy

Fig. 7.7: Daylight autonomy performance of the six strategies with standard occupant presence schedules

Daylight Glare Index The glare performance of the various strategies with standard assumptions about occupant presence are shown in Fig. 7.8 for three different viewpoints. As expected, the viewpoint “Facing Window” was characterized by the highest glare, followed by “Facing Corner” and, lastly, “Facing Wall”. Whether the amount of glare indicated by the software is acceptable or not will therefore depend on the envisioned layout of the office. The benchmark option “Always Open” delivered the worst performance, leading to about 40% of occupied hours with intolerable glare when facing the window. This value decreased to ca. 13% when the occupant faces the wall instead. According to this PI, the options “Always Closed”, S4 and S5 were completely glare-free. This result was expected as the mentioned strategies were purposively developed to avoid glare. In contrast, S6 was characterized by a low amount of intolerable glare, indicating that the sky illuminance may also be enough to cause glare when the sky is overcast. Overall, S1 led to the worst performance with about 25% hours of intolerable glare while facing the window, while S4 and S5 were the best-performing. In the following sections, it is investigated whether these results were presence-sensitive, and if higher model complexity is needed to reach reliable conclusions.

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0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Always S1 S2 S3 S4 S5 S6 Always Closed Open

Occupied time fraction with DGI > 31 [%] Shading strategy

Facing Wall Facing Corner Facing Window

Fig. 7.8: DGI performance of the six strategies with standard occupant presence schedules

Impact Indices Method

Due to the nature of the selected PIs (annual primary energy demand, DA and DGI), the Impact Indices Method was not deemed suitable to achieve the objectives stated in Section 7.1.1, as it currently covers only heating and cooling energy use. For this reason, this step of the strategy was bypassed and the user is directly referred to the Diversity Patterns Method.

Diversity Patterns Method

As mentioned in the introduction to Section 7.1, the fit-for-purpose complexity of occupant presence modeling is at the heart of the investigation. Therefore, diversity patterns were defined only for occupant presence. The operation of the shading devices was automated by means of the strategies, hence it does not require the formulation of diversity patterns. Likewise, the lighting system was assumed to be automatic, and the HVAC system is centrally controlled. The use of equipment was, together with presence, the only type of behavior that was left to the occupants’ discretion in this case study. However, this case study focused on presence modeling to narrow the scope. The developed diversity patterns are shown in Fig. 7.9. In particular, they correspond to a rather rigid work environment where people have a maximum deviation of ± 2 h from the reference

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schedule. In order to evaluate the robustness of the diversity patterns, two possible combinations, named Option 1 and Option 2, were developed. In Option 1, the occupants arrived one hour sooner or later than the reference and left one hour sooner or later. Alternatively, in Option 2, the highly present workers worked through lunchbreak and stayed one hour longer in the evening, while the workers spending least time in the office took a three-hour lunchbreak. These two options were developed to assess the impact of considering hours characterized by different illuminance levels.

Fig. 7.9: Two options of diversity patterns for presence: Option 1 (left) and Option 2 (right)

Once the diversity patterns are developed, the simulations are run and the results are processed.

Annual primary energy demand Fig. 7.10 shows the effect of applying the diversity patterns on the annual primary energy demand. The results show how the robustness to occupant presence varied significantly across strategies. In particular, the benchmarking option “Always Closed” showed the highest sensitivity to presence, while the option “Always Open” had the most robust performance. This effect is largely imputable to the energy demand for lighting, which in the “Always Closed” case depended solely on presence, while it was significantly influenced by available daylight in the “Always Open” case. The increased effect of presence on S5 and S6 (which included the “Energy Mode” feature when occupants are absent) was somewhat visible. Fig. 7.10 also shows the importance of the defined diversity patterns. A change of occupant presence during the first and last hours had a stronger effect than during the mid-hours of the day. Generally, the effect of presence on the strategies’ energy performance was visible in all cases. Whether such an effect is acceptable or a higher model complexity is required will be up to the decision-maker (see Section 7.1.5).

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Fig. 7.10: Effect of Diversity patterns on the annual primary energy demand. Option 1 (left) and Option 2 (right)

Daylight autonomy Fig. 7.11 shows the effect of applying the diversity patterns on the daylight autonomy. The results showed a similar robustness to occupant presence across strategies. In this case a change of occupant presence during the first and last hours also had a stronger effect than during the mid-hours of the day. In fact, the PI was very robust to the mid-hours diversity patterns. This effect can be explained given the southern orientation of the large window: the differences in natural daylight, especially when a threshold of 500 lux is considered, are expected to be stronger during the first and last hours of the day, rather than during the central hours.

Fig. 7.11: Effect of Diversity patterns on the daylight autonomy. Option 1 (left) and Option 2 (right)

It is also interesting to consider the distribution of results in relation to the standard model results. When Option 1 is implemented, the median value of the distribution is in line with the results obtained with the standard model. Conversely, when Option 2 is implemented, the median value of the distribution falls below the standard model in all cases. This result can be explained as, in Option 2, the occupants are present during hours with reduced DA in both

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patterns. In Pattern A the occupants stay one hour longer in the evening, while in Pattern B they take a three-hour lunchbreak, which coincides with the hours of highest DA. The effect of occupant presence on decision-making is considered in Section 7.1.5.

Daylight Glare Index Fig. 7.12 shows the effect of applying the diversity patterns on the glare performance of the six strategies. The results showed a diverse robustness to occupant presence across strategies. In particular, the most sensitive options were the benchmark “Always Open” and S1, S2, S3. The option “Always Closed” and the solutions with sun-tracking (S4, S5 and S6) guaranteed the lowest number of occupied hours characterized by intolerable glare and were robust to occupant presence. The behavior of the median value in respect to the standard model is similar as observed for the DA indicator.

Fig. 7.12: Effect of diversity patterns on the glare performance. Option 1 (above) and Option 2 (below)

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Decision-making

Assessing which control strategy delivers the best building performance Despite the different sensitivity across strategies to occupant presence, especially pronounced for annual primary energy demand and glare performance, it was possible to make a decision concerning the best-performing strategy in all cases. In particular, the ranking of strategies from best to worst performance (see Fig. 7.13) was unaffected by the application of diversity patterns. For this reason, it is advised not to increase the level of complexity for occupant presence modeling if the aim is to assess which control strategy delivers the best building performance. Out of all strategies, the only one that remained in the top 4 (considered as the “good performance zone”) for all PIs is S6. However, it will be up to the company to prioritize PIs and/or ease and cost of development. These priorities will inform the choice of which strategy to favor.

Best performance Worst performance

Primary Energy Demand S6 S3 AO S5 S1 S2 S4 AC

Daylight Autonomy AO S3 S6 S1 S2 S4 S5 AC

Glare AC S4 S5 S6 S2 S3 S1 AO

Fig. 7.13: Ranking of strategies performance from best to worst. “Always Open” and “Always Closed” are indicated by AO and AC, respectively

Evaluating the performance of the best control strategy S6 was identified as the best performing control strategy, and it thus selected to evaluate its performance. The need to refine the model complexity of occupant presence when evaluating the performance depends on the desired accuracy of the results. For example, one may conclude that the spread in annual primary energy demand obtained by means of the diversity patterns is too high. The occupant presence Pattern A (Option 1) led to a primary energy demand of 79.2 kWh/m2y, while Pattern B indicated that the building would need only 66.4 kWh/m2y to operate during the same period. Assuming that Pattern A and Pattern B were extreme conditions, one could attempt reducing the variation of these results by refining the occupant presence model.

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Instead, in the case of comfort PIs, building codes typically exist that define the minimum acceptable percentage of occupied hours within a given threshold. (CIBSE 1994), for example, sets the minimum percentage of occupied hours with an illuminance level above 500 lux at 75%. This kind of compliance-driven calculations would normally come with a standard occupant presence schedule. Nevertheless, it is interesting to determine whether such limits would be achieved also in more extreme presence scenarios, as hypothesized by the diversity patterns. An analysis of the response of the strategies to occupant presence illustrated how only two strategies comply with this limit in all cases: S3 and S6. For these two strategies, it was hence not required to increase the presence model complexity, as they always fell within the required limits. Likewise, one may seek to limit the number of occupied hours with intolerable glare at 10%. Of all investigated strategies, only S4, S5 and S6 fell within this threshold while facing the window. Therefore, for these three strategies it was not required to increase the complexity of the occupant presence model. To conclude, a refinement of the occupant presence model (in this study coinciding with the application of higher model complexity) is necessary only when evaluating the energy performance of S6.

Higher model complexity

The selected higher complexity model for occupant presence was the stochastic model by Page et al. (2008). Page et al.’s discrete-time, non-homogeneous Markov model allows generating the annual presence time-series at 15 min time steps. This model requires two parameters: the weekly occupant presence schedule and the parameter of mobility. If the user is not aware of a building-specific weekly occupant presence schedule or function, an option is to input the ASHRAE schedule (ASHRAE 2013). However, the ASHRAE schedule potentially takes over-hours into account, as the presence probability after 7 pm is a positive quantity (see Fig. 7.14 left). Moreover, it is worth reminding that the implementation of probabilistic OB models in deterministic BPS tools often occurs by comparing the probability computed by the model with a random number at each simulation time step. In the case of occupant presence, if the model probability is higher than the random number, occupants turn out to be present. This workaround may result in non-significant patterns as presence is questioned every 15 min. Therefore, two options were explored, i.e. the standard ASHRAE schedule (Fig. 7.14 left), and a modified ASHRAE schedule (Fig. 7.14 right) whereby people are not present before 8 am and

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after 7 pm. The assumption of not considering over-hours is consistent with the assumption made when developing the diversity patterns (see Fig. 7.9).

Fig. 7.14: Standard ASHRAE occupant presence fraction (left), and modified as absence before 8 am and after 7 pm (right)

The value for the parameter of mobility was taken from (Gunay, O’Brien, and Beausoleil- Morrison 2016). Fig. 7.15 shows the results obtained by running the Page model 50 times according to the presence probability schedules presented in Fig. 7.14. The reasoning behind the value of 50 can be found in (Feng, Yan, and Wang 2016).

90 Limits defined by diversity patterns

ASHRAE ASHRAE mod.

Fig. 7.15: Energy performance of S6 when implementing Page et al.’s model

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The median values of energy demand according to the Page model were closer to the upper limit of the diversity patterns. This result was in line with expectations, as the main share of energy demand was imputable to lighting energy, which is occupant-dependent. In both cases illustrated in Fig. 7.14, it rarely occurred that no occupant was present between 9 am and 6 pm. It is worth noting the differences in results according to the assumed probability distribution function. In particular, assuming the standard ASHRAE schedule as a probability distribution function for occupant presence led to higher uncertainty than the defined diversity patterns. This result was mainly caused by the after-hours. In contrast, the results obtained with the modified ASHRAE schedule were quite robust to occupant presence, and were close to the upper limit indicated by the diversity patterns, as expected. This result can be explained as the major differences across simulation runs concerned whether one or two occupants are present, and not whether the space was occupied or not (see Appendix J: Example of occupancy status obtained using Page et al. occupant presence model). To conclude, implementing a higher model complexity for occupant presence revealed that the expected energy performance of S6 was closer to the upper limit of the diversity patterns, rather than to the median or lower limit. The use of standard ASHRAE schedules (which considers over-time) as input to the Page model led to a higher energy demand than the diversity patterns. The decision-maker should assess whether the actual occupant presence probability distribution function is closer to the ASHRAE schedule or to the modified ASHRAE schedule. By doing so, it would be possible to decide if S6 led to a satisfactory performance or not.

Concluding remarks

This study applied the FFP-OBm strategy to the R&D of adaptive, presence-dependent, innovative shading elements. Six different shading control strategies were evaluated in terms of energy and visual comfort performance. The fit-for-purpose occupant presence model was identified for each strategy and each PI. The results show that most PIs were sensitive to the assumptions made regarding occupant presence. The only strategy/PI that were completely robust to occupant presence assumptions were the glare results for S4 and S5. Both these strategies were developed to avoid glare, and indeed, they delivered the required performance independently of the assumptions made for occupant presence. Despite the different response of various shading strategies to occupant presence, a refinement of the model complexity was not necessary to rank their mutual performance for all investigated PIs. In contrast, it may be needed when calculating the actual performance of a single strategy. In this case study, the stochastic occupant presence model from Page et al. (2008) helped to

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establish that the actual performance is likely be closer to the upper limit identified by the diversity patterns. This result is achieved when neglecting over-hours. Further, this case study highlights the importance of carefully considering the assumptions made while modeling occupant presence. Two different pairs of diversity patterns had a different impact on the strategies/PIs. Moreover, two different schedules input to the Page et al. model yielded drastically different results. In conclusion, this case study identified the potential for the FFP-OBm to be used during the R&D process of adaptive façade elements.

7.2 Performance assessment (Design/Commissioning phase)

The second application case study of this chapter concerns the performance assessment of an office building located in the TU Vienna university campus. While the building is already built and high-resolution data is available from colleagues at TU Vienna (Tahmasebi and Mahdavi 2016), this case study is a double-blind study. In fact, a building model was developed in EnergyPlus as if no prior knowledge was available, mimicking the standard situation of building designers during the design phase. This initial model was named the standard model. The investigated PIs are also typical of the design phase. The available data from a local weather station was used to create a weather file, with the aim of ruling out the uncertainties related to weather and focusing only on OB-related uncertainties. Conversely, the available data with regards to occupant presence status, equipment energy use, light energy use, heating temperature setpoints and window state was used to develop data- driven models. These models acted as ultimate model refinement for each OB aspect when increasing OB model complexity (if/when needed). The EnergyPlus model containing all available data was defined as the calibrated model. This case study was developed to evaluate whether the FFP-OBm strategy correctly identifies the required complexity of different OB models, according to specific performance indicators. In other words, it is interesting to corroborate whether, without prior knowledge, the FFP-OBm strategy indeed leads to a fit-for-purpose model. The intention is to obtain a model of the building – for each PI – that guarantees the desired accuracy but requires less data than the calibrated model, i.e. is less complex. In the following sections, the fit-for-purpose model complexity of various OB models (occupant presence, light use, equipment use and heating temperature setpoint setting) is investigated for a range of performance indicators.

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Problem definition

Purpose The aim of this case study was to assess the performance of a 7-occupant office building located in the TU Wien University campus. Various performance indicators (PIs) were considered, related to both the energy and the comfort performance of the building. The selected PIs were:

― Annual Heating Energy Use; expressed in [kWh/m2]; ― Heating Peak Load; calculated with the Load Duration Curve (LDC) method, as opposed to simply maximum occurring value, and expressed in necessary power to satisfy the heating requirements during 95% of the occupied hours [kW] (see Fig. 7.16);

Fig. 7.16: Example of LDC Method

― Time Outside the EN15251 Category III comfort requirement; calculated as an average of all occupants and expressed in percentage of occupied time [%].

Building characteristics Seven workstations of an office building, in which each occupant has access to an operable window (Fig. 7.17), were the focus of this study. This office space has been investigated before by colleagues from TU Vienna, who developed the calibrated model of the building for different purposes (e.g., Tahmasebi and Mahdavi 2016). The basic office data and modeling assumptions are given in Table 7.4.

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Climate The office is located in Vienna, Austria.

Fig. 7.17: Schematic illustration of the TU Wien office area, with investigated occupants and operable windows

Table 7.4: Basic office area data and modeling assumptions (Tahmasebi and Mahdavi 2016)

Building data/Modeling assumptions Value Net conditioned floor area [m2] 187.6 Gross wall area [m2] 120.1 Average WWR [%] 26.7 Exterior walls U-value [W/m2K] 0.65 Exterior windows U-value [W/m2K] 2.79 Exterior windows SHGC [-] 0.77 Number of occupants [-] 7 Number of operable windows [-] 7 Windows discharge coefficient when open [-] 0.28 Windows air mass flow coefficient when closed [kg/s m] 4.15 x 10-4

Use scenario The maximum LPD was set to 4.1 W/m2. The heat emitted by lights was assumed to be 42% radiant. The maximum EPD was set to 9.9 W/m2, with 20% radiant heat. In the new model developed for this study (later referred to as standard model) reference schedules were used for light use, equipment use and occupant presence, in accordance with widespread standards (ASHRAE 2013)(Fig. 7.18).

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An ideal load air heating system kept Tin=22.5°C during the heating season, i.e. from September 25 to April 21. No mechanical cooling is present. Windows were assumed to be always closed.

0.8

0.6

0.4

0.2

0 2 4 6 8 10 12 14 16 18 20 22 24 Time of day [-]

Fig. 7.18: Standard schedules for for occupant presence, light use and equipment use

Choice of building model complexity and preliminary results

As mentioned, a calibrated model was available in EnergyPlus version 8.7 (Tahmasebi and Mahdavi 2016). The calibrated model was taken as a benchmark for the actual performance of the building. For this study, however, all modeling assumptions concerning OB were standardized, as presented in the introduction to Section 7.2, as if the simulation user had no previous knowledge of OB. As mentioned, the possibility of reaching a FFP model was assessed for each PI. The complexity of a FFP model is possibly in between the standard model and the calibrated model, and the predictive ability of the FFP model is adequate to the desired accuracy. As a first step, the PIs were calculated with the standard model.

Annual heating energy use Fig. 7.19 shows the annual building energy use for heating when standard assumptions about occupant presence, light use, equipment use, temperature setpoint setting and window operation were made. The heating energy demand of the standard model was about 45 kWh/m2y, while the calibrated model indicated a much higher demand, of about 65 kWh/m2y. This figure raises concerns about the appropriateness of using standard models for assessing building performance, as expected.

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60 - 32% 50

10 Heating Energy Use [kWh/m2y] 0 Benchmark Standard Model Model

Fig. 7.19: Comparison between calibrated and standard models: Heating Energy Use

Heating peak load Fig. 7.20 (left) shows the heating peak load when standard assumptions about occupant presence, plug loads use, light use, setpoint setting and window operation were made. While the calibrated heating peak load was 4.7 kW, the standard model predicted a peak of just below 4 kW. Considering that safety margins may be taken into account in practice, one could assume that the system would also be sized appropriately if the standard model was considered. In this sense, the results shown in Fig. 7.20 (left) were less concerning than Fig. 7.19. However, the discrepancy between standard model and calibrated model was still relevant.

Time Outside the EN15251 Category III Fig. 7.20 (right) shows the results of the comfort indicator: the percentage of occupied time falling below the EN15251 Category III standards. In this case, the assumptions about occupant presence, plug loads use, light use, setpoint setting and window operation were also taken from standards. While the calibrated model showed that occupants are uncomfortable during <10% of the time, the standard model showed an alarming 55% of time in discomfort. This result was imputable to the assumption of keeping the windows always closed, which made the indoor environment prone to overheating during the summer, as no cooling system was present. However, this assumption may be often made in reality, as it represents the simplest possible model for window operation. The large discrepancy shows how the standard model was unable to provide significant results concerning this comfort indicator. In the following sections, the FFP-OBm strategy is applied to this case study to verify the strategy’s capability of reaching a FFP model.

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5 60 4.5 - 19% 4 50 3.5 40 3 2.5 30 + 49 p.p. 2 Zone [%] 1.5 20 1 Peak Heating Load [kW] 10 0.5 Time Outside EN15251 Comfort Time 0 0 Benchmark Standard Model Benchmark Standard Model Model Model

Fig. 7.20: Comparison between calibrated and standard models: Peak Heating Load (left) and Time Outside EN15251 Comfort Zone (right)

Impact Indices Method

The only indicator among the selected PIs that could be addressed with the Impact Indices Method is the Annual Heating Energy Use. Therefore, IIs were calculated for this indicator as illustrated in Chapter 4 and are reported in Fig. 7.21.

Non applicable in current Impact Indices Method

Fig. 7.21: Impact indices for Annual Heating Energy Use

Depending on the tolerance limit, different aspects of OB are taken to the next step, i.e. the Diversity Patterns Method. In this case, a limit of II = 0.1 was taken. Therefore, it can be concluded that all investigated aspects (occupant presence, light use, equipment use and heating temperature setpoint setting) must be investigated by means of diversity patterns (Fig. 7.21).

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Diversity Patterns Method

As mentioned in the introduction to Section 7.2, the FFP complexity of occupant presence, light use, equipment use, temperature setpoint setting, and window operation modeling is here investigated. Thus, diversity patterns were defined for the different aspects of OB (Table 7.5).

Table 7.5: OB diversity patterns

Pattern A Pattern B

Presence

Equipment Use

Lighting Use

Tsp, heating 21°C 24°C

Windows Always closed Open if Tin > 26°C

For equipment use, light use, and presence schedules, the patterns consisted of positive and negative variations of the ASHRAE standard. As for window operation, a further scenario was added to the “Always Closed” pattern, whereby the windows were open when the indoor temperature reaches 26 °C. The patterns for the heating temperature setpoint were variations of ±1.5 °C from the initial 22.5 °C. Once the diversity patterns were developed, the simulations were run and the results were processed (Fig. 7.22).

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1 5

0.8 4

0.6 3

0.4 2

0.2 1

0 0

Fig. 7.22: Diversity Patterns Method results for Annual Heating Energy Use (left), Time Outside EN15251 Comfort Zone (center) and Peak Heating Load (right)

Fig. 7.22 shows how in all cases the application of diversity patterns caused a large distribution of results. In particular, the boxplots depicting the distributions overlap in all cases with the calibrated model results.

Decision-making and Mann-Whitney U test

It is assumed that the distributions obtained in Fig. 7.22 were too broad to be able to properly assess the energy and comfort performance of the office building. Therefore, the Mann-Whitney U test was applied to find out which aspects of OB caused such large distributions and may therefore need a more refined modeling approach (Fig. 7.23). A stringent tolerance limit equal to a p-value = 0.1 was taken. Fig. 7.23 shows that Tsp, heating, light use and equipment use were responsible for the spread of results in Annual Heating Energy Use. Instead, equipment use and window operation affected the comfort indicator the most. A refined modeling approach may be needed for Tsp, heating and equipment use when calculating the Peak Heating Load. In the next section, the effect of applying a refined modeling approach to those aspects that turned out to be influential according to the FFP-OBm strategy is investigated.

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Fig. 7.23: Mann-Whitney U test for Annual Heating Energy Use (left); Time Outside EN15251 Comfort Zone (center) and Peak Heating Load (right)

Higher model complexity

The Mann-Whitney U test helped in identifying the influential OB aspects. According to the strategy, the next step consists of refining the modeling formalism of the influential OB aspects. In this case, because real data were available, such data were used instead of merely increasing the model complexity, e.g. by applying stochastic models. Doing so allowed to use the best model available for a given case, and should always be the preferred option, when feasible. Fig. 7.24 shows the effect of refining how the influential OB aspects for each PI are modeled.

Fig. 7.24: Annual Heating Energy Use (left); Time Outside EN15251 Comfort Zone (center) and Peak Heating Load (right) when applying the fit-for-purpose model. The OB aspects that did not require refined modeling formalism are shaded

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Fig. 7.24 shows how the FFP models have a comparable predictive ability with the calibrated model. The results of Fig. 7.24 are given as boxplots because the less influential OB aspects were still modeled by means of diversity patterns. Should the simulation user deem a boxplot (i.e., a PI’s variation) too large to allow decision-making, the next influential OB aspect can be considered for refined modeling. For example, in the case of the comfort PI, according to the Mann-Whitney U test, the model of light use could be refined to possibly reduce the height of the boxplot. The FFP-OBm strategy showed that a refined modeling approach is not needed for: occupant presence and window operation to predict Annual Heating Energy Use; occupant presence, heating temperature setpoint setting and light use to predict the Time Outside EN15251 Comfort Zone; occupant presence, light use and window operation to predict the Peak Heating Load. Therefore, the FFP models are much more lightweight than the calibrated model in all cases, and they still deliver high predictive ability. The decision-maker will assess whether the FFP models’ predictive ability is appropriate.

7.3 Energy Performance Contracting (Retrofitting phase)

The Impact Indices Method (Chapter 4) is an efficient and easy-to-implement method to provide an initial estimate of the potential effect of various aspects of OB on building cooling and heating energy demand. In contrast to common sensitivity analysis approaches based on a high number of scenarios, this method requires only a single simulation run. For these reasons, it is believed to be extremely interesting for energy service companies (ESCOs) that perform energy performance contracting (EPC). In EPC, the ESCO aims to guarantee a specified level of energy savings in the built environment for a client. Among the building energy performance uncertainties that hinder EPC, OB plays a major role. ESCOs are hence interested in including OB-related clauses in their contracts. Two case studies are presented hereafter: the ABT Building (Section 7.3.1), and the Strukton Buildings (Section 7.3.2). The two case studies are carried out to evaluate the potential of the Impact Indices Method in assisting ESCOs for EPC. The Strukton Buildings case study is briefly detailed, as the case study was carried out as a MSc Thesis Project by Miss Lenny Mennen, a student under the author’s supervision. Lenny worked at a company, Strukton Worksphere, and successfully integrated the II Method into the company workflow using the BPS software IES VE. This case study concerned two buildings previously investigated by the company, and allowed the author to verify and improve the suitability of the II Method for practitioners. Therefore, it is interesting to briefly mention the main outcomes

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in this section. For further details, readers are advised to refer to Lenny’s MSc Thesis (Mennen 2018).

The ABT Building

7.3.1.1 Problem definition

Purpose The building is part of a project that has an interest in understanding the OB-related risks associated with EPC. Firstly, the aim of the project is to assess buildings’ potential for EPC. Even if the analysis shows high potential for EPC (i.e., relatively low influence of OB), the ESCO involved is still interested in understanding whether further clauses should be applied in the EP contract for specific aspects of OB on a monthly basis. The PI of interest is:

 Annual Gas Use for Heating;

Building characteristics The building was built in 2001, consists of three floors, has dimensions of 39 x 29 x 9 m3 (W x L x H) and a gross floor area of 2040 m2. There is an atrium spanning the three floors which functions as common space, and a restaurant for the employees. Fig. 7.25 gives an impression of the double façade and atrium of the building as well as a typical floor plan. Automatically controlled sun shading devices are incorporated on the inside of the outer façade for the eastern, southern and western sides of the building to prevent overheating during the summer. As for the thermal characteristics, the external walls, ground floor, and roof R-values are 3.0 m2K/W, while the windows’ U-value is 1.2 W/m2K. The building has Dutch Energy Label A and Energy Index (EI) = 0.96.

Fig. 7.25: Façade of the ABT building (left), impression of the atrium (middle), and plan of the second floor (right)

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The indoor climate of the office is controlled by two air handling units (AHUs): one for the offices, which controls both supply and exhaust air, and one for the restaurant/atrium, which controls only the supply air of the restaurant. The building has a gas-fired boiler and a fully- electric cooling machine. The AHU of the office spaces also has a rotary heat/cold exchanger with an efficiency of around 72 %, according to the technical specifications. The AHUs are also used for night ventilation during the summer to cool the building. The operational hours of night ventilation are between 1 am and 6 am during weekdays, and always on during the weekend. The outdoor air itself is used for night ventilation. The general system properties are specified in Table 7.6.

Table 7.6: General system properties of the ABT building

General system properties AHU offices 122 kW heating, 71.4 kW cooling Airflow: 4.06 m3/s Rotary heat/ cold exchanger Efficiency = ca. 72 % (AHU offices) AHU restaurant (only supply) 23 kW heating, 9.2 kW cooling Airflow: 0.42 m3/s Boiler capacity HR, 160 kW Efficiency = 0.975 Cooling machine 105.9 kW Post heaters 14 kW per post heater Energy generation PV panels HVAC temperature setpoint 22.5 °C HVAC control Centrally/automatically

Climate The building is located in Delft, The Netherlands.

Use scenario At present, the building is used as semi-open office spaces and flexible workspaces, with occupied hours of 7:30 am – 7 pm during weekdays only. Sun shading and climate installations are centrally controlled; hence, people do not directly interact with them. Conversely, occupants trigger the occupant presence-dependent lighting in office spaces and restrooms and have a direct influence on the amount of equipment (two computer screens, a keyboard, and a computer mouse) that is used in the approximately 80 workspaces in the building. Moreover, people (may) have an impact on the thermal status of the building by means of their metabolic rate. While operable windows and internal blinds are present throughout the building, they are seldom

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operated by the occupants. Hence, it was decided to focus the subsequent analysis on the effect of occupant presence, equipment and light use on energy demand for heating and cooling. Hereunder, the analysis is reported for heating only, which represents 20% of the building’s total primary energy consumption, while electricity use for cooling represents only about 5%. Heating is used throughout the year except during June, July and August.

7.3.1.2 Choice of building model complexity and preliminary results

A model of the building was developed in EnergyPlus version 8.7; the model was representative of the building energy behavior both in terms of aggregated yearly results for electricity and heating use (Fig. 7.26) and of hourly electricity profiles (Fig. 7.27).

BPS model Utility

Electricity

Heating

0 50 100 150 200 250 Energy Use [MWh]

Fig. 7.26: Comparison between utility bills (2015) and simulated end uses for electricity (including cooling) and heating of the ABT building

60 50 40 30 20 10 0 1357911131517192123 Hour of the day [-]

Installations Lighting

Energy load [kW] Energy load Small power Kitchen Workspaces + servers Transport Mean real consumption

Fig. 7.27: Simulated hourly load profile of the electric consumers compared with the mean hourly electricity consumption measured for workdays (Monday to Friday) of Week 4, 2017

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7.3.1.3 Impact Indices Method

The impact indices for heating were calculated as in Chapter 4 and are reported in Fig. 7.28. It can be noticed that the whole building impact of each OB aspect is rather low, reaching a peak of 0.40 for light use. It is expected that an II of 0.40 corresponds to a heating energy demand variation of approximately 10-15% if the LPD is varied by ±50% (an extremely high variation). A lower impact on the heating energy demand is expected for OB-related equipment use and occupant presence. It is interesting to note that the total equipment use has a very high impact index, while the potential impact is substantially decreased when looking at OB-related equipment use. The added value of calculating II for each zone is dual: on one hand, it provides an indication that different model complexity levels may be required to represent OB for different zones, and on the other hand it could provide insights into the appropriate placing and installing of OB-related monitoring sensors.

1.0

0.8

0.6

0.4

Impact Index [-] 0.2

0.0 Presence Light Use Eq Use OB-related Eq Use OB Aspect

Whole Building Atrium 1st floor 2nd floor

Fig. 7.28: Yearly Impact indices for presence, light use, equipment use and OB-related equipment use for whole building and each separate zone

In order to verify the results obtained by means of the II, an OAT sensitivity analysis increasing and decreasing presence, LPD and EPD by 50% was carried out. The sensitivity analysis was performed in respect to gas use for heating, as that was the PI of interest for the ESCO. The results are reported in Fig. 7.29. As expected, changing the LPD by ±50% causes the maximum variation in the output (ca. 10-15%), followed by OB-related EPD and occupant presence.

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(a) (b) (c)

60 20% 60 20% 60 20%

40 40 40 10% 10% 10% 20 20 20

0 0% 0 0% 0 0% -50% BASE 50% -50% BASE 50% -50% BASE 50% -20 -20 -20 -10% -10% -10% -40 -40 -40 Variation in in gas use [%] Variation Variation in in gas use [%] Variation Variation in in gas use [%] Variation Variation in in gas use [GJ] Variation Variation in in gas use [GJ] Variation Variation in in gas use [GJ] Variation -60 -20% -60 -20% -60 -20%

Fig. 7.29: OAT sensitivity analysis. Absolute and % gas use variation resulting from increasing/decreasing LPD (a), OB-related EPD (b), and presence (c) by 50%

Monthly analysis As mentioned, one of the aims of the project is to understand whether to incorporate guidelines/clauses in the EP contract for occupants on how to operate the building on a monthly basis. For this reason, it seems relevant to calculate II for the selected aspects of OB for each month. The months Jun-Aug are omitted, as during that period no heating occurs. The results of this analysis are reported in Fig. 7.30.

1.00 Summer months 0.80 0.60 0.40 0.20 Impact Index [-] 0.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Presence Light Use OB-rel Eq Use

Fig. 7.30: Monthly Impact indices for occupant presence, light use, equipment use and OB-related equipment use for the whole building

Fig. 7.30 shows what appears to be an inversely proportional relationship between the effect of OB and weather. In fact, the II increase for all three considered OB aspects as the weather conditions become milder (up to May-September), and decrease again with the arrival of winter months. This result confirms the well-established hypothesis that the relative impact of OB is more important for buildings that are less affected by the outdoor environment. Hence, a similar observation can be made on a seasonal/monthly basis. However, it is important to note that the II give an indication of the relative potential impact

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of OB. In other words, they quantify the % variation of a performance indicator related to its absolute value. As one expects the absolute heating energy demand to be higher during the colder months, the II presented in Fig. 7.30 do not directly provide information about the actual amount of gas that could be consumed because of OB in absolute terms. Fig. 7.31 shows both the absolute monthly variation in gas use and the relative monthly variation when varying the LPD by ±50%. The relative variation follows the trend indicated by the II in Fig. 7.30. In contrast, the absolute variation shows that the “riskiest” months are March and December. August and September, despite presenting a high II (and therefore a high relative variation), are characterized by the lowest absolute variation. This example clearly shows the importance of considering both absolute and relative values when dealing with sensitivity analysis.

8 20%

6 15%

4 10% 2 5% 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0% -2

-5% 50% LPD [%] 50% LPD [GJ] ±

± -4 -6 -10%

Gas consumption variation for -8 -15% Gas consumption variation for -10 -20%

Fig. 7.31: Monthly absolute and relative variation in gas use when varying the LPD by ±50%

7.3.1.4 Decision-making

For this building, it is possible to issue EP contracts with a low risk-factor. If the clients are interested in absolute gas use variation, it is advisable to include clauses about light use for the months of March, April, and Oct-Dec. However, the necessity of including such clauses should depend on the required tolerance, as well as on the assessment of actual light use variability. For this case study, there is no need to proceed with the FFP-OBm to the Diversity Patterns Method, nor to increase the model complexity of any aspect of OB.

7.3.1.5 Physical correctness of results

For the investigated building, historical data about the gas use during the last years was available. Fig. 7.32 confirms that the yearly variation in gas use was not significant, and was

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mostly imputable to differences in weather conditions, here quantified by means of the Heating Degree Days (HDD) indicator, rather than OB.

20000 4000

] 3200

3 15000 2400 10000 1600 HDD [-]

Gas use [m Gas use 5000 800

0 0 2010 2011 2012 2013 2015 2016

Fig. 7.32: Observed yearly variation in gas use (2014 was omitted due to lack of data)

The Strukton buildings

7.3.2.1 Problem definition

Purpose Two buildings were assessed for their potential for EPC, including OB. In particular, Strukton was interested in understanding whether the proposed strategy could be integrated into the risk matrix that is usually employed within the firm. Doing so would allow them to compare OB- related risks with other types of risk such as planning, geology, location, etc. Moreover, the firm was interested in providing a relative comparison of their buildings’ sensitivity to OB by comparing the results to those of a pool of (virtual) buildings. The PIs of interest are:

 Annual Heating Energy Use;  Annual Cooling Energy Use;

Building characteristics Two different buildings with different characteristics, named Building A and Building B, were evaluated (Fig. 7.33). Building A is the office building of Strukton in Son and Building B is the city hall/music theater in Amsterdam. Building B is analyzed only partly, i.e. for the area corresponding to offices. The case studies are both open plan office buildings of about the same size. For both buildings, all floors are considered in the analysis except for the ground floor, which has a different function.

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Fig. 7.33: The Strukton Buildings: Building A (left, source: van Schijndel Bouwgroep) and Building B (right)

Table 7.7 provides a general description of the two case studies. As can be seen from Table 7.7, Building B is an older building and has worse insulation. Table 7.7 shows that Building B is adjacent to other parts of the building, which are also open-plan offices. Relatively less surfaces of Building B are exposed to the outdoor conditions than those of Building A, so Building B has a lower surface-area-to-volume ratio. Moreover, Building B has a lower window-to-wall ratio.

Table 7.7: The Strukton Buildings: General description of the case studies

Building A Building B Floor area [m2] 6700 6000 Floors 6 6 Year of construction 2010 1984 R-value walls [m2K/W] 3.23 2.2 – 2.4 SA:V ratio [m2/m3] 0.26 0.19 WWR [-] 40% 36%

Climate Building A is located in Son, The Netherlands, and Building B is located in Amsterdam, The Netherlands.

Use Scenario The user profiles of the zones are building specific. For example, Building B is designed to have a slightly higher occupant presence level than Building A. The exact profiles can be found in (Mennen 2018). Table 7.8 indicates the building/occupant interactions that were possible in the two buildings. The equipment and light use of both case studies are dependent on occupant presence. One main difference between the case studies is the use of blinds. In Building A the blinds are controlled manually, whereas in Building B they are automatic. This feature results

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in a higher degree of freedom for the occupants of Building A, and potentially higher sensitivity to blind operation due to the higher SA:V ratio and WWR. The temperature setpoint variations are in both cases limited to a ±1°C.

Table 7.8: The Strukton Buildings: building/occupant interactions in the case studies

Building A Building B Blind Operation Manual Automatic, manually overruled Equipment Use Depending on presence Depending on presence Light Use Occupant presence sensors Occupant presence sensors Occupant Presence - - Tsp Setting ±1°C ±1°C

Phase in building lifecycle Building A is built and presently in use. Building B is in construction.

7.3.2.2 Choice of building model complexity

The simulation program IES VE is used (Integrated Environmental Solutions Ltd, 2018). This program was chosen because it is used by Strukton Worksphere. The simulation model of Building A was obtained from an earlier study (van Heijst, 2017). The simulation model of Building B was provided by Strukton Worksphere. Extensive information on the simulation models can be found in (Mennen 2018). A robustness test on the complexity of the building model was conducted by comparing three space granularities: room level, floor level, and building level (Fig. 7.34).

Fig. 7.34: Complexity levels: room (left), floor (middle), building (right) (van Heijst, 2017)

7.3.2.3 Impact Indices Method

The problem concerns heating and cooling energy use, and hence the Impact Indices Method is considered suitable. However, the company was interested in providing a context for the impact indices’ results of the two buildings mentioned above. Because the method has been recently

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developed and the company had no other building model that could act as benchmark, a pool of virtual buildings was developed. The aim of creating such a pool was to be able to provide further context for “high” impact indices. The building variants were obtained by using the simulation models of the case studies. Extreme values for the structure, the gain intensity and the orientation of the buildings were considered (Table 7.9).

Table 7.9: Ranges of extreme values for the building variants

Building characteristic Range Reference SA:V [m2/m3] 0.19 – 0.26 - Orientation (long façade) [°] 0 – 90 - WWR [-] 0.20 – 0.60 (Goia 2016) Façade characteristics Wall R-value [m2K/W] 1.30 – 4.50 (BWT 2015) Roof R-value [m2K/W] 1.30 – 6.00 (BWT 2015) Floor R-value [m2K/W] 1.30 – 3.50 Window U-value [W/m2K] 3.10 – 1.30 g-value [-] 0.59 – 0.73 (Integrated Environmental Solutions Ltd, 2018) Visible transmittance [-] 0.76 – 0.48 (Gaetani, et al., 2018) LPD [W/m2] 5.38 – 16.14 (ASHRAE 2013) EPD [W/m2] 5.38 – 16.14 Occupant presence density 5.45 – 16.35 (British Council [m2/person] for Offices, 2013)

In Table 7.10 the impact indices’ results for Building A and Building B are reported.

Table 7.10: Results impact indices Building A and Building B

Building A Building B Impact index [-] Cooling Heating Cooling Heating Blind Operation 1.10 0.59 0.88 0.44 Equipment Use 0.14 0.19 0.25 0.29 Light Use 0.15 0.20 0.47 0.55 Occupant Presence 0.16 0.21 0.28 0.32 Tsp cooling 0.46 - 0.32 - Tsp heating - 0.10 - 0.43

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In order to verify the results obtained by means of the II, an OAT sensitivity analysis was carried out. Such analysis should act as a verification of the II results, and hence was performed for both OB aspects characterized by high II and OB aspects characterized by low II. In particular, presence, LPD and EPD were increased and decreased by 50%, temperature setpoints by 1 °C, and a scenario in which blinds were operated when incident solar radiation exceeded 250 W/m2 was added. The sensitivity analysis was performed in respect to heating and cooling energy use, as those were the PIs of interest for the ESCO. All building variants were investigated. As an example, the results for light use are reported in Fig. 7.35. The R-values of > 0.9 confirm that there a very high correlation existed between II values and sensitivity to OB.

100% 200%

80% 160%

60% 120%

40% 80% ±50% LPD [%] ±50% LPD ±50% LPD [%] ±50% LPD 20% 40% y = 0.92x - 0.05 y = 1.47x - 0.10 Cooling energy variation due to R² = 0.94 to due Heating energy variation R² = 0.98 0% 0% 0.0 0.5 1.0 1.5 0.00 0.50 1.00 1.50 Impact Index [-] Impact Index [-]

Fig. 7.35: II reliability test: comparison with OAT sensitivity analysis results for the impact of light use on cooling (left) and heating (right) energy. Each dot represents a building variant

7.3.2.4 Decision-making

Table 7.10 shows how Building A is particularly sensitive to blind use for both cooling (II = 1.10) and heating (II = 0.59), and to temperature setpoint setting for cooling (II = 0.46). An analysis of the likelihood and magnitude of variation confirmed that there is a risk in issuing EP contracts without considering these two aspects of OB. Building B, conversely, was much more sensitive to OB. All investigated OB aspects were sensitive for heating and cooling energy use (all II > 0.2). Only by considering the likelihood and magnitude of OB variation, however, it will be possible to confirm whether there is a risk in issuing EP contracts (see (Mennen 2018)).

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7.3.2.5 Physical correctness of results

To check whether the approach highlights the correct OB aspects as being risky, the uncertainty ranges of the OB aspects that were considered as non-influential were also analyzed. In Fig. 7.36 the results obtained by doing an OAT sensitivity analysis on the case studies are shown. The input data used to obtain these results were the realistic building-specific values. Hereunder, Building A was taken as an example since actual data is available for this building. Fig. 7.36 shows how the IIs resulted in a slight overestimation of the sensitivity for equipment use, light use and presence. This is due to the fact that in reality the magnitude of variation is slightly lower than the considered ±50%. However, Fig. 7.36 also shows that the riskiest OB aspects were correctly identified. It will be possible to issue risk-free EPC contracts once clauses regarding blind operation and temperature setpoint setting are added.

40% 40% 20% 20% 0% 0% -20% -20% -40% -40% -60% -60% -80% -80% energy demand [%] energy demand [%] Relative uncertainty range cooling Simulated (II Method) Relative uncertainty range heating Simulated (II Method) Actual (Real Data) Actual (Real Data)

Fig. 7.36: Uncertainty range for Building A (heating and cooling) resulting from the OAT sensitivity analysis

7.4 Concluding remarks

A number of real case studies were presented in this chapter to test the usability of the methods proposed in this thesis, and – in those cases where the buildings were already built – to verify that the outcomes of the strategy are physically correct. The first case study evaluated whether the FFP-OBm strategy may be useful during the R&D process of innovative, adaptive shading systems. In this case study, a number of different shading systems were compared. According to the strategy’s outcome, no higher complexity models for

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presence were needed when ranking the energy performance of the shading systems, nor when evaluating the performance indicators related to DA and glare of the best performing system. Instead, it was vital to devote attention to presence modeling when evaluating the energy performance of the best performing system. The second case study exploited the availability of high-resolution data to evaluate the physical correctness of the FFP-OBm strategy’s outcome. An office building in TU Wien was evaluated in terms of energy and comfort performance. Applying the strategy led to a lighter-weight model than the calibrated model and it was still able to deliver a high predictive ability. This outcome has a number of benefits, from saving time- and cost-expenditure related to modeling, to supporting an efficient placing of sensors for further studies. The third and fourth case studies investigate the potential of the Impact Indices Method for energy performance contracting (EPC). In both cases, the method provides useful indications about which OB-related clauses should be included in the contracts. The results shown in this chapter confirm the usability of the methods proposed in this thesis and the physical correctness of the outcomes of the FFP-OBm strategy.

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8 Conclusions and discussion

‘[…] Keep Ithaka always in your mind. Arriving there is what you’re destined for. But don’t hurry the journey at all. Better if it lasts for years, so you’re old by the time you reach the island, wealthy with all you’ve gained on the way, not expecting Ithaka to make you rich.

Ithaka gave you the marvelous journey. Without her you wouldn't have set out. She has nothing left to give you now.

And if you find her poor, Ithaka won’t have fooled you. Wise as you will have become, so full of experience, you’ll have understood by then what these Ithakas mean.’

Ithaka, C. P. Cavafy, Translated from Greek by Edmund Keeley

This chapter presents the main conclusions of this research in Section 8.1. The limitations, both connected to the state-of-the-art of the research field, and connected to the methods developed in this research, are addressed in Section 8.2. Section 8.3 discusses the potential for future research related to this field. In particular, the discussion focuses on the need to extend the research scope and the fundamental scientific knowledge in the field, and on the necessary R&D activities to encourage the use of the strategy by BPS users.

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8.1 Conclusions

BPS is a useful tool to support the design and operation phases of buildings and the realization of performance goals. An increasing number of goals are directed by sustainability and energy efficiency targets. However, the reliability of BPS results is impaired by uncertainties. In particular, improper OB modeling is a widely recognized source of discrepancy between simulation results and actual building performance. In this context, researchers have developed numerous models with the aim of providing better and more realistic representations of occupant presence and behavior in buildings. These models are fairly complex and are subject to questions about their validity and applicability. Moreover, the effort needed to implement them in BPS may not always be justified, as in some cases they may not be useful or necessary. On the other end of the scale, practitioners still rely upon standard schedules and simple IF-THEN models in their simulations. While recognizing how inappropriate these approaches may be, practitioners lament that there is no better solution available. This dissatisfaction is compounded by the fact that simulation users are unable to know when they should refine their modeling approach and have no systematic guidance on how to choose among the many existing models. To address these issues, this research aimed to develop, test and evaluate a computational approach: the fit-for-purpose occupant behavior modeling (FFP-OBm) strategy. The FFP-OBm strategy can be used to support the simulation user in choosing the appropriate model complexity of various OB aspects for building energy and comfort performance simulation. The concept of “fit-for-purpose” implies that the appropriate OB modeling approach is strictly connected with the object and purpose of the BPS model. Four objectives were directly related to this research aim:

― To develop an effective strategy for the selection of model complexity of various OB aspects;

― To integrate such a strategy into a computational building performance simulation tool and test its reliability by means of virtual experiments;

― To illustrate, using virtual and real case studies, how the developed strategy leads to efficient, informed decision-making and improves traditional modeling techniques;

― To better understand the relationships between various aspects of OB and building (energy and comfort) performance in a number of demonstration examples.

In this section, it is considered whether these objectives were achieved in the research presented

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here.

Development of the FFP-OBm strategy

The FFP-OBm was successfully developed after an initial process of review of the state-of-the- art in OB model selection. Existing studies comparing the predictive ability of models with different complexities were reviewed with the aim of gaining an understanding of the applicability range of various approaches. It was found that no a priori conclusions could be derived. The requirements of a strategy for OB model selection were then identified. It was concluded that the strategy should consider the different aspects of OB rather than OB as a whole, as different actions may have entirely dissimilar consequences on the building performance. For some buildings and some purposes of the simulation, a refined model of a given OB aspect may be essential, while for others it may be superfluous. The modeling approach should be refined only for influential aspects, and the trade-off between input uncertainty and abstraction error should be considered. In this framework, it is essential to be able to distinguish influential from non-influential OB aspects. This goal was achieved by means of three increasingly complex sensitivity analyses. This process of analysis can be understood as sequential, meaning that if the first analysis does not provide a clear answer, then the subsequent analysis should be conducted. However, if an analysis yields a suitable answer, then the simulation user need not progress to the subsequent test. Each of the three analysis steps are summarized below. The first analysis, named the Impact Indices Method, allows to quickly filter out non-influential OB aspects after one simulation run in EnergyPlus. The second analysis, the Diversity Patterns Method, tests the effect of extreme variations of OB by applying all possible combinations of diversity patterns to the aspects identified as influential by the Impact Indices Method. The third analysis, the Mann-Whitney U test, consists of the application of a statistical test to the output of the Diversity Patterns Method. Once the influential OB aspects are identified (in whichever stage), the option is given to reduce their connected uncertainty. If their uncertainty cannot be reduced and the third analysis confirms that a given OB aspect is influential, more refined modeling approaches are evaluated.

Integration of the FFP-OBm strategy in BPS tool and testing of its reliability

The FFP-OBm strategy was developed from the combination of two software packages:

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EnergyPlus (BPS) and Matlab (data processing). The Matlab software is used to apply the diversity patterns, automatically write new .idf files as input for EnergyPlus, efficiently run the simulations and process the results. Moreover, it allows the smooth execution of the Mann- Whitney U test. It is envisioned that the strategy will be automated in the future, rendering it available for a variety of BPS tools. To date, apart from EnergyPlus, the strategy has been successfully implemented in IES VE software. With regards to higher complexity OB models, two approaches are implemented: i) stochastic models from literature, implemented in EnergyPlus by means of the Energy Runtime Language (ERL) in the Energy Management System (EMS); and ii) high-resolution real data, collected at a 15-min time interval. The reliability of the different methods comprised in the strategy was tested by means of virtual experiments. In particular, the outcome of the Impact Indices Method was compared to traditional sensitivity analysis techniques (such as OAT sensitivity analysis). Alternatively, to test the Diversity Patterns Method, higher model complexity was also applied to non-influential OB aspects and the results were assessed. The virtual experiments were successful, indicating that the methods were able to correctly determine which OB aspects were worthy of further investigation.

Illustration of the usability and efficacy of the FFP-OBm strategy

A number of case studies were conducted to illustrate how the presented strategy aids the consideration of the uncertainties related to OB, thereby allowing the simulation user to make more informed choices than is possible with traditional techniques. The first case study evaluated the FFP-OBm strategy in the R&D process of occupant- dependent, adaptive shading systems. In the results related to energy use up to a 20% difference was found between the traditional approach and the developed strategy. It is worth noting here that the only difference between the two conditions was the modelling complexity of occupant presence. The second case study used the FFP-OBm strategy to assess the energy and comfort performance in an office building through PIs that are routinely considered in the design phase, i.e. Heating Energy Use, Heating Peak Load, and Time Outside Comfort Zone. However, in this case the building was built, and it was possible to measure the strategy results against real data. The outcome showed that the strategy was able to produce a more lightweight model than the calibrated model and it still delivered high predictive ability. In other words, in this case the FFP model was simpler than the calibrated model, confirming that higher complexity models are not always needed.

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The third and fourth case studies confirmed the capability of the Impact Indices Method to support Energy Performance Contracting. The method is able to quickly identify the potential for EPC and/or OB aspects to be carefully regulated in the contracts. It is also noteworthy that both of these case studies were carried out at companies where the usability of the method was evaluated by practitioners and the Impact Indices Method was implemented in the company’s workflow.

New insights derived from the application of the FFP-OBm strategy

A number of insights were gained from the application of the strategy to virtual and real case studies. Some of these insights were previously formulated as hypotheses in Section 3.3.4, while others derive from research activities connected to this thesis.

 The effect of OB on building performance cannot be generalized a priori: ― Different buildings are affected differently by OB; ― Different PIs are affected differently by OB; ― Different aspects of OB have different effects on the results; ― Alongside the potential effect of OB on building performance, it is important to quantify the likelihood of variation of various OB aspects, as well as the magnitude of such a variation. This process is necessary to convert potential impact into actual risk.

 About OB model complexity: ― The appropriate OB model complexity is indeed case-specific, it cannot be selected a priori, and it depends on the object and purpose of the simulation; ― The fit-for-purpose OB model complexity varies according to aspect of OB; ― Higher complexity models are often not needed for decision-making and are subject to questions regarding their validity and applicability; however, consciously addressing the issue of OB model selection as opposed to using traditional, oversimplified techniques is beneficial for successful decision- making in building design and operation; ― There is a strong need for validated, reliable, higher-complexity OB models; such models may also be developed from diversity patterns, as is similar to the case of weather uncertainties modeling; ― If the simulation user uses diversity patterns, all possible pattern combinations must be simulated;

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― The quest for fit-for-purpose modeling is a multifaceted problem, which does not concern complexity alone.

 About the here-presented FFP-OBm strategy: ― The results derived from the Impact Indices Method (one simulation required) are in line with those derived from OAT sensitivity analysis (multiple simulations required); ― The Impact Indices Method can be used to support Energy Performance Contracting; ― The FFP-OBm strategy can be used to support informed design decision- making; ― The results of the FFP-OBm strategy increase the reliability of BPS results if compared to traditional techniques, assuming the validity/applicability of the connected higher complexity models.

8.2 Limitations

During the development of the strategy, a number of decisions were made that inevitably influenced the outcome of this thesis and the applicability range, as well as the reliability of the methods presented here. It is worth making a distinction between limitations of the OB modeling field, which relate to the relatively immature state-of-the-art in the field of OB modeling, and limitations of the FFP-OBm strategy, or the limitations connected to the methods and the required input from the user of the strategy.

Limitations of the OB modeling field

Chapter 2 highlighted the common issues connected with currently available higher complexity OB models. The fact that most available stochastic models and ABMs have not undergone rigorous verification and validation is a limitation of the actual results presented in this study regarding building performance. However, this external limitation does not have an effect on the development of the strategy or on the reliability of the proposed methods, since it does not compromise the theoretical grounds of the strategy. The reliability of the models’ outcome is weakened by the lack of detailed guidelines regarding their applicability. The implementation in BPS software (such as variability of the coefficients or required simulation time-step) further impairs the credibility of the models’ results. In this

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sense, it cannot be guaranteed that the numerical outcome of the FFP-OBm strategy in its present state yields physically correct results, as the connected models cannot be fully trusted. To overcome this issue, the author used real data when it was available rather than higher complexity models. The author envisions that, in the near future, the database of OB models connected to the FFP-OBm strategy (see Fig. 6.2) will only include verified, validated and applicable models. Until then, it is suggested that all available models of a given complexity are used and their results are critically compared. ABMs are not implemented in this study as they present even greater applicability issues than stochastic models. Diversity in stochastic models is also not considered for the same reasons, but it should certainly be part of future research.

Limitations of the FFP-OBm strategy

As mentioned in Chapter 4, the current development of the Impact Indices Method uses ready- made output from the simulation run regarding the sensible heat balance of a building. This is a strength of the method, which is supposed to be easily accessible to practitioners and easy to implement. However, the results should be carefully considered when significant latent loads are present and in buildings characterized by particularly high gains/losses ratios and a low time- constant (i.e. for those cases where the utilized gains are suspected to be extremely low). While these are important limitations, it is worth considering that the Impact Indices Method was developed as an initial screening test to identify non-influential OB aspects. In this sense, the two limitations are not crucial for the usability of the method. In fact, any errors that may arise would always result in an overestimation of the impact indices, i.e. in identifying non-influential OB aspects as influential, and never vice versa. The Diversity Patterns Method requires some assumptions to be made regarding the diversity patterns. These assumptions are critical for the outcome of the method, as is always the case with assumptions in scenario analysis. In the course of this research, different tests were carried out to evaluate the robustness of the method to the scenario definition (e.g., Section 7.1.4). It remains critical that the closer the patterns are to the actual potential variation in behavior, the more representative the method’s outcome is. This point should be carefully addressed when automating the strategy to ensure that the variations are transparent and can be easily modified by the user. The option of considering more than one high/low pattern combination should be evaluated. Further, the final user of the strategy should be made aware of the standard p-value considered for the Mann-Whitney U test. The implications of the p-value (in this doctoral disseration taken as p-value = 0.05 – 0.1) should be clearly communicated and its value should be easy to override.

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Limitations arising from the limited scope of applications of the strategy to date are discussed in relation to suggested future research that may mitigate them in Section 8.3.

8.3 Future research

The efforts connected with the development, integration in BPS, and application of the presented FFP-OBm strategy helped in identifying a number of necessary topics for future research. These topics concern: i) extension of scientific knowledge (Section 8.3.1 and Section 8.3.2); and ii) R&D to enable the widespread implementation of the here presented strategy in BPS software (Section 8.3.3).

Extension of the research’s scope

― The Impact Indices Method is currently developed for two performance PIs only, i.e. Heating and Cooling Energy Use. The method is able to provide a fast screening for influential OB aspects; this ability is useful for practitioners, as well as essential for an efficient fit-for-purpose OB modeling workflow. It would be valuable to develop similar, fast screening methods for a wider range of PIs, for example comfort indicators. ― A wider range of purposes, PIs, space-scales and time-scales should be addressed. In particular, previous research (Giskes 2017) confirmed that there is an averaging effect when the building-level scale is considered as opposed to the room-level scale. It would be interesting to consider the implications of the strategy at smaller space-scales, as well as bigger space-scales such as neighborhoods. However, the building-level and floor-level scales addressed in this thesis cover the majority of today’s practical uses of BPS. ― The FFP-OBm strategy should be tested on different building types and BPS software to verify its applicability range; ― The range of models and complexity levels of the integrated OB models should be extended, for example to include diversity in stochastic modeling and agent-based models. Doing so would also allow to further tackle the notion of trade-off between approximation error and uncertainty in input parameters when increasing the model complexity. At the present time, due to the questionable accuracy of higher complexity models (and therefore their connected ability to provide a better representation of reality), a rigorous investigation of this concept is not possible;

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― It would be useful to derive similar strategies for different building model complexities (the issue of fit-for-purpose building model complexity itself also deserves further research).

Application to connected areas of interest

This research thoroughly considered only one sub-domain of the BPS model: the OB sub-domain. However, as indicated in Section 2.1, the sub-domains are closely interconnected. A necessary area for further research lies within the interconnection of OB with other sub-domains and model inputs. Doing so would allow for comparison of the uncertainties derived from OB alone with those from other sub-domains, and for the establishing of best-practices for the implementation of OB models based on quantitative research (such as, “higher complexity window models are only advisable with an appropriate airflow network model”). Moreover, a wider application of this research could provide significant insights concerning grid interaction and energy matching. The simulation of concepts such as demand side management and building energy flexibility require the prediction of occupant behavior. It is therefore necessary to direct research towards establishing fit-for-purpose OB modeling practices in these fields of inquiry. Further interesting research directions are: occupant-centric performance evaluation; occupant- centric building design; and simulation-based occupant feedback.

Automation of the strategy

The FFP-OBm strategy in its present state consists of a number of scripts coded in Matlab. These scripts allow the user to run simulations in EnergyPlus, to analyze and visualize simulation output, to write .idf files when applying the Diversity Patterns Method, and to implement the Mann-Whitney U test. All these actions have potential for automation, which would minimize the effort required by the user. The users would still be expected to take an active role in: i) assessing whether a decision can be made following each strategy step; ii) verifying the assumptions regarding diversity patterns and p-value and possibly overriding them; iii) making themselves aware of the models in the connected database of OB models and their requirements, and possibly adding building-specific real data. The automation of the strategy is essential to ensure its diffusion outside the academic community. However, carrying out such an operation is the domain of computer scientists rather than building scientists. Discussions regarding implementation in various BPS software programs

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are being carried out. All developed code is made available on https://data.4tu.nl/repository with DOI 10.4121/uuid:3e03a5ac-b1c9-42a4-b7f4-6ace04c23b90.

In conclusion, it is important to note that this strategy and the methods developed in this strategy will not become obsolete with the increase of computational power and available data. For example, the identification of the main influential aspects of OB for different buildings and purposes of the simulation is a necessary step for a number of applications. Among them are: i) saving time- and cost-expenditure related to modeling; ii) prioritizing data collection related to specific OB aspects; and iii) supporting the efficient placement of occupant sensors for further studies. Moreover, the trade-off between input uncertainty and abstraction error when increasing model complexity should always be considered in order to optimize the overall error in the simulation predictions. In other words, one should always prefer models that are as simple as possible, but not simpler.

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Appendices

Appendix A: Mathematical formulations for probabilistic models

Generalized linear models and logistic regression

Various approaches are available to model the relation between probability of an occurrence and predictor variable(s). A number of researchers (e.g., (Mahdavi et al. 2008)) proposed using a linear relationship between the action probability p and the predictor variable x, such as:

, , ⋯, (A.1)

where is the vector of the regression coefficients. However, the assumption of a normally distributed response variable and of a linear relationship between the predictor variable and the mean value of the action probability (or response variable) is not always verified. While it could be helpful to describe non-adaptive behaviors such as the relationship between presence and use of appliances, the representation of adaptive behaviors typically requires binary yes/no values (such as “are the lights on? Are the windows open? Are the blinds up?”), rather than continuous values that change linearly with a predictor. This issue was overcome in 1972, when statisticians R. W. M. Wedderburn and J. A. Nelder proposed the Generalized Linear Models (GLM) (Wedderburn was to pass away three years later, at 28, of anaphylactic shock from an insect bite while on holidays). GLM unify various statistical models, including linear regression, logistic regression, and Poisson regression. In particular, GLM generalizes linear regression by allowing a linear relationship between predictor variable and response variable via a link function g, such as

(A.2)

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As mentioned, OB models often need to predict a probability of making a yes/no choice (also called Bernoulli variable), with probabilities bounded on both ends (they must be between 0 and 1). For example, if one was to relate the probability of turning on the lights as a consequence of the workplane illuminance, one may say that at a given illuminance the odds are doubling: from 2:1 odds, to 4:1 odds, etc. It would not be reasonable to double the probability itself, say from 50% to 100%, etc. In this case, the response variable follows a Bernoulli distribution and would be typically modeled with a log-odds (or logit) link function. Models using log-odds (or logit) link functions are known as logistic regression models or logit models. Instead, if one was interested in predicting the number of lights which are on, one would model the problem with a Poisson distribution and a log link function. Some common distributions with typical uses and canonical link functions are shown in Table A.1.

Table A.1: Relevant distributions of logistic regressions with link function and typical applications

Distribution Typical use Link name Link function Application Normal Linear-response Identity Temperature,

data CO2, ... Poisson Count of Log ln Number of occurrences in occupants fixed amount of time/space Poisson Count of Complementary log log1p Number of occurrences in log-log (cloglog) occupants fixed amount of time/space Binomial Proportion of Logit ln # lights ON “yes” occurrences 1 out of n yes/no occurrences Bernoulli Outcome of single Logit ln Lights ON/OFF yes/no occurrence 1 Bernoulli Outcome of single Probit Φ Lights ON/OFF yes/no occurrence

A further generalization of the GLM are the Linear Mixed Effects Models (LMM). LMM introduce random effects U (assumed to be normally distributed) alongside the explanatory variable X, such as

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(A.3)

Random effects are useful to explain variations that are present in the data, but whose direct relation to the outcome variable is meaningless for the model. For example, data collected in different years could show an incomprehensible year-dependency, which should be considered also if year is obviously not a suitable predictor variable.

Random processes: Bernoulli and Markov

Bernoulli processes are discrete-time stochastic processes consisting of a sequence of identically distributed and independent binary variables, canonically 0 and 1. A Bernoulli process has no memory and each time step can be considered as the start of a new process. A typical example of a Bernoulli process is a set of coin tosses, where p = ½ at each toss for an unbiased coin. If p is constant in time the process is homogeneous, while if p is time-dependent the process is non- homogeneous. In the context of OB, however, the assumption of independence from current time step appears simplistic. Markov processes extend Bernoulli processes by including the current time step t to predict t+1. A process satisfies the Markov property if its future state can be predicted based solely on its present state, independently on the process history. This property is referred to as memorylessness. The probabilities associated with state changes are called transition probabilities. A Markov chain is a Markov model that predicts states at discrete equally-spaced times, by means of transition probabilities matrices. The matrix entries can be calculated e.g. from time-use (TU) data. A Markov chain is defined as follows. Let M be a finite set, and T be an index set. A collection of M-valued random variables with ∈ is called a Markov chain if the following equation holds true:

|,,…, | (A.4)

Hence, the previous time step contains all information to calculate the probability of the current time step. This Markov property expresses the memorylessness of the process . The set M is called the state space of the Markov chain. For , ∈ the conditional probability is given by

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| , (A.5)

If the transition probabilities do not depend on time, the Markov chain is called homogeneous. Otherwise it is called non-homogeneous. A drawback for using Markov chains in behavioral modeling is that the probability functions are time-step dependent. To solve this issue, (Gunay et al. 2014) propose using discrete-event Markov chains. A Hidden Markov Model (HMM) is a probabilistic model that consists of a Markov chain, whose states are not directly observed, and a series of observations.

Survival analysis

Survival analysis models offer an alternative to Markov chains. Such models compute the expected duration of time until an event will occur. For example, the expected duration of time until a light will be switched OFF. They consist of a survival function, conventionally denoted S, and defined as Pr , which denotes the probability Pr that the time to event T is greater than a specified time t. Related to this function are the lifetime distribution function F (or the complement of the survival function), defined as Pr 1 and the differential of the distribution function . Different functions of f can be assumed to fit measured data for the survival function, such as the exponential distribution or the Weibull distribution .

Bayesian networks models

A Bayesian network (or Bayesian Belief Network (BBN) or causal probabilistic network) is a graphical model that encodes probabilistic relationships between predictor and response variables. The advantage of Bayesian models is that they can learn causal relationship, hence aiding in gaining understanding of the problem under investigation; this attribute is particularly interesting when dealing with incomplete or uncertain information. Because of the causal and probabilistic nature of this formalism, Bayesian networks have been explored for the domain of OB.

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Appendix B: List of existing OB models from literature

Existing models of presence

Table B.1: Review of available presence models according to: level of complexity; reference; simulation or modeling framework; type of behavior; building type; location; pros; cons

Complexi Model ty (M) or (0=non- Simulat probabilis ion tic, Author(s) framew Type of Building Locat Keywords Pros Cons 1=probab year [Ref.] ork (S) behavior Type ion ilistic, 2=agent- based models) 0 (Davis and M Occupancy Profiles University US Clustering for Building- Nutter status derived from weekday type; specific; many 2010) extensive estimate of info needed; measured data uncertainty high (measurements) dependence on data 0 (Duarte, M Occupancy Descriptive Office US Clustering per Open plan Van Den status statistics; weekday and offices not Wymelenbe validated month type; accurately rg, and comparison described; Rieger with ASHRAE comparison 2013) guidelines and with Page Page model model unclear 0 (Ardeshir M Occupancy Statistically Office A Separate sets of Requires Mahdavi aggregated data used for aggregate and profiles; training and profile of Tahmasebi building evaluating the presence 2015) systems models; probability control comparison with Reinhart’s and Page et al.’s models 1 (D. Wang, M Occupancy Non- Office US Vacant Fixed profiles Federspiel, homogeneous intervals’ weekends/week and Poisson distribution days; total Rubinstein process; (improves presence 2005) integrated Reinhart’s overestimated; EnergyPlus; model); time intervals do not partially varying; fit well validated matches with observation 1 (J. Page et M Occupancy Markov chain Office and CH Vast range of Long-term al. 2008) non- Household applications monitoring; homogeneous (given the right complex input; 2 states; inputs); underestimates validated; includes long total absence; MATLAB absences; calibration on 5 script; comprehensive; university integrated realistic; offices EnergyPlus pioneer MC-

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based occ. modeling 1 (Richardso M Occupancy Markov chain Household UK Active/inactive Only n, status and n non- users; sharing classification Thomson, homogeneous behavior; weekdays/week and Infield 2 states MC; combined with ends and 2008) partially activity model; people/househo validated; free download ld implemented Excel 1 (Yu 2010)M Occupancy Genetic Office CH 80-83% Single-person programming accuracy; office only; (GP) different testbed of 5 dataset for offices for 4 training and weeks only; testing; same does not dataset as Page compare well model with Page model 1 (C. Wang, M Occupancy Markov chain Office - Produces non- Probability Yan, and homogeneous; synchronous functions not Jiang 2011) partially change of O in time- validated; time/uneven dependent; MATLAB distribution of arithmetic script O in space; speed problem simplicity, [Feng.]; simple accuracy validation/calib ration missing 1 (Dong and S Occupancy Hidden Office US Multi-occupant Max 4 Lam 2011) n Markov Model office; 83% occupants based on accuracy; predicted; Gaussian comparison requires testbed Mixture with ASHRAE with high Model (GMM) 90.1; 18.5% amount of energy savings sensors for (EP) development 1 (Chang and M Occupancy Cumulative Office - 5 occupancy Single-person Hong 2013) and patterns; office only probability extensive distribution database (200 function cubicle offices) 1 (Aerts et M Occupancy Probabilistic; Household B 3 states, 7 Strong al. 2014) hierarchical occupancy dependence on clustering patterns; key TUS; variables occupancy only transition probability and duration probability; improves (Wilke et al. 2013); calibration for download; low complexity 1 (P. D. M Occupancy Markov chain Office US Captures No correlations Andersen status and n non- dependence on between et al. 2014) homogeneous time; occupants; no with time step comparison day-to-day as covariate with correlations; homogeneous complexity Markov chain and non- homogeneous

182

with exponential smoothing 1 (Jia and M Occupancy Queuing Office US Minimizes Average Spanos n theory; calibration behavior of 2017) arrivals efforts; multiple described by validated with occupants only; Poisson (Liao, Lin, and occupancy processes Barooah 2012); patterns of different different spaces dataset for assumed to be training and independent; validation; lower accuracy comparison at short time with MCM, intervals ABM and GM 2 (Erickson M Occupancy Agent-based; Office US Optimize Poor fit of et al. 2009) integrated HVAC loads occupancy eQuest with estimation occupancy- (20% error, but based energy shown to have control low impact); relatively low possible savings 2 (Liao, Lin, M Occupancy Agent-based; Office US Relates with Much and validated former state information in Barooah [Feng]; parallel an instance 2012) low-complexity [Feng]; high model based on complexity; not covariance suited for real- graphical model time occupancy framework; estimations; no improves Page's clear model integration 2 (Feng, S Occupancy Cumulative Office - Various Occupancy Yan, and and occupancy only; Hong 2015) probability levels complexity distribution integrated; function flexible and extensible, ease of updates or maintenance

Existing models of adaptive behaviors

Table B.2: Review of available adaptive behaviors models according to: level of complexity; reference; simulation or modeling framework; type of behavior; building type; location; pros; cons

Complexi Model ty (M) or (0=non- Simulat probabilis ion tic, Author(s) framew Type of Building Keywords Location Pros Cons 1=probab year [Ref.] ork (S) behavior Type ilistic, 2=agent- based models)

183

1 (Hunt M Lighting Probit Office and UK Pioneer No combination 1979) control analysis; school field-based with other integrated stochastic control laws ESP-r and modeling; (e.g. dimming, EnergyPlus function of occupancy work plane sensors) illuminance 1 (Newsha M Lighting Markov chain; Office CA Function of Switch-on m 1995) control + not validated work plane events during occupancy illuminance; occupation variety of period not lighting considered controls 1 (Degelma M Lighting Monte Carlo; Office and US, JP Energy Not calibrated; n 1999) control integrated university saving no real utility ENER-WIN potential records with sensors/dim mers 1 (Richards M Lighting Markov chain Household UK Active/inact Only on, control + non- ive users; classification Thomson, occupancy homogeneous sharing weekdays/week and 2 states MC; behavior; ends and Infield partially combined people/househo 2008) validated; with activity ld implemented model; free Excel download 1 (Widén, M Lighting Markov chain Household JP Absent/pres Old (‘96) TUS; Nilsson, control + non- ent problems in and occupancy homogeneous inactive/pre night-time Wäckelgå 3 states MC; sent active demand data rd 2009) validated user collection; lighting sharing not included 2 (Stokes, M Lighting Bottom-up; Household UK Fine time- Old (’96-‘97) Rylatt, control Object-based; scale (1 TUS; and validated min); dependence on Lomas flexible large amount of 2004) model design input (single/multi data/assumptio ple ns; complexity dwellings); aggregated demands 1 (Zhou et M Lighting Poisson and Office CN, HK Considers Case specific al. 2015) control normal time-varying (large offices distribution; nature peak with no validated for usage; daylight weekdays uncertainties control) in occupant behavior (main driver) 1 (C. Wang M Lighting Weibull Office CN General Tested with et al. control distribution as mathematica single 2016) probabilistic l formula for environmental function; relating OB factor; not Stevens’ in response validated; power law to requires environment estimation of al parameters; 4 conditions; months only; lightweight; one office only

184

can be extended 1 (C. M Lighting Decision tree Office CA Function of Fixed profiles Reinhart and shades including occupancy/ 7h45-18h15; and Voss control logistic work plane presence 2003) regression; illuminance; overestimation integrated active/passi (no absences Lightswitch ve user; apart from Wizard, switching break); no DAYSIM, actions intermediate ESP-r and depend on switch-off; EnergyPlus; random blinds fully Ligtswitch200 probability; open or closed; 2 blinds no thermal control considerations function of for blinds glare risk; activation improves Newsham et al.’s model 1 (Newsha M Lighting Markov chain; Office CA Function of Switch-on m 1994) and shades not validated work plane events during control illuminance; occupation variety of period not lighting considered controls 1 (Mahdavi M Lighting Linear Office and AMentions Dependency on et al. and shades regression university different dataset; only 2008) control models are occupancy and needed for few different environmental buildings; triggers calculates considered savings potential 1 (Haldi M Shades Markov chain Office CH Initial blind Single and control MC; validated status, configuration of Robinson indoor/outd blinds 2010) oor illuminance input to Markov process; separate sub-models for chosen shaded fraction 1 (Fritsch M Windows Markov chain; Office CH Window High et al. opening validated opening dependence on 1990) angle; time series; function of winter only

Tout 1 (Geun M Windows Logistic Office UK Function of Summer only; 6

Young opening regression; Tin, time of offices only; Yun and validated; day, considers Steemers integrated previous natural 2008) ESP-r and window ventilation EnergyPlus state; behavior but separate not sub-models exhaustively; for WO at diversity not arrival, explicitly departure, considered

185

during occupancy 1 (Rijal et S Windows Logistic Office UK Function of Old (’96-‘97)

al. 2008) opening regression; Tout and Tin TUS; not clear

validated; (to include how Tout is integrated building considered; ESP-r and design); window EnergyPlus active/passi opening dead ve user; band needs develop revision; no robust diversity design solutions 1 (Herkel, M Windows 2 stochastic Office DE Correlation Only predicts Knapp, opening processes for with season; windows status; and occupancy used to lacks Pfafferott and window assess generalization; 2008) opening; sub- robustness no diversity models of natural arrival, ventilation; occupancy, most departure openings at arrival 1 (Yun, M Windows Markov chain Office UK Passive, Single-person, Tuohy, opening non- medium and single-sided and homogeneous active natural Steemers Monte Carlo occupant ventilation 2009) with logistic classification office only; regression ; light, summer only medium, heavy thermal mass; comparison with Humphreys’ algorithm 1 (Haldi M Windows Markov chain Office CH Extensive (7 No extensive and opening with logistic yrs.) elaboration on Robinson regression and measuremen BES 2009) survival ts; function integration;

analysis; of Tout, Tin, building- integration humidity, specific possible in wind speed; calibration; any BES; refinement windows angle validated active/passi opening needs ve; extensive refinement; cross- diversity not validation explicitly considered 1 (Schweike M Windows Bernoulli and Household CH, JP Comparisons Issues with r et al. opening Markov chain with Haldi external 2012) and calibration; Robinson poor window 2009 and use prediction Rijal et al. of models 2007 calibrated from Swiss data for Japan 1 (Zhang M Windows Probit model Office UK Considers No and opening orientation; presence/absen Barrett consider ce data; only 2012) non-office one driver

spaces in Tout<20C; only

186

office one building; buildings no validation nor integration in BES 1 (R. M Windows Markov chain Household DK Considers 4 Occupancy

Andersen opening with logistic groups of function of CO2 et al. regression dwellings: levels; only 2013) including owner- data Jan to interaction occupied/ren Aug terms tal, naturally and mechanically ventilated 1 (Haldi M Window Markov Office CH Improves on Calibration 2013) opening and process (Haldi and must be shades discrete-time; Robinson improved control probabilities 2009; Haldi as mixed- and effects logistic Robinson model 2010); considers distinction between fixed and random effects; considers inter- individual variability 1 (Fabi et M Windows Markov chain Office CZ Extensive No validation; al. 2014) opening with logistic range of some variables regression measured only monitored including variables in some rooms interaction terms 1 (D’Oca M Windows Logistic Office DE Identificatio No validation; and Hong opening regression; k- n of one building 2014) means motivational only clustering; , opening association duration, rule interactivity and opening position behavioral patterns 1 (Li et al. M Windows Logistic Office CN Multi-factor One building 2015) opening regression; analysis only; weekdays Monte Carlo with SPSS during1.5 simulation of relevant months only; environment 10000 runs al triggers needed; no reversal

function; Tout only

1 (Calì et M Windows Logistic Household DE One full CO2 as main al. 2016) opening regression year, 300 predictor windows, 60 difficult to apartments, simulate in high BPS; two resolution; models for each model window (one selection for opening one

187

analysis for closing); based on lack of AIC; room- occupancy data dependency 1 (Barthel M Windows Bayesian Household DK Pioneer BN One apartment mes et al. opening Networks for OB only 2017) modeling 2 (Alfakara S Windows Agent-based; Household UK Response to Agents’ actions and opening + integrated summer (lighting, Croxford cooling TAS overheating windows, 2014) cooling,…) fed to BS model every time step; only 2 occupants 1 (D’Oca et M Windows Markov chain Household DK Active/medi No comparison al. 2014) opening + with logistic um/passive with measured thermostat regression; user; values; case integrated compares 5 specific IDA Ice scenarios from deterministic to probabilistic 1 (Gunay M Thermostat Markov chains Office CA Comparison Dependency on et al. setting discrete-event with Markov the dataset; 2018) with logistic chains cannot be regression discrete- generalized time; actual implementat ion

Existing models of non-adaptive behaviors

Table B.3: Review of available non-adaptive behavior models according to: level of complexity; reference; simulation or modeling framework; type of behavior; building type; location; pros; cons

Complexi Model ty (M) or (0=non- Simulat probabilis ion tic, Author(s) framew Type of Building Keywords Location Pros Cons 1=probab year [Ref.] ork (S) behavior Typr ilistic, 2=agent- based models) 1 (Capasso M Load Bottom-up; Househol IDSM High et al. adds power d application; dependence on 1994) demand of accuracy; outdated (’88- individual aggregation ’89) data; appliances; and complexity of partially ownership input data validated 1 (Paatero M Appliance Bottom-up; Househol FI 10000 Partly and Lund electricity statistical d and households; overlapping 2006) demand mean values Office application datasets for and general to simple statistical DSM analysis, model

188

statistical training and distributions verification; no time-based pricing for DSM; needs stronger validation 2 (Tanimot M Load Agent-based; Househol JP Public Complexity (32 o, validated d statistical activities); only Hagishim data summer, only a, and (improves cooling loads; Sagara Page et al.’s needs further 2008a) model); validation coupled with load calculation 0 (Gaceo, M Load Artificial Househol E Comparison No comparison Vázquez, Neural d with Spanish with real data; and Network; Technical overall unclear Moreno integrated Code for 2009) TRNSYS; Buildings ESOM (CTE); extensive data 0 (Armstro M Load Bottom-up; Househol CA Considers Limited ng et al. profiles d system available 2009) derived by performance information; measured in underestimatio data; cogeneration n of base-loads; validated ; typical does not households consider HVAC function of demand 2 (Richards M Appliance Bottom-up Househol UK 22 domestic Dependence on on et al. usage and d dwellings TUS; building 2010) electricity data for thermal validation; behavior not Occupancy included as in (Richardson, Thomson, and Infield 2008) 0 (Gottwalt M Load Bottom-up Househol DE Includes Dependence on et al. d time-based dataset; 2011) pricing for avalanche DSM effects (simultaneous shifting of appliance runs to cheaper hours) 0 (Buso, M Load Calibrated Househol I Comparison 1 key user; data D’Oca, realistic d with from 1 and schedules; UNI/TS dwelling; no Corgnati validated; 11300; issue data for DHW 2014) integrated of system and heating; IDA Ice sizing case-specific; based on past history of consumption 0/1 (Mahdavi S Plug loads Linear Office A Compare One office , regression and deterministic building only; Tahmase and mobility factor

189

bi, and Weibull probabilistic from previous Kayalar distribution approaches publications 2016) 1 (Yilmaz, M Appliance Inverse Househol UK Compares No comparison Firth, usage transform d two among various and sampling; cdfs approaches; households; no Allinson (appliance 16 appliance DSM 2017) duration) and types; application pdfs (switch- considers on) variation within individual households 1 (Jordan M DHW Use Bottom-up; Househol CH Multiple Low fractional and statistical d configuratio energy savings Vajen mean values ns (3%) due to 2001) and Gaussian considered; realistic load distribution; multiple profiles integrated data- TRNSYS aggregation predictive ability comparison 1 (Kazmi et M DHW Use Markov Househol NL Propose 6 households al. 2016) decision d control only; significant process strategy for differences in (MDP), optimized mean and polynomial DHW use; variance of regression lightweight flexibility algorithm

Existing models of comprehensive occupant behavior

Table B.4: Review of available comprehensive OB models according to: level of complexity; reference; simulation or modeling framework; type of behavior; building type; location; pros; cons

Complexi Model ty (M) or (0=non- Simulat probabilis ion tic, Author(s) framew Type of Building Keywords Location Pros Cons 1=probab year [Ref.] ork (S) behavior Type ilistic, 2=agent- based models)

1 (Nicol MUser Logistic Office F, GR, Function of Tout Survey in form

2001) behavior regression and P, and Tin; first of charts; no (windows, househol PAK, coherent prob. consideration lights, d S, UK distribution for design, systems blinds, windows’ state etc. heaters and fans use) 1 (Yamagu MUser Markov chain Office - Realistic 2 buildings’ chi, behavior presence/absenc combinations Shimoda, e; 4 working only; not based and states; on TUS; not Mizuno cogeneration; clear if whole 2003) costs and year or 1 day;

190

energy saving long absences strategies neglected 1 (Pfafferot MUser Monte Carlo; Office DE Considers Adiabatic t and behavior integrated passive cooling boundary Herkel ESP-r specifically conditions 2005) between rooms; data for 42 days 2 (Bourgeoi SUser Object-based; Office CA, I Self-contained No advanced s, behavior integrated simulation solar shading; Reinhart, (lights, ESP-r; module; fully lacks flexibility; and blinds, SHOCC expandable; deterministic Macdonal windows improves definition of d 2006) and Nicol’s and passive users equipment Reinhart’s use) models 2 (Zimmer MUser Agent-based; Office US First agent- Building/servic mann behavior validated; based e/control 2007) integrated application; models need BES satisfactory fit; refinement; whole office activity building patterns need validation; lacks generalization 2 (Tanimot MUser Agent-based; Househol JP Pioneers of o, behavior validated d TUS data use; Hagishim considers a, and electricity and Sagara DHW 2008b) 1 (Haldi MUser Logit analysis; Office CH Function of No reversal of and behavior integrated work plane, adaptive Robinson EnergyPlus outdoor behavior;

2008) illuminance and function of Tout

occupancy; and Tin only covers many activities; personal/enviro nmental adaptation 2 (Tabak M Activities Agent-based Office NL Many activities; Very building and de MC realistic events; specific input; Vries whole office lacks 2010) building generalization 1 (Widén M Activities Markov chain Househol S Balanced Only covers and non- d complexity/out electricity Wäckelgå homogeneous put quality; demand; rd 2010) MC; validated considers limited TU various data household types 2 (Azar MUser Agent-based; Universit US Includes change No extensive and behavior integrated y over time by description of Menassa eQuest considering used agent- 2010) word-of-mouth based model; similarity among scenarios 1 (Parys, SUser Markov chain Office B Holistic Built-in error Saelens, behavior MC; approach; from choice of and Hens integrated improves drivers (not 2011) TRNSYS 6 Bourgeois et exhaustive); al.’s model not validated; modest change

191

in energy consumption due to OB (in contrast with other studies) 2 (Robinso SUser Agent-based CH Basis for future Huge amount n, Wilke, behavior simulation at of info needed; and Haldi various scales not clear if 2011) (from building integrated; not to urban) clear if fully developed 1 (Yamagu MUser Markov chain; Househol JP Considers Model chi, behavior modified d weekday, underestimates Tanaka, Tanimoto’s Saturday, changes in and approach holiday; behavior; Shimoda (Roulette electricity unclear 2012) Selection) consumption; integration 27 behaviors; BES compare different modeling approaches 2 (Wilke et M Activities + Logit analysis Househol F Assigns No al. 2013) occupancy multinomial; d duration at the simultaneous Markov chain start of new activities; no higher-order occupancy electrical/water pre-process; state; activities appliances Weibull function of distribution week day, 17 survival socio- analysis; demographic validated variables 2 (Lee and MUser Agent-based Office US Considers 1 agent; no Malkawi behavior optimized validation; no 2014) behaviors and calibration sensitivity to different climate conditions 2 (Chapma SUser Agent-based; Office UK Comparison No validation; n, behavior integrated and with not yet DHW, Siebers, EnergyPlus Househol deterministic appliances, and d simulation HVAC Robinson (window model setpoints, 2014) most impact on interactions difference); among agents coherent, general, extensible 2 (Rysanek SUser Object-based; - - Ease of use Stand-alone and behavior not validated; comparable/bet software (not Choudhar integrated ter than clear influence y 2015) TRNSYS, deterministic of EnergyPlus; approach; environment); tool straight-forward time step 1h; DELORES no relation lights/occupant s; no clustering holiday periods; lighting method not established nor validated

192

1 (Gunay SUser Logit analysis; Office CA Overcome Probability of et al. behavior integrated conceptual adaptive 2014) EnergyPlus problem that behavior always occupant non-zero actions are discrete events 2 (Langevin SUser Agent-based; Offce US Considers Interaction , Wen, behavior integrated energy and between EP and EnergyPlus thermal simulation and Gurian with BCVTB; comfort; MATLAB 2014) HABIT multiple zones, behavior model offices takes place at every time-step; multiple runs needed (probabilistic nature) 1 (Schweike MUser Multivariate Office DE Considers Lab r and behavior logistic interactions environment Wagner regression and among (comparison 2016) linear mixed occupants; with field effects models highlights studies are differences necessary); between single subject sample and multi- not person office; representative comparison (students) with Langevin and Haldi and Robinson models 1 (Haldi et MUser Markov chain Office CH, Accounts for High al. 2017) behavior with and DE, inter-individual complexity; generalized Househol DK diversity with calibration linear mixed d regard to needs further effects models windows, strengthening shading devices and lighting operation; clear equations and coefficients

193

Appendix C: Mathematical formulation of Mann-Whitney U test

The Mann-Whitney U test (also called the Mann–Whitney–Wilcoxon (MWW), Wilcoxon ranksum test, or Wilcoxon–Mann–Whitney test) is a nonparametric test of the null hypothesis that it is equally likely that a randomly selected value from one sample will be less than or greater than a randomly selected value from a second sample. In practice, the test is used to assess whether two independent groups are significantly different from each other. In this research, the difference among the two groups concern the assumption made for a specific OB aspect. The output of all models characterized by a given OB aspect modeled according to Pattern A (see Chapter 5) is compared with the output of all models characterized by the same OB aspect modeled according to Pattern B. The test is often performed as a two-sided test, which means that the research hypothesis indicates that the populations are not equal, as opposed to specifying directionality. A one-sided research hypothesis is used for detecting a positive or negative shift in one population as compared to the other.

Unlike its parametric equivalent, the t-test, the Mann-Whitney test does not make an assumption about the distribution of a population (i.e. that the sample came from a normally distributed population). Instead, the Mann-Whitney U test is characterized by the following assumptions:

― The sample drawn from the population is random; ― Independence within the samples and mutual independence is assumed. This means that an observation is in one group or the other (it cannot be in both); ― Ordinal measurement scale is assumed.

To test the null hypothesis, a U statistic is obtained and then converted to an equivalent p value.

The U statistic is the smaller of U1 and U2 calculated as follows

(C.1)

(C.2) where

U1 = Mann-Whitney U test statistic sample 1

U2 = Mann-Whitney U test statistic sample 2

194

n1 = sample size 1

n2 = sample size 2

R1 = Sum of the ranks for group 1

R2 = Sum of the ranks for group 2

Once the value of U is known, statistical tables (see Table C.1) should be used to find the probability of observing a value of U or lower. If the test is one-sided, this probability equals to the p-value; if the test is a two-sided test, the p-value is the double of this probability.

If the number of observation is such that n1n2 is large enough (> 20), the sampling distribution of the U statistic approaches a normal distribution. Hence, the U statistic will be accompanied by a z value (normal distribution variate value), calculated by means of formula 5A.2

(C.3)

Table C.1: Critical values of the Mann-Whitney U (two-tailed testing) up to n1=20 and n2=20 for 95% and 99% confidence levels (α=0.05 and α=0.01, respectively)

195

In this research, the Mann-Whitney U test is implemented by means of the ranksum function of Matlab. p = ranksum(x,y) returns the p-value of a two-sided Wilcoxon rank sum test. ranksum tests the null hypothesis that data in x and y are samples from continuous distributions with equal medians, against the alternative that they are not, at the default 5% significance level. The p-value of the test, returned as a positive scalar from 0 to 1, is the probability of observing a test statistic as or more extreme than the observed value under the null hypothesis.

196

Appendix D: Mann-Whitney U test for heating energy and WOH

Sensitivity analysis of heating energy to occupant behavior The sensitivity analysis of heating energy use to occupant behavior for building variants 1 – 16 shows that all variants are sensitive to heating setpoint and HVAC use. Building variants 1 and 8 are sensitive to occupant’s presence. Building variant 1 (low WWR, high thermal insulation, high PD) is sensitive to equipment use. Variants 1 – 3, 5, 7 and 9 – 15 are sensitive to light use, confirming the hypothesis that the energy consumption of variants with low thermal insulation and low PD is less affected by internal gains. Building variants 13 – 15, characterized by high WWR in Rome, are affected by cooling setpoint. Window use is non-influential for heating energy in all buildings but variant 13, located in Rome with high WWR, well-insulated envelope and high PD. Building variants 4, 7 and 8 (all characterized by high SHGC) are sensitive to blind use (see Fig. D.1).

Fig. D.1: Sensitivity of heating energy use to various occupant behavior aspects for all building variants

197

Sensitivity analysis of weighted overheating hours (WOH) to occupant behavior Only building variants 7, 8 and 9 to 16 were within the scope of the sensitivity analysis, as for all other variants it was assumed that the influence of OB did not cause reaching the assumed threshold. For WOH, no building variant is sensitive to heating setpoint temperature or equipment use. Instead, all building variants are sensitive to HVAC use. Building 9, 10, 12, 14 and 16 are sensitive to occupants’ presence. Building variants 11 and 13 are sensitive to light use. The cooling temperature setpoint significantly affects the WOH only for building variants characterized by high thermal insulation in Rome, WWR=80%, while blind use is relevant for all variants with low thermal insulation. Building variants 7, 11, 12, 13, 15 and 16 are sensitive to window use when it comes to overheating hours (see Fig. D.2).

Fig. D.2: Sensitivity of weighted overheating hours (WOH) to various occupant behavior aspects for building variants 7 – 16

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Appendix E: Stochastic models implemented in Chapter 5

In Chapter 5, three stochastic models were implemented in the EnergyPlus model to represent the adaptive behavior of occupants in respect to light switching, window operation and blind operation. The models consist of parameters mimicking the behavioral tendency of occupants, mostly based on observations. Once the parameters representing the behavior models are generated, the probability of undertaking these behaviors in the next time-step (i.e. discrete- time Markov) or finding an adaptive state in the active position (i.e. Bernoulli) is computed during the simulation via a logistic function such as:

∑ |, or | = (E.1) ∑

where S is the adaptive state, t is the time step index, a and ,…, are the parameters of the models, and ,…, are the predictors of the models. As mentioned in Section 2.2, a random number [0,1] is then generated and compared with the probability of undertaking an action estimated by the model. If the estimated probability exceeds the random number, the action is undertaken. The predictors of the models are de facto output variables computed at each simulation time- step, and they are known as sensors in EMS language. The adaptive states (e.g., lights are on/off, occupants are present/absent) are the outcome of the model, and they are referred to as actuators. According to the actuators, the simulation is run and new output variables (or sensors) are produced at each simulation time step. Fig. E.1 illustrates the dependency between EMS sensors, OB models, and EMS actuators during the EnergyPlus simulation.

EMS Sensors

EnergyPlus OB Models simulation

EMS Actuators

Fig. E.1: Illustration of the data exchange enabling the implementation of OB models in EnergyPlus EMS at each simulation time step

199

Reinhart’s Lightswitch-2002 model (Reinhart 2004)

The Lightswitch-2002 model uses as input measured or simulated 5-min annual profiles of occupant presence and workplane illuminances due to daylight. Fig. E.2 shows the basic functioning of the algorithm. This model improved on the Hunt model (Hunt 1979) by considering different light switch behaviors depending on the occupancy status, i.e. at arrival or during intermediate presence. Fig. E.2 also shows how the occupants are expected to switch off the lights only upon departure. The model does not consider the possibility of switching off the lights during intermediate presence, even if the daylight thresholds would allow it.

Is the no space already yes occupied?

no no Are lights Are lights switched on? switched on? no yes no yes

no Does Does Does Does the occupant the occupant the occupant the occupant arrive? arrive? leave? leave?

yes yes no yes

Stochastic process: Stochastic process: Stochastic process: Stochastic process: switch on probability switch off probability intermediate switch switch off probability at arrival at arrival on probability at departure

yes

Is P > R? no Is P > R? Is P > R? no Is P > R?

yes yes yes yes

Switch lights on Switch lights off Switch lights on Switch lights off

Fig. E.2: Lightswitch model by Reinhart as implemented in this research

The EMS code used to run this stochastic model in EnergyPlus is reported below. The stochastic processes and their integration in the BPS deterministic model are highlighted through comments.

EnergyManagementSystem:Program, LightSwitchOnReinhart, !- Name

200

if arr_event == 1, !- Program Line 1 % <--Arrival event set handle=A2+B2*daylight, !- Program Line 2 % <--Model parameters (A2; B2) and predictor/sensor (daylight) set handle=@Exp handle, !- A4 set handle=handle/(handle+1), !- A5 % <--Logistic function set R=@RandomUniform 0 1,!- A6 % <--Random number generation for stochastic model implementation if handle>R, !- A7 % <--Comparison of logistic function with random number set Light_EMS=1, !- A8 % <--Actuator endif, !- A9 endif, !- A10 if Num_People>0 && daylight<240, !- A11 % <--During intermediate occupancy set handle=A3+B3*daylight, !- A12 set handle=@Exp handle, !- A13 set handle=handle/(handle+1), !- A14 set R=@RandomUniform 0 1,!- A15 if handle>R, !- A16 set Light_EMS=1, !- A17 % <--Actuator endif, !- A18 endif; !- A19

EnergyManagementSystem:Program, LightSwitchOffReinhart, !- Name if CurrentTime<17, !- Program Line 1 set handle=A4+B4*C2, !- Program Line 2 else, !- A4 set handle=A4+B4*12*60, !- A5 endif, !- A6 set handle=@Exp handle, !- A7 set handle=handle/(handle+1), !- A8 % <--Logistic function set R=@RandomUniform 0 1,!- A9 % <--Random number generation for stochastic model implementation if dpt_event==1, !- A10 % <--Departure event if handle>R, !- A11 % <--Comparison of logistic function with random number set Light_EMS=0, !- A12 % <--Actuator endif, !- A13 endif; !- A14 set A2=@RandomNormal 1.6 0.1 1 2, !- A5 set B2=@RandomNormal 0-0.009 0.002 0-1 0, !- A42 set A3=@RandomNormal 0-3.9 0.1 0-5 0, !- A6 set B3=@RandomNormal 0-0.002 0.0005 0-1 0, !- A43 set A4=@RandomNormal 0-1.3 0.01 0-5 0, !- A7 set B4=@RandomNormal 0.006 0.0001 0 1, !- A44 set C2=@RandomNormal 1 0.25 0 1.5, !- A90

Haldi and Robinson window operation model (Haldi and Robinson 2009)

The window operation model proposed by Haldi and Robinson is a hybridized model, which includes a discrete-time Markov model for the prediction of openings and a Weibull distribution to predict opening durations. The action probability functions differ according to the occupancy status, i.e. if the occupant arrives, during intermediate presence, and if the occupant departs. The Haldi and Robinson’s window operation model uses as input 5-min resolution occupant presence status, indoor and outdoor temperature and rainfall. The main challenge is to dynamically predict the indoor temperature, resulting from previous actions on windows and the associated impact of their induced airflows. It is worth noting how this challenge is overcome by using an airflow model that resolves for wind and buoyancy pressures.

201

The model does not consider different window angles. Fig. E.3 shows the basic functioning of the algorithm.

What is the occupant presence status?

Arrival Intermediate Departure

Is the Is the Is the window open? window open? window open? yes no yes no yes no

Stochastic process: Stochastic process: Stochastic process: Stochastic process: Decrement opening window closing window opening Draw opening time window opening intermediate window time probability at probability at probability at arrival opening probability departure departure

yes no yes no no no Opening time Opening time Is P > R? Is P > R? Is P > R? Is P > R? < timestep? < timestep?

no yes no yes yes yes

Close window Open window Close window Open window Close window Open window

Fig. E.3: Window operation model by Haldi and Robinson as implemented according to (Gunay, O’Brien, and Beausoleil-Morrison 2016)

The EMS code used to run the Haldi and Robinson stochastic window operation model in EnergyPlus is reported below. The stochastic processes and their integration in the BPS deterministic model are highlighted through comments.

EnergyManagementSystem:Program, Window_Haldi_2009, !- Name if arr_event==1 && Tout>15, !- Program Line 1 % <--Arrival event and outdoor T threshold set handle=A15+B15*Tin+B18*Tout+B21*Rain, !- Program Line 2 % <--Model parameters (A15; B15; B18; B21) and predictors (indoor T; outdoor T; rainfall) set handle=@Exp handle, !- A4 set handle=handle/(handle+1), !- A5 % <--Logistic function set R=@RandomUniform 0 1,!- A6 % <--Random number generation for stochastic model implementation if handle>R, !- A7 % <--Comparison of logistic function with random number set Window=1, !- A8 % <--Actuator endif, !- A9 endif, !- A10 if Num_People>0, !- A11 % <--Window opening during intermediate occupant presence set handle=A16+B16*Tin+B19*Tout+B22*Rain, !- A12 set handle=@Exp handle, !- A13 set handle=handle/(handle+1), !- A14

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set R=@RandomUniform 0 1,!- A15 if handle>R, !- A16 set Window=1, !- A17 % <--Actuator endif, !- A18 endif, !- A19 if arr_event==1, !- A20 % <--Window closing at arrival set handle=A18+B24*Tin+B27*Tout, !- A21 set handle=@Exp handle, !- A22 set handle=handle/(handle+1), !- A23 set R=@RandomUniform 0 1,!- A24 if handle>R, !- A25 set Window=0, !- A26 % <--Actuator endif, !- A27 endif, !- A28 if dpt_event==1, !- A29 % <--Window closing at departure set handle=A37+B48*Tin+B49*Tout, !- A30 set handle=@Exp handle, !- A31 set handle=handle/(handle+1), !- A32 set R=@RandomUniform 0 1,!- A33 if handle>R, !- A34 set Window=0, !- A35 % <--Actuator endif, !- A36 endif, !- A37 if Num_People>0, !- A38 % <--Window closing during intermediate occupant presence set handle=A19+B25*Tin+B28*Tout, !- A39 set handle=@Exp handle, !- A40 set handle=handle/(handle+1), !- A41 set R=@RandomUniform 0 1,!- A42 if handle>R, !- A43 set Window=0, !- A44 % <--Actuator endif, !- A45 endif; !- A46 set A15=@RandomNormal 0-12 0.4 0-15 0, !- A18 set B15=@RandomNormal 0.31 0.02 0 1, !- A55 set B18=@RandomNormal 0.043 0.003 0 1, !- A58 set B21=@RandomNormal 0-0.45 0.11 0-2 0, !- A60 set A16=@RandomNormal 0-12.2 0.3 0-15 0, !- A19 set B16=@RandomNormal 0.28 0.01 0 1, !- A56 set B19=@RandomNormal 0.027 0.002 0 1, !- A59 set B22=@RandomNormal 0-0.34 0.08 0-2 0, !- A61 set A18=@RandomNormal 4 0.4 0 10, !- A21 set B24=@RandomNormal 0-0.29 0.02 0-1 0, !- A63 set B27=@RandomNormal 0-0.05 0.005 0-1 0, !- A66 set A37=@RandomNormal 0-1.520 0.051 0-5 0, !- A40 set B48=@RandomNormal 0.213 0.022 0-10 10, !- A87 set B49=@RandomNormal 0-0.091 0.061 0-10 10, !- A88 set A19=@RandomNormal 0-1.6 0.2 0-5 0, !- A22 set B25=@RandomNormal 0-0.05 0.01 0-1 0, !- A64 set B28=@RandomNormal 0-0.078 0.002 0-1 0, !- A67

Haldi and Robinson blind operation model (Haldi and Robinson 2010)

The model for blind operation prediction takes pre-processed occupant presence states as an input, as well as indoor and outdoor illuminance. This model should then be coupled with a daylight model. Blind operation is permitted at any occupied time step. However, the probability

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of raising blinds upon arrival is higher than during intermediate occupant presence. An important contribution of the Haldi and Robinson blind operation model is that the blinds are allowed to be partially open and/or closed, as opposed to previous models that considered fully raised/closed statuses only. Fig. E4 illustrates how the algorithm of this model works.

What is the occupant presence status?

Arrival Intermediate

Stochastic process: Stochastic process: blind lowering intermediate blind probability at arrival lowering probability

Stochastic process: Stochastic process: Is P > R? no blind raising Is P > R? intermediate blind probability at arrival raising probability

yes

Stochastic process: Stochastic process: probability of full Is P > R? probability of full Is P > R? blind lowering blind lowering

yes yes

Stochastic process: Stochastic process: probability of full probability of full blind raising blind raising

Is P > R? Is P > R? Is P > R? Is P > R?

yes no yes no yes no yes no

Lower blinds fully Draw shaded fraction Raise blinds fully Draw shaded fraction Lower blinds fully Draw shaded fraction Raise blinds fully Draw shaded fraction

Fig. E.4: Blind operation model by Haldi and Robinson as implemented according to (Gunay, O’Brien, and Beausoleil-Morrison 2016)

The EMS code used to run the Haldi and Robinson stochastic blind operation model in EnergyPlus is reported below. Also in this case, the stochastic processes and their integration in the BPS deterministic model are highlighted through comments.

EnergyManagementSystem:Program, Haldi_Blinds, !- Name set alpha=1.708, !- Program Line 1 set unshadedfrac=1-blinds, !- Program Line 2 if arr_event==1, !- A4 set handle=A26+B31*daylight+B32*unshadedfrac, !- A5 set handle=@Exp handle, !- A6 set R=@RandomUniform 0 1,!- A7 set handle=handle/(handle+1), !- A8 % <--Lowering probability at arrival

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if handle>R, !- A9 set handle=A28+B35*E_glob+B36*unshadedfrac, !- A10 set handle=@Exp handle, !- A11 set handle=handle/(handle+1), !- A12 % <--Probability to perform the action up to a full shaded fraction set R=@RandomUniform 0 1,!- A13 if handle>R, !- A14 set Blinds=1, !- A15 % <--Lower blinds completely (actuator) else, !- A16 set lambda=1.522*blinds-2.294, !- A17 set lambda=@Exp lambda, !- A18 set db = @RandomUniform 0 1, !- A19 set db = @ln (1-db), !- A20 set lambda = (lambda^alpha)*(0-1), !- A21 set db=db*lambda, !- A22 set db=db^(1/alpha), !- A23 set db=db*100, !- A24 set remainder=@Mod db 25,!- A25 set db=(db-remainder)/100, !- A26 set db=@max 0.25 db, !- A27 set blinds=@min blinds+db 1, !- A28 % <--Draw shaded fraction (actuator) endif, !- A29 endif, !- A30 endif, !- A31 if Num_People == 1, !- A32 % <--Same for intermediate occupant presence set handle=A27+B33*daylight+B34*unshadedfrac, !- A33 set handle=@Exp handle, !- A34 set handle=handle/(handle+1), !- A35 % <--Lowering probability at intermediate occupant presence set R=@RandomUniform 0 1,!- A36 if handle>R, !- A37 set handle=A28+B35*E_glob+B36*unshadedfrac, !- A38 set handle=@Exp handle, !- A39 set handle=handle/(handle+1), !- A40 % <--Probability to perform the action up to a full shaded fraction set R=@RandomUniform 0 1,!- A41 if handle>R, !- A42 set Blinds=1, !- A43 % <--Lower blinds completely (actuator) else, !- A44 set lambda=1.522*blinds-2.294, !- A45 set lambda=@Exp lambda, !- A46 set db = @RandomUniform 0 1, !- A47 set db = @ln (1-db), !- A48 set lambda = (lambda^alpha)*(0-1), !- A49 set db=db*lambda, !- A50 set db=db^(1/alpha), !- A51 set db=db*100, !- A52 set remainder=@Mod db 25,!- A53 set db=(db-remainder)/100, !- A54 set db=@max 0.25 db, !- A55 set blinds=@min blinds+db 1, !- A56 % <--Draw shaded fraction (actuator) endif, !- A57 endif, !- A58 endif, !- A59 if arr_event==1, !- A60 set handle=A29+B37*daylight+B38*unshadedfrac, !- A61 set handle=@Exp handle, !- A62 set R=@RandomUniform 0 1,!- A63 set handle=handle/(handle+1), !- A64 % <--Raising probability at arrival if handle>R, !- A65 set handle=A31+B41*E_glob+B42*unshadedfrac, !- A66 set handle=@Exp handle, !- A67 set handle=handle/(handle+1), !- A68 % <--Probability to perform the action up to a full unshaded fraction set R=@RandomUniform 0 1,!- A69 if handle>R, !- A70 set Blinds=0, !- A71 % <--Raise blinds completely (actuator) else, !- A72 set db = @RandomUniform 0 1, !- A73 set db=db*100, !- A74 set remainder=@Mod db 25,!- A75

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set db=(db-remainder)/100, !- A76 set blinds=@max blinds-db 0, !- A77 % <--Draw shaded fraction (actuator) endif, !- A78 endif, !- A79 endif, !- A80 if Num_People == 1, !- A81 % <--Same for intermediate occupant presence set handle=A30+B39*daylight+B40*unshadedfrac, !- A82 set handle=@Exp handle, !- A83 set handle=handle/(handle+1), !- A84 % <--Raising probability at intermediate occupant presence set R=@RandomUniform 0 1,!- A85 if handle>R, !- A86 set handle=A31+B41*E_glob+B42*unshadedfrac, !- A87 set handle=@Exp handle, !- A88 set handle=handle/(handle+1), !- A89 % <--Probability to perform the action up to a full unshaded fraction set R=@RandomUniform 0 1,!- A90 if handle>R, !- A91 set Blinds=0, !- A92 % <--Raise blinds completely (actuator) else, !- A93 set db = @RandomUniform 0 1, !- A94 set db=db*100, !- A95 set remainder=@Mod db 25,!- A96 set db=(db-remainder)/100, !- A97 set blinds=@max blinds-db 0, !- A98 % <--Draw shaded fraction (actuator) endif, !- A99 endif, !- A100 endif, !- A101 if Blinds==0, !- A102 set BottomShade=0, !- A103 % <--Actuator set MidBottomShade=0, !- A104 % <--Actuator set MidTopShade=0, !- A105 % <--Actuator set TopShade=0, !- A106 % <--Actuator elseif Blinds==0.25, !- A107 set BottomShade=0, !- A108 % <--Actuator set MidBottomShade=0, !- A109 % <--Actuator set MidTopShade=0, !- A110 % <--Actuator set TopShade=1, !- A111 % <--Actuator elseif Blinds==0.5, !- A112 set BottomShade=0, !- A113 % <--Actuator set MidBottomShade=0, !- A114 % <--Actuator set MidTopShade=1, !- A115 % <--Actuator set TopShade=1, !- A116 % <--Actuator elseif Blinds==0.75, !- A117 set BottomShade=0, !- A118 % <--Actuator set MidBottomShade=1, !- A119 % <--Actuator set MidTopShade=1, !- A120 % <--Actuator set TopShade=1, !- A121 % <--Actuator elseif Blinds==1, !- A122 set BottomShade=1, !- A123 % <--Actuator set MidBottomShade=1, !- A124 % <--Actuator set MidTopShade=1, !- A125 % <--Actuator set TopShade=1, !- A126 % <--Actuator endif; !- A127

set A26=@RandomNormal 0-7.4 0.16 0-100 0, !- A29 set A27=@RandomNormal 0-8 0.1 0-100 0, !- A30 set A28=@RandomNormal 0-0.27 0.14 0-100 0, !- A31 set A29=@RandomNormal 0-1.520 0.1 0-100 0, !- A32 set A30=@RandomNormal 0-3.625 0.03 0-100 0, !- A33 set A31=@RandomNormal 0.435 0.062 0 100, !- A34 set B31=@RandomNormal 0.001 0.00001 0 100, !- A70 set B32=@RandomNormal 2.17 0.16 0 100, !- A71 set B33=@RandomNormal 0.0008 0.00001 0 100, !- A72 set B34=@RandomNormal 1.27 0.086 0 100, !- A73 set B35=@RandomNormal 0.00000091 0.00000133 0 1, !- A74 set B36=@RandomNormal 0-2.23 0.16 0-100 100, !- A75 set B37=@RandomNormal 0-0.000654 0.00005 0-1 0, !- A76 set B38=@RandomNormal 0-3.139 0.07 0-100 0, !- A77 set B39=@RandomNormal 0-0.000276 0.00002 0-1 0, !- A78

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set B40=@RandomNormal 0-2.683 0.04 0-100 0, !- A79 set B41=@RandomNormal 0-0.0000235 0.0000011 0-1 0, !- A80 set B42=@RandomNormal 1.95 0.11 0 5, !- A81

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Appendix F: Method to determine the appropriate number of runs nRuns for stochastic models

The appropriate number of runs for stochastic models has been extensively investigated in (Feng, Yan, and Wang 2016). Below, an example of how mean and standard deviation are used to derive the appropriate number of runs is given. As an example, a widespread model for blind operation (Haldi and Robinson 2010) is applied to two high/low patterns combinations for two selected buildings (Variant 1 and Variant 16 investigated in Chapter 5). The high/low patterns combinations are supposed to be very different, such as they are representative. In particular, Combination 1 is characterized by low equipment and light use, low occupant presence, Tsp, heating = 18 °C, Tsp, cooling = 22 °C, non- operable windows and low HVAC system operation hours. Instead, Combination 96 is characterized by high equipment and light use, high occupant presence, Tsp, heating = 23 °C, Tsp, cooling = 26 °C, operable windows and long HVAC system operation hours. Each combination is run in order to obtain 5 sets of 20 runs each. The standard deviations are then analyzed.

(a) (b) (c) (d)

Fig. F.1: Standard deviation at corresponding number of runs for Variant 1, Combination 1 (a), Variant 1, Combination 96 (b), Variant 16, Combination 1 (c), Variant 16, Combination 96 (d)

As expected, the standard deviations in cooling energy for Variant 1 are not influenced by the number of runs of the blind operation stochastic model, as the performance indicator is not influenced by this aspect of occupant behavior (Fig. F.1). Running the model 10 times seems to be sufficient for this case (Fig. F.1a-b). When it comes to Variant 16, running the stochastic model 20 times shows no sign of convergence in the standard deviation (Fig. F.1c-d). Variant 16, Combination 96 is taken as a case study for a deeper analysis of mean and standard deviation. Fig. F.2 shows the influence of the number of runs on the mean value and standard deviation of the cooling energy for building Variant 16, Combination 96. To obtain these results, the

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stochastic model is run 1000 times, leading to 5 sets of 200 runs each. While there exists a clear convergence for the mean value after 90 runs (Fig. F.2a), the standard deviation shows no clear convergence also for 200 runs (Fig. F.2b). However, whether the noise is significant or not depends on the desired accuracy and consequent deviation tolerance (for example, deviation tolerance of ±10% from the average). For the case under investigation, the absolute variations of the values of mean and standard deviation variations are very small, which leads to the conclusion that 50 runs could be enough to give a representative distribution. Depending on the

desired accuracy, the number of runs changes. Fig. F.3 shows the variations in the distributions by means of boxplots at increasing number of runs for all 4 analyzed cases. It is clear how for Variant 1, where the cooling energy is hardly influenced by blind operation, running the stochastic model once would be sufficient. Conversely, Variant 16 may require a higher number of runs, depending on the desired accuracy. (a) (b)

Fig. F.2: Influence of nRuns on mean (a) and standard deviation (b) for building Variant 16, Combination 96

(a) (b) (c) (d)

Fig. F.3: Influence of n runs on performance indicator distribution for Variant 1, Combination 1 (a), Variant 1, Combination 96 (b), Variant 16, Combination 1 (c), Variant 16, Combination 96 (d)

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Fig. F.3 also shows how running a stochastic model an inappropriate number of times may lead to a loss of information.

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Appendix G: Thermophysical properties variations considered for the uncertainty analysis in Chapter 6

Table G.1: Thermophysical properties variation (s = thickness [m], k = thermal conductivity [W/mK], 3 D = density [kg/m ], cp = specific heat capacity [J/kgK], μ = mean value, σ = standard deviation)

External Wall (LOW TI)

3 Material s [m] k [W/mK] D [kg/m ] cp [J/kgK]

μ 0.005 50 7800 480 Steel σ 0.0005 0.75 25.74 19.2 Fibre quilt μ 0.046 0.04 12 840 σ 0.0046 0.0032 1.08 56.28 Concrete block μ 0.2 1.41 1900 1000 σ 0.02 0.1269 28.5 106

External Wall (HIGH TI)

3 Material s [m] k [W/mK] D [kg/m ] cp [J/kgK]

μ 0.005 50 7800 480 Steel σ 0.0005 0.75 25.74 19.2 Fibre quilt μ 0.174 0.04 12 840 σ 0.0174 0.0032 1.08 56.28 Concrete block μ 0.2 1.41 1900 1000 σ 0.02 0.1269 28.5 106

Internal Wall

3 Material s [m] k [W/mK] D [kg/m ] cp [J/kgK]

Gypsum μ 0.019 0.16 800 1090 σ 0.0019 0.024 25 163.5 Air cavity μ 0.15 σ 0.01 Gypsum μ 0.19 0.16 800 1090 σ 0.019 0.24 25 163.5

Internal Floor

3 Material s [m] k [W/mK] D [kg/m ] cp [J/kgK]

Acoustic tiles μ 0.0191 0.06 368 590 σ 0.00191 0.0198 64.4 63.42 Air cavity μ 0.18 σ 0.012

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Lightweight concrete μ 0.1016 0.53 1280 840 σ 0.01016 0.0477 192 89.04

Window (LOW TI) U-value g-value Visible transmittance μ 3 0.73 0.75 σ 0.150 0.073 0.075

Window (HIGH TI) U-value g-value Visible transmittance μ 1.1 0.29 0.48 σ 0.055 0.029 0.048

Air tightness Air Changes per Hour (ACH) μ 0.12 σ 0.04

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Appendix H: Thermophysical properties of the Task 56 reference building

Table H.1: Boundary conditions of the numerical model of the reference office zone

Facade building assembly – glazing g-value [-] 0.59 U-value (glass) [W/m2K] 1.40 U-value (frame) [W/m2K] 1.18 Frame thickness [m] 0.11 Ventilation Fresh-air supply [m2/h/pers] 40 Specific fan power [Wh/m3] 0.55 Infiltration ACH [l/h] 0.15 Specific fan power [Wh/m3] 0.55

Table H.2: Construction assembly of internal walls

3 Material s [m] k [W/mK] D [kg/m ] cp [kJ/kgK] Plasterboard 0.024 0.160 950 0.84 Mineral wool 0.080 0.038 80 0.84 Plasterboard 0.024 0.160 950 0.84

Table H.3: Construction assembly of floor/ceiling

3 Material s [m] k [W/mK] D [kg/m ] cp [kJ/kgK] Carpet 0.005 0.060 200 1.30 Screed 0.120 0.080 350 0.40 Concrete 0.350 2.100 2500 0.84

Table H.4: Construction assembly of external walls

3 Material s [m] k [W/mK] D [kg/m ] cp [kJ/kgK] Aluminum 0.003 200 2700 0.86 Mineral wool var. 0.038 80 0.84 Aluminum 0.003 200 2700 0.86

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Table H.5: Optical characteristics of indoor surfaces (valid for the overall solar spectrum)

Material Reflectance [-] Absorptance [-] Emissivity [-] Plasterboard 0.707 0.293 0.900 Mineral wool 0.384 0.616 0.900 Plasterboard 0.850 0.150 0.900

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Appendix I: EMS code for modeling the innovative solar shading controls investigated in Section 7.1

In this section, the EMS code relative to the EnergyManagementSystem:Program of EnergyPlus is given for each control strategy investigated in Section 7.1. Where possible, a short explanation of the code is given as comment. A number of variables, corresponding to output variables computed by EnergyPlus, are used throughout the code. Table I.1 illustrates the variables’ names and the corresponding EnergyPlus output variable.

Table I.1: Relevant distributions of logistic regressions with link function and typical applications

EMS Variable Corresponding EnergyPlus Output Variable IrradianceHorizontal Surface Outside Face Incident Solar Radiation Rate per Area (ceiling) IrradianceVerticalSouth Surface Outside Face Incident Solar Radiation Rate per Area (windowframe) SunAngleDeg Surface Window Solar Horizontal Profile Angle

SetpointHeating Zone Thermostat Heating Setpoint Temperature

SetpointCooling Zone Thermostat Cooling Setpoint Temperature

TempIndoor Zone Air Temperature

TempOutdoor Site Outdoor Air Drybulb Temperature Azimuth Site Solar Azimuth Angle

The reader is reminded that all variables should be first initialized and called through EnergyManagementSystem:ProgramCallingManager. For more information about the EnergyPlus EMS, please refer to (U.S. Department of Energy (DOE) 2015).

Control Strategy #1

Two positions available (fully up or fully down); irradiance sensor on the roof; lowering threshold 300 W/m2.

EnergyManagementSystem:Program, ControlStrategy1_HorizontalThreshold_FullyUpDown, !- Name set PreviousBlinds=Blinds, !- Program Line 1

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if DayOfWeek == 2 || DayOfWeek == 3 || DayOfWeek == 4 || DayOfWeek == 5 || DayOfWeek == 6, !- Program Line 2 if DelayMode == 0 && IrradianceHorizontal >= 300 && Blinds <> 1, !- A4 % <--Lowering threshold set Blinds=1, !- A5 set BlindLoweringAuto=BlindLoweringAuto+1, !- A6 set BlindLoweringAutoExact=BlindLoweringAutoExact+1, !- A7 elseif DelayMode == 0 && IrradianceHorizontal < 275 && Blinds <> 0, !- A8 % <--Raising threshold set Blinds=0, !- A9 set BlindRaisingAuto=BlindRaisingAuto+1, !- A10 set BlindRaisingAutoExact=BlindRaisingAutoExact+1, !- A11 endif, !- A12 endif, !- A13 set BlindHeightDifferenceAuto=0-(Blinds-PreviousBlinds); !- A14

Control Strategy #2

Two positions available (fully up or fully down); irradiance sensor on the façade; lowering threshold 200 W/m2.

EnergyManagementSystem:Program, ControlStrategy2_VerticalThreshold_FullyUpDown, !- Name set PreviousBlinds=Blinds, !- Program Line 1 if DayOfWeek == 2 || DayOfWeek == 3 || DayOfWeek == 4 || DayOfWeek == 5 || DayOfWeek == 6, !- Program Line 2 if DelayMode == 0 && IrradianceVerticalSouth >= 200 && Blinds <> 1, !- A4 % <--Lowering threshold set Blinds=1, !- A5 set BlindLoweringAuto=BlindLoweringAuto+1, !- A6 set BlindLoweringAutoExact=BlindLoweringAutoExact+1, !- A7 elseif DelayMode == 0 && IrradianceVerticalSouth < 175 && Blinds <> 0, !- A8 % <--Raising threshold set Blinds=0, !- A9 set BlindRaisingAuto=BlindRaisingAuto+1, !- A10 set BlindRaisingAutoExact=BlindRaisingAutoExact+1, !- A11 endif, !- A12 endif, !- A13 set BlindHeightDifferenceAuto=0-(Blinds-PreviousBlinds); !- A14

Control Strategy #3

Two positions available (fully up or lowered to eye height); irradiance sensor on the façade; lowering threshold 200 W/m2.

EnergyManagementSystem:Program, ControlStrategy3_VerticalThreshold_ToEyeHeight, !- Name set PreviousBlinds=Blinds, !- Program Line 1 if DayOfWeek == 2 || DayOfWeek == 3 || DayOfWeek == 4 || DayOfWeek == 5 || DayOfWeek == 6, !- Program Line 2 if DelayMode == 0 && IrradianceVerticalSouth >= 200 && Blinds <> 0.75, !- A4 % <--Lowering threshold set Blinds=0.75, !- A5 % <--Eye height set BlindLoweringAuto=BlindLoweringAuto+1, !- A6 set BlindLoweringAutoExact=BlindLoweringAutoExact+1, !- A7 elseif DelayMode == 0 && IrradianceVerticalSouth < 175 && Blinds <> 0, !- A8 % <--Raising threshold set Blinds=0, !- A9 set BlindRaisingAuto=BlindRaisingAuto+1, !- A10 set BlindRaisingAutoExact=BlindRaisingAutoExact+1, !- A11 endif, !- A12

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endif, !- A13 set BlindHeightDifferenceAuto=0-(Blinds-PreviousBlinds); !- A14

Control Strategy #4

Twenty positions available (full height discretized in 20 identical parts); sun-tracking feature to avoid direct sunlight.

EnergyManagementSystem:Program, ControlStrategy4_GlareSafe, !- Name set PreviousBlinds=Blinds, !- Program Line 1 set StartShadingHeight=2890-(Blinds*2000), !- Program Line 2 if DayOfWeek == 2 || DayOfWeek == 3 || DayOfWeek == 4 || DayOfWeek == 5 || DayOfWeek == 6, !- A4 if Azimuth > 90 && Azimuth < 270, !- A5 set SunAngleRad=@DegToRad SunAngleDeg, !- A6 set GlareSafeHeight=DeskToWindowDistance*((@Sin SunAngleRad)/(@Cos SunAngleRad))+DeskHeight, !- A7 % <--Glare safe height calculation; DeskToWindowDistance previously defined as 750 mm; DeskHeight previously defined as 800 mm if GlareSafeHeight >= 2890, !- A8 % <--Glare threshold set Blinds=0, !- A9 elseif GlareSafeHeight <= 890, !- A10 set Blinds=1, !- A11 else, !- A12 set RelativeHeight=GlareSafeHeight-890, !- A13 set HeightFraction=1-(RelativeHeight/2000), !- A14 set HeightPercentage=HeightFraction*100, !- A15 set remainder=@Mod HeightPercentage 5, !- A16 set Blinds=(HeightPercentage+(5-remainder))/100, !- A17 endif, !- A18 else, !- A19 set Blinds=0, !- A20 endif, !- A21 else, !- A22 set GlareSafeHeight=StartShadingHeight, !- A23 endif, !- A24 if Blinds > PreviousBlinds, !- A25 set BlindLoweringAuto=BlindLoweringAuto+1, !- A26 elseif Blinds < PreviousBlinds, !- A27 set BlindRaisingAuto=BlindRaisingAuto+1, !- A28 endif, !- A29 if GlareSafeHeight < StartShadingHeight, !- A30 set BlindLoweringAutoExact=BlindLoweringAutoExact+1, !- A31 elseif GlareSafeHeight > StartShadingHeight, !- A32 set BlindRaisingAutoExact=BlindRaisingAutoExact+1, !- A33 endif, !- A34 set BlindHeightDifferenceAuto=0-(Blinds-PreviousBlinds); !- A35

Control Strategy #5

Same as Strategy 4 but includes an energy mode when occupants are absent.

EnergyManagementSystem:Program, ControlStrategy5_GlareSafe_EnergyMode, !- Name set PreviousBlinds=Blinds, !- Program Line 1 set StartShadingHeight=2890-(Blinds*2000), !- Program Line 2

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if NumberOfPeople == 0, !- A4 % <--When occupants are absent if EMDelay < EMDelayTime,!- A5 % <--EMDelayTime previously defined as 15 min set EnergyMode=0, !- A6 set ComfortMode=1, !- A7 set EMDelay=EMDelay+5, !- A8 elseif EMDelay == EMDelayTime, !- A9 set EnergyMode=1, !- A10 set ComfortMode=0, !- A11 endif, !- A12 elseif NumberOfPeople > 0, !- A13 set EnergyMode=0, !- A14 set ComfortMode=1, !- A15 set EMDelay=0, !- A16 endif, !- A17 if EnergyMode == 1, !- A18 % <--EnergyMode set SetpointAverage=(SetpointHeating+SetpointCooling)/2, !- A19 set HeatFlowInwards=(TempOutdoor-TempIndoor)*Uvalue, !- A20 % <--Simplified formula to determine whether heating is needed; Uvalue previously defined as 1.493 W/m2K if (IrradianceVerticalSouth + HeatFlowInwards) > 0, !- A21 set HeatingPotential=1, !- A22 elseif (IrradianceVerticalSouth + HeatFlowInwards) <= 0, !- A23 set HeatingPotential=0, !- A24 endif, !- A25 if TempIndoor < SetpointAverage, !- A26 if HeatingPotential == 1,!- A27 set Blinds=0, !- A28 elseif HeatingPotential == 0, !- A29 set Blinds=1, !- A30 endif, !- A31 elseif TempIndoor > SetpointAverage, !- A32 if HeatingPotential == 1,!- A33 set Blinds=1, !- A34 elseif HeatingPotential == 0, !- A35 set Blinds=0, !- A36 endif, !- A37 endif, !- A38 endif, !- A39 if ComfortMode == 1, !- A40 % <--ComfortMode if Azimuth > 90 && Azimuth < 270, !- A41 set SunAngleRad=@DegToRad SunAngleDeg, !- A42 set GlareSafeHeight=DeskToWindowDistance*((@Sin SunAngleRad)/(@Cos SunAngleRad))+DeskHeight, !- A43 if GlareSafeHeight >= 2890, !- A44 set Blinds=0, !- A45 elseif GlareSafeHeight <= 890, !- A46 set Blinds=1, !- A47 else, !- A48 set RelativeHeight=GlareSafeHeight-890, !- A49 set HeightFraction=1-(RelativeHeight/2000), !- A50 set HeightPercentage=HeightFraction*100, !- A51 set remainder=@Mod HeightPercentage 5, !- A52 set Blinds=(HeightPercentage+(5-remainder))/100, !- A53 endif, !- A54 else, !- A55 set Blinds=0, !- A56 endif, !- A57 else, !- A58 set GlareSafeHeight=StartShadingHeight, !- A59 endif, !- A60 if Blinds > PreviousBlinds, !- A61 set BlindLoweringAuto=BlindLoweringAuto+1, !- A62 elseif Blinds < PreviousBlinds, !- A63 set BlindRaisingAuto=BlindRaisingAuto+1, !- A64 endif, !- A65 if GlareSafeHeight < StartShadingHeight, !- A66 set BlindLoweringAutoExact=BlindLoweringAutoExact+1, !- A67 elseif GlareSafeHeight > StartShadingHeight, !- A68 set BlindRaisingAutoExact=BlindRaisingAutoExact+1, !- A69 endif, !- A70 set BlindHeightDifferenceAuto=0-(Blinds-PreviousBlinds); !- A71

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Control Strategy #6

Same as Strategy 5 but includes overcast sky feature, whereby if the room is occupied and the vertical solar irradiance on the façade is below 120 W/m2, the shades are fully raised.

EnergyManagementSystem:Program, ControlStrategy6_GlareSafe_EnergyMode_OvercastSkyDetection, !- Name set PreviousBlinds=Blinds, !- Program Line 1 set StartShadingHeight=2890-(Blinds*2000), !- Program Line 2 if NumberOfPeople == 0, !- A4 if EMDelay < EMDelayTime,!- A5 set EnergyMode=0, !- A6 set ComfortMode=1, !- A7 set EMDelay=EMDelay+5, !- A8 elseif EMDelay == EMDelayTime, !- A9 set EnergyMode=1, !- A10 set ComfortMode=0, !- A11 endif, !- A12 elseif NumberOfPeople > 0, !- A13 set EnergyMode=0, !- A14 set ComfortMode=1, !- A15 set EMDelay=0, !- A16 endif, !- A17 if EnergyMode == 1, !- A18 set SetpointAverage=(SetpointHeating+SetpointCooling)/2, !- A19 set HeatFlowInwards=(TempOutdoor-TempIndoor)*Uvalue, !- A20 if (IrradianceVerticalSouth + HeatFlowInwards) > 0, !- A21 set HeatingPotential=1, !- A22 elseif (IrradianceVerticalSouth + HeatFlowInwards) <= 0, !- A23 set HeatingPotential=0, !- A24 endif, !- A25 if TempIndoor < SetpointAverage, !- A26 if HeatingPotential == 1,!- A27 set Blinds=0, !- A28 elseif HeatingPotential == 0, !- A29 set Blinds=1, !- A30 endif, !- A31 elseif TempIndoor > SetpointAverage, !- A32 if HeatingPotential == 1,!- A33 set Blinds=1, !- A34 elseif HeatingPotential == 0, !- A35 set Blinds=0, !- A36 endif, !- A37 endif, !- A38 endif, !- A39 if ComfortMode == 1, !- A40 if Azimuth > 90 && Azimuth < 270, !- A41 if IrradianceVerticalSouth >= OvercastThreshold, !- A42 % <--Overcast threshold previously set as 120 W/m2 set SunAngleRad=@DegToRad SunAngleDeg, !- A43 set GlareSafeHeight=DeskToWindowDistance*((@Sin SunAngleRad)/(@Cos SunAngleRad))+DeskHeight, !- A44 if GlareSafeHeight >= 2890, !- A45 set Blinds=0, !- A46 elseif GlareSafeHeight <= 890, !- A47 set Blinds=1, !- A48 else, !- A49 set RelativeHeight=GlareSafeHeight-890, !- A50 set HeightFraction=1-(RelativeHeight/2000), !- A51 set HeightPercentage=HeightFraction*100, !- A52 set remainder=@Mod HeightPercentage 5, !- A53 set Blinds=(HeightPercentage+(5-remainder))/100, !- A54 endif, !- A55 elseif IrradianceVerticalSouth < OvercastThreshold, !- A56 set Blinds=0, !- A57

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endif, !- A58 else, !- A59 set Blinds=0, !- A60 endif, !- A61 else, !- A62 set GlareSafeHeight=StartShadingHeight, !- A63 endif, !- A64 if Blinds > PreviousBlinds, !- A65 set BlindLoweringAuto=BlindLoweringAuto+1, !- A66 elseif Blinds < PreviousBlinds, !- A67 set BlindRaisingAuto=BlindRaisingAuto+1, !- A68 endif, !- A69 if GlareSafeHeight < StartShadingHeight, !- A70 set BlindLoweringAutoExact=BlindLoweringAutoExact+1, !- A71 elseif GlareSafeHeight > StartShadingHeight, !- A72 set BlindRaisingAutoExact=BlindRaisingAutoExact+1, !- A73 endif, !- A74 set BlindHeightDifferenceAuto=0-(Blinds-PreviousBlinds); !- A75

AlwaysOpen and AlwaysClosed

EnergyManagementSystem:Program, AlwaysOpen, !- Name set Blinds=0; !- Program Line 1

EnergyManagementSystem:Program, AlwaysClosed, !- Name set Blinds=1; !- Program Line 1

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Appendix J: Example of occupancy status obtained using Page et al. occupant presence model

Fig. J.1: Example of occupancy status using the stochastic model of Page et al. employed in Section 7.1. Results for a single run during the first week of January

221

References

Aerts, D., J. Minnen, I. Glorieux, I. Wouters, and F. Descamps. 2014. “A Method for the Identification and Modelling of Realistic Domestic Occupancy Sequences for Building Energy Demand Simulations and Peer Comparison.” Building and Environment 75. Elsevier Ltd: 67–78. doi:10.1016/j.buildenv.2014.01.021.

Aghemo, C, L Blaso, and A Pellegrino. 2014. “Automation in Construction Building Automation and Control Systems: A Case Study to Evaluate the Energy and Environmental Performances of a Lighting Control System in Offices.” Automation in Construction 43. Elsevier B.V.: 10–22. doi:10.1016/j.autcon.2014.02.015.

Ahn, Ki Uhn, Deuk Woo Kim, Cheol Soo Park, and Pieter de Wilde. 2017. “Predictability of Occupant Presence and Performance Gap in Building Energy Simulation.” Applied Energy 208. Elsevier Ltd: 1639–1652. doi:10.1016/j.apenergy.2017.04.083.

Ahn, Ki Uhn, and Cheol Soo Park. 2016. “Correlation between Occupants and Energy Consumption.” Energy and Buildings 116. Elsevier B.V.: 420–433. doi:10.1016/j.enbuild.2016.01.010.

Alfakara, Alaa, and Ben Croxford. 2014. “Using Agent-Based Modelling to Simulate Occupants’ Behaviours in Response to Summer Overheating.” In Symposium on Simulation for Architecture and Urban Design. Tampa, Florida, USA.

Alonso, William. 1968. “Predicting Best with Imperfect Data.” Journal of the American Planning Association 34 (4): 248–255. doi:10.1080/01944366808977813.

Amecke, Hermann, Jeff Deason, Andrew Hobbs, Aleksandra Novikova, Yang Xiu, and Zhang Shengyuan. 2013. Buildings Energy Efficiency in China, Germany, and the United States. http://climatepolicyinitiative.org/wp-content/uploads/2013/04/Buildings- Energy-Efficiency-in-China-Germany-and-the-United-States.pdf.

Andersen, P D, A Iversen, H Madsen, and C Rode. 2014. “Dynamic Modeling of Presence of Occupants Using Inhomogeneous Markov Chains.” Energy and Buildings 69. Elsevier B.V.: 213–223.

Andersen, Rune, Valentina Fabi, Jorn Toftum, Stefano P. Corgnati, and Bjarne W. Olesen. 2013. “Window Opening Behaviour Modelled from Measurements in Danish Dwellings.” Building and Environment 69. Elsevier Ltd: 101–113. doi:10.1016/j.buildenv.2013.07.005.

Andersen, Rune Korsholm. 2012. “The Influence of Occupants’ Behaviour on Energy Consumption Investigated in 290 Identical Dwellings and in 35 Apartments.” In , 18–20.

Andrews, Clinton J., Maryann Soresen Allacci, Jennifer A. Senick, and Ioanna Tsoulou. 2016. “Using Synthetic Population Data for Prospective Modeling of Occupant Behavior

222

during Design.” Energy and Buildings 126. doi:10.1016/j.enbuild.2016.05.049.

Antoni, D M, P Bonato, F Ochs, M Magni, De Vries S, R C G M Loonen, and R Fedrizzi. 2018. “Definition of a Reference Office Building for Simulation Based Evaluation of Solar Envelope Systems.” In EuroSun 2018 - 12th International Conference on Solar Energy for Buildings and Industry. Rapperswil (Switzerland).

Armstrong, Marianne M., Mike C. Swinton, Hajo Ribberink, Ian Beausoleil-Morrison, and Jocelyn Millette. 2009. “Synthetically Derived Profiles for Representing Occupant- Driven Electric Loads in Canadian Housing.” Journal of Building Performance Simulation 2 (January 2015): 15–30. doi:10.1080/19401490802706653.

ASHRAE. 2013. ANSI/ASHRAE/IESNA Standard 90.1-2013: Energy Standard for Buildings except for Low-Rise Residential Buildings.

Astrup, Rasmus, K. David Coates, and Erin Hall. 2008. “Finding the Appropriate Level of Complexity for a Simulation Model: An Example with a Forest Growth Model.” Forest Ecology and Management 256: 1659–1665. doi:10.1016/j.foreco.2008.07.016.

Azar, Elie, and Carol C. Menassa. 2010. “A Conceptual Framework to Energy Estimation in Buildings Using Agent Based Modeling.” In Proceedings of the 2010 Winter Simulation Conference, edited by B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yuecesan, 3145–3156.

Azar, Elie, and Carol C. Menassa. 2012. “A Comprehensive Analysis of the Impact of Occupancy Parameters in Energy Simulation of Office Buildings.” Energy and Buildings 55. Elsevier B.V.: 841–853. doi:10.1016/j.enbuild.2012.10.002.

Balci, Osman. 1994. “Validation, Verification, and Testing Techniques throughout the Life Cycle of a Simulation Study.” Annals of Operations Research 53: 121–173.

Barthelmes, Verena M., Yeonsook Heo, Valentina Fabi, and Stefano P. Corgnati. 2017. “Exploration of the Bayesian Network Framework for Modelling Window Control Behaviour.” Building and Environment 126 (October). Elsevier: 318–330. doi:10.1016/j.buildenv.2017.10.011.

Beck, W., D. Dolmans, G. Dutoo, A. Hall, and O. Seppanen. 2010. Solar Shading: How to Integrate Solar Shading in Sustainable Buildings, REHVA Guidebook 12.

Boerstra, A.C., J. van Hoof, and A.M. van Weele. 2015. “A New Hybrid Thermal Comfort Guideline for the Netherlands: Background and Development.” Architectural Science Review 58 (1): 24–34. doi:10.1080/00038628.2014.971702.

Bourgeois, Denis, Christoph Reinhart, and Iain Macdonald. 2006. “Adding Advanced Behavioural Models in Whole Building Energy Simulation: A Study on the Total Energy Impact of Manual and Automated Lighting Control.” Energy and Buildings 38: 814–823. doi:10.1016/j.enbuild.2006.03.002.

Brooks, R. J., and A. M. Tobias. 1996. “Choosing the Best Model: Level of Detail,

223

Complexity, and Model Performance.” Mathematical and Computer Modelling 24 (4): 1– 14. doi:10.1016/0895-7177(96)00103-3.

Bullinger, Hans Jörg, Wilhelm Bauer, Günter Wenzel, and Roland Blach. 2010. “Towards User Centred Design (UCD) in Architecture Based on Immersive Virtual Environments.” Computers in Industry 61 (4): 372–379. doi:10.1016/j.compind.2009.12.003.

Burge, P. S. 2004. “Sick Building Syndrome.” Occupational and Environmental Medicine 61 (2): 185–190. doi:10.1136/oem.2003.008813.

Buso, Tiziana, Simona D’Oca, and Stefano Paolo Corgnati. 2014. “The Influence of Realistic Schedules for the Use of Appliances on the Total Energy Performances in Dwellings,” 1– 18.

BWT. 2015. “Bouwregels van 1965-1992 in Kaart.” www.bwtinfo.nl/nieuws/2015/4/bouwregels-van-1965-1992-in-kaart.

Calì, Davide, Rune Korsholm, Dirk Müller, and Bjarne W Olesen. 2016. “Analysis of Occupants’ Behavior Related to the Use of Windows in German Households.” Building and Environment 103. Elsevier Ltd: 54–69. doi:10.1016/j.buildenv.2016.03.024.

Cao, Xiaodong, Xilei Dai, and Junjie Liu. 2016. “Building Energy-Consumption Status Worldwide and the State-of-the-Art Technologies for Zero-Energy Buildings during the Past Decade.” Energy and Buildings 128. Elsevier B.V.: 198–213. doi:10.1016/j.enbuild.2016.06.089.

Capasso, A., W. Grattieri, R. Lamedica, and A. Prudenzi. 1994. “Bottom-up Approach to Residential Load Modeling.” IEEE Transactions on Power Systems 9 (2): 957–964. doi:10.1109/59.317650.

Chang, Wen Kuei, and Tianzhen Hong. 2013. “Statistical Analysis and Modeling of Occupancy Patterns in Open-Plan Offices Using Measured Lighting-Switch Data.” Building Simulation 6: 23–32. doi:10.1007/s12273-013-0106-y.

Chapman, Jacob, Peer-olaf Siebers, and Darren Robinson. 2014. “Coupling Multi-Agent Stochastic Simulation of Occupants with Building Simulation.” In Building Simulation and Optimization Conference (BSO14). London, UK.

Chvatal, Karin Maria Soares, and Helena Corvacho. 2009. “The Impact of Increasing the Building Envelope Insulation upon the Risk of Overheating in Summer and an Increased Energy Consumption.” Journal of Building Performance Simulation 2 (4): 267–282. doi:10.1080/19401490903095865.

Chwif, L., M.R.P. Barretto, and R.J. Paul. 2000. “On Simulation Model Complexity.” In 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165), 449–455. doi:10.1109/WSC.2000.899751.

CIBSE. 2013. The Limits of Thermal Comfort: Avoiding Overheating in European Buildings. CIBSE TM52: 2013. Edited by Bonnie Craig. Norwich, Norfolk NR6 6SA: Page Bros.

224

(Norwich) Ltd.

CIBSE. 2015. “Building Performance Modelling.” London, UK: CIBSE Publications.

CIBSE, CODE. 1994. “Code for Interior Lighting.” London: The Chartered Institute of Building Services Engineers.

Clevenger, Caroline M., and John R. Haymaker. 2006. “The Impact of the Building Occupation on Energy Modeling Simulations.” In Joint International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada, 1–10.

Clevenger, Caroline M., John R. Haymaker, and Maral Jalili. 2014. “Demonstrating the Impact of the Occupant on Building Performance.” Journal of Computing in Civil Engineering 28 (February): 99–102. doi:10.1061/(ASCE)CP.1943-5487.0000323.

Cole, Raymond J., and Zosia Brown. 2009. “Reconciling Human and Automated Intelligence in the Provision of Occupant Comfort.” Intelligent Buildings International 1 (1): 39–55. doi:10.3763/inbi.2009.0007.

Cowie, Andrew, Tianzhen Hong, Xiaohang Feng, and Quentin Darakdjian. 2017. “Usefulness of the ObFMU Module Examined through a Review of Occupant Modelling Functionality in Building Performance Simulation Programs University of Strathclyde , Glasgow , UK Lawrence Berkeley National Laboratory , Berkeley CA , USA Tsinghua University.” In Proceedings of the 2017 Building Simulation Conference, 1880–1889. San Francisco.

Crawley, Drury B., Linda K. Lawrie, Frederick C. Winkelmann, W.F. Buhl, Y.Joe Huang, Curtis O. Pedersen, Richard K. Strand, et al. 2001. “EnergyPlus: Creating a New- Generation Building Energy Simulation Program.” Energy and Buildings 33 (4): 319– 331. doi:10.1016/S0378-7788(00)00114-6.

D’Oca, Simona, Valentina Fabi, Stefano P. Corgnati, and Rune Korsholm Andersen. 2014. “Effect of Thermostat and Window Opening Occupant Behavior Models on Energy Use in Homes.” Building Simulation 7: 683–694. doi:10.1007/s12273-014-0191-6.

D’Oca, Simona, and Tianzhen Hong. 2014. “A Data-Mining Approach to Discover Patterns of Window Opening and Closing Behavior in Offices.” Building and Environment 82. Elsevier Ltd: 726–739. doi:10.1016/j.buildenv.2014.10.021.

Davis, James a., and Darin W. Nutter. 2010. “Occupancy Diversity Factors for Common University Building Types.” Energy and Buildings 42 (9). Elsevier B.V.: 1543–1551. doi:10.1016/j.enbuild.2010.03.025. de Dear, R, and G S Brager. 1998. “Developing an Adaptive Model of Thermal Comfort and Preference.” ASHRAE Transactions 104.

Degelman, Larry O. 1999. “A Model for Simulation of Daylighting and Occupancy Sensors as an Energy Control Strategy for Office Buildings.” In Proceedings of Building Simulation

225

99, 571–578. Kyoto, Japan.

Deru, Michael, Kristin Field, Daniel Studer, Kyle Benne, Brent Griffith, Paul Torcellini, Bing Liu, et al. 2011. U.S. Department of Energy Commercial Reference Building Models of the National Building Stock. NREL/TP-5500-46861. doi:NREL Report No. TP-5500- 46861.

Djunaedy, E., J.L.M. Hensen, and M.G.L.C. Loomans. 2003. “Toward External Coupling of Building Energy and Airflow Modelling Programs.” In ASHRAE Transactions, 771–787.

Dong, B, and K P Lam. 2011. “Building Energy and Comfort Management through Occupant Behaviour Pattern Detection Based on a Large-Scale Environmental Sensor Network.” Journal of Building Performance Simulation 4 (4): 359–369.

Duarte, Carlos, Kevin Van Den Wymelenberg, and Craig Rieger. 2013. “Revealing Occupancy Patterns in an Office Building through the Use of Occupancy Sensor Data.” Energy and Buildings 67. Elsevier B.V.: 587–595. doi:10.1016/j.enbuild.2013.08.062.

EnergyPlus. 2019. EnergyPlus Engineering Reference: The Reference to EnergyPlus Calculations.

Erickson, Varick L., Yiqing Lin, Ankur Kamthe, Rohini Brahme, Amit Surana, Alberto E. Cerpa, Michael D. Sohn, and Satish Narayanan. 2009. “Energy Efficient Building Environment Control Strategies Using Real-Time Occupancy Measurements.” ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, 19. doi:10.1145/1810279.1810284.

Fabi, Valentina, Rune Vinther Andersen, Stefano Corgnati, and Bjarne W. Olesen. 2012. “Occupants’ Window Opening Behaviour: A Literature Review of Factors Influencing Occupant Behaviour and Models.” Building and Environment 58. Elsevier Ltd: 188–198. doi:10.1016/j.buildenv.2012.07.009.

Fabi, Valentina, V Maggiora, Sp Corgnati, and Rune Andersen. 2014. “Occupants ’ Behaviour in Office Building : Stochastic Models for Window.” 8th Windsor Conference: Counting the Cost of Comfort in a Changing World Cumberland Lodge, Windsor, UK, 10-13 April 2014. London: Network for Comfort and Energy Use in Buildings, no. April: 10.

Fanger, P.O. 1970. Thermal Comfort. Copenhagen, Denmark: Danish Technical Press.

Feng, Xiaohang, Da Yan, and Tianzhen Hong. 2015. “Simulation of Occupancy in Buildings.” Energy & Buildings 87. Elsevier B.V.: 348–359. doi:10.1016/j.enbuild.2014.11.067.

Feng, Xiaohang, Da Yan, and Chuang Wang. 2016. “On the Simulation Repetition and Temporal Discretization of Stochastic Occupant Behaviour Models in Building Performance Simulation.” Journal of Building Performance Simulation 0 (0). Taylor & Francis: 1–13. doi:10.1080/19401493.2016.1236838.

Fritsch, R., A. Kohler, M. Nygard-Ferguson, and J. L. Scartezzini. 1990. “A Stochastic Model of User Behaviour Regarding Ventilation.” Building and Environment 25 (2): 173–181.

226

doi:10.1016/0360-1323(90)90030-U.

Gaceo, Sergio Cantos, Félix Iglesias Vázquez, and Jordina Vidal Moreno. 2009. “Comparison of Standard and Case-Based User Profiles in Building’s Energy Performance Simulation.” Eleventh International IBPSA Conference, 584–590.

Gaetani, Isabella, Pieter-Jan Hoes, and Jan L. M. Hensen. 2016a. “Occupant Behavior in Building Energy Simulation: Towards a Fit-for-Purpose Modeling Strategy.” Energy and Buildings 121. Elsevier B.V.: 188–204. doi:http://dx.doi.org/10.1016/j.enbuild.2016.03.038.

Gaetani, Isabella, Pieter-Jan Hoes, and Jan L.M. Hensen. 2016b. “On the Sensitivity to Different Aspects of Occupant Behaviour for Selecting the Appropriate Modelling Complexity in Building Performance Predictions.” Journal of Building Performance Simulation. Taylor & Francis, 1–11. doi:10.1080/19401493.2016.1260159.

Gaetani, Isabella, Pieter-Jan Hoes, and Jan L.M. Hensen. 2017. “Introducing and Testing a Strategy for Fit-for-Purpose Occupant Behavior Modeling in a Simulation-Aided Building Design Process.” In Building Simulation. San Francisco.

Gaetani, Isabella, Pieter-Jan Hoes, and Jan L.M. Hensen. 2018. “Estimating the Influence of Occupant Behavior on Building Heating and Cooling Energy in One Simulation Run.” Applied Energy 223 (August). Elsevier: 159–171. doi:10.1016/j.apenergy.2018.03.108.

Galasiu, A D, G R Newsham, C Suvagau, and D M Sander. 2007. “Energy Saving Lighting Control Systems for Open-Plan Offices: A Field Study.” Leukos 4 (1): 7–29.

Galasiu, A D, and J A Veitch. 2006. “Occupant Preferences and Satisfaction with the Luminous Environment and Control Systems in Daylit Offices: A Literature Review.” Energy and Buildings 38: 728–742.

Gaunt, Louisa, and Ann-Margaret Berggren. 1983. Brukarvanor Och Energiforbrukning (Occupant Habits and Energy Use).

Ghahramani, Ali, Kenan Zhang, Kanu Dutta, Zheng Yang, and Burcin Becerik-Gerber. 2016. “Energy Savings from Temperature Setpoints and Deadband: Quantifying the Influence of Building and System Properties on Savings.” Applied Energy 165. Elsevier Ltd: 930– 942. doi:10.1016/j.apenergy.2015.12.115.

Gilani, Sara, William O’Brien, H. Burak Gunay, and Juan Sebastián Carrizo. 2016. “Use of Dynamic Occupant Behavior Models in the Building Design and Code Compliance Processes.” Energy and Buildings 117. Elsevier B.V.: 260–271. doi:10.1016/j.enbuild.2015.10.044.

Giskes, Bas. 2017. “The Influence of Occupant Behavior on the Total Energy Consumption in Offices: A Case Study for the ABT Office.” Eindhoven University of Technology. http://www.janhensen.nl/team/past/master/Giskes_2017.pdf.

Goia, Francesco. 2016. “Search for the Optimal Window-to-Wall Ratio in Office Buildings in

227

Different European Climates and the Implications on Total Energy Saving Potential.” Solar Energy 132: 467–492.

Gottwalt, Sebastian, Wolfgang Ketter, Carsten Block, John Collins, and Christof Weinhardt. 2011. “Demand Side Management — A Simulation of Household Behavior under Variable Prices.” Energy Policy 39 (12). Elsevier: 8163–8174. doi:10.1016/j.enpol.2011.10.016.

Gram-Hanssen, K. 2011. “Households{\textquoteright} Energy Use - Which Is the More Important: Efficient Technologies or User Practices?,” 992–999.

Guerra Santin, O, L Itard, and H Visscher. 2009. “The Effect of Occupancy and Building Characteristics on Energy Use for Space and Water Heating in Dutch Residential Stock.” Energy and Buildings 41 (11): 1223–1232.

Gunay, H. Burak, William O’Brien, and Ian Beausoleil-Morrison. 2013. “A Critical Review of Observation Studies, Modeling, and Simulation of Adaptive Occupant Behaviors in Offices.” Building and Environment 70. Elsevier Ltd: 31–47. doi:10.1016/j.buildenv.2013.07.020.

Gunay, H. Burak, William O’Brien, Ian Beausoleil-Morrison, Rhys Goldstein, Simon Breslav, and Azam Khan. 2014. “Coupling Stochastic Occupant Models to Building Performance Simulation Using the Discrete Event System Specification Formalism.” Journal of Building Performance Simulation 7 (January 2015): 1–22. doi:10.1080/19401493.2013.866695.

Gunay, H Burak, William O’Brien, and Ian Beausoleil-Morrison. 2016. “Implementation and Comparison of Existing Occupant Behaviour Models in EnergyPlus.” Journal of Building Performance Simulation 9 (6): 567–588. doi:10.1080/19401493.2015.1102969.

Gunay, H Burak, William O’Brien, Ian Beausoleil-morrison, and Jayson Bursill. 2018. “Development and Implementation of a Thermostat Learning Algorithm.” Science and Technology for the Built Environment 24 (1). Taylor & Francis: 43–56. doi:10.1080/23744731.2017.1328956.

Haldi, Frédéric. 2010. “Towards a Unified Model of Occupants’ Behaviour and Comfort for Building Energy Simulation.” Thesis. EPFL. doi:10.5075/epfl-thesis-4587.

Haldi, Frédéric. 2013. “A Probabilistic Model to Predict Building Occupants’ Diversity towards Their Interactions with the Building Envelope.” In 13th Conference of International Building Performance Simulation Association, 1475–1482.

Haldi, Frédéric, Davide Calì, Rune Korsholm Andersen, and Mark Wesseling. 2017. “Modelling Diversity in Building Occupant Behaviour: A Novel Statistical Approach.” Journal of Building Performance Simulation 10 (5–6): 527–544. doi:10.1080/19401493.2016.1269245.

Haldi, Frédéric, and Darren Robinson. 2008. “On the Behaviour and Adaptation of Office Occupants.” Building and Environment 43: 2163–2177. doi:10.1016/j.buildenv.2008.01.003.

228

Haldi, Frédéric, and Darren Robinson. 2009. “Interactions with Window Openings by Office Occupants.” Building and Environment 44 (12). Elsevier Ltd: 2378–2395. doi:10.1016/j.buildenv.2009.03.025.

Haldi, Frédéric, and Darren Robinson. 2010. “Adaptive Actions on Shading Devices in Response to Local Visual Stimuli.” Journal of Building Performance Simulation 3: 135– 153. doi:10.1080/19401490903580759.

Hensen, Jan L M, and Roberto Lamberts. 2011. Building Performance Simulation for Design and Operation. London/New York: Spon Press.

Herkel, Sebastian, Ulla Knapp, and Jens Pfafferott. 2008. “Towards a Model of User Behaviour Regarding the Manual Control of Windows in Office Buildings.” Building and Environment 43: 588–600. doi:10.1016/j.buildenv.2006.06.031.

Hong, Tianzhen, Yixing Chen, and Zsofia Belafi. 2018. “Occupant Behavior Models : A Critical Review of Implementation and Representation Approaches in Building Performance Simulation Programs,” 1–14.

Hong, Tianzhen, Simona D’Oca, William J.N. Turner, and Sarah C. Taylor-Lange. 2015. “An Ontology to Represent Energy-Related Occupant Behavior in Buildings Part I: Introduction to the DNAs Framework.” Building and Environment. doi:10.1016/j.buildenv.2015.02.019.

Hong, Tianzhen, Jared Langevin, and Kaiyu Sun. 2018. “Building Simulation: Ten Challenges.” Building Simulation 11 (5): 871–898. doi:10.1007/s12273-018-0444-x.

Hong, Tianzhen, and Hung-wen Lin. 2012a. “Occupant Behavior: Impact on Energy Use of Private Offices.” Asim IBSPA Asia Conference, no. January.

Hong, Tianzhen, and Hung-Wen Lin. 2012b. “Occupant Behavior: Impact on Energy Use of Private Offices.” In ASim 2012 - 1st Asia Conference of International Building Performance Simulation Association, edited by LBNL. Shanghai, China. https://escholarship.org/uc/item/6jp5w8kn.

Hopkinson, R. G. 1972. “Glare from Daylighting in Buildings.” Applied Ergonomics 3.4 (December): 206–215. doi:https://doi.org/10.1016/0003-6870(72)90102-0.

Hoyt, Tyler, Edward Arens, and Hui Zhang. 2014. “Extending Air Temperature Setpoints: Simulated Energy Savings and Design Considerations for New and Retrofit Buildings.” Building and Environment 88. Elsevier Ltd: 89–96. doi:10.1016/j.buildenv.2014.09.010.

Hunt, D.R.G. 1979. “The Use of Artificial Lighting in Relation to Daylight Levels and Occupancy.” Building and Environment 14 (c): 21–33. doi:10.1016/0360-1323(79)90025- 8.

IEA. 2017. Tracking Clean Energy Progress 2017. Energy Technology Perspectives 2017 Excerpt. doi:10.1787/energy_tech-2014-en.

“IEA EBC Annex 79.” 2019. Accessed January 15. http://annex79.iea-ebc.org/.

229

International Organization for Standardization. 2005. “ISO 13790: Thermal Performance of Buildings — Calculation of Energy Use for Space Heating and Cooling.” International Organization, 106.

Jia, Ruoxi, and Costas Spanos. 2017. “Occupancy Modelling in Shared Spaces of Buildings: A Queueing Approach.” Journal of Building Performance Simulation 10 (4): 406–421. doi:10.1080/19401493.2016.1267802.

Jordan, Ulrike, and Klaus Vajen. 2001. “Influence of the DHW Load Profile on the Fractional Energy Savings: A Case Study of a Solar COMBI-System with TRNSYS Simulation.” Solar Energy 69 (prEN 12977): 197–208.

Kazmi, H., S. D’Oca, C. Delmastro, S. Lodeweyckx, and S. P. Corgnati. 2016. “Generalizable Occupant-Driven Optimization Model for Domestic Hot Water Production in NZEB.” Applied Energy 175. Elsevier Ltd: 1–15. doi:10.1016/j.apenergy.2016.04.108.

Kellner, Marc I, Raymond J Madachy, and David M Ra. 1999. “Software Process Simulation Modeling: Why? What? How?” The Journal of Systems and Software 46: 91–105.

Kingma, Boris, and Wouter van Marken Lichtenbelt. 2015. “Energy Consumption in Buildings and Female Thermal Demand.” Nature Climate Change 5 (August). Nature Publishing Group: 1054. http://dx.doi.org/10.1038/nclimate2741.

Langevin, Jared, Jin Wen, and Patrick L. Gurian. 2014. “Including Occupants in Building Performance Simulation: Integration of an Agent-Based Occupant Behavior Algorithm with Energyplus.” In 2014 ASHRAE/IBPSA-USA. Atlanta, GA.

Larson, Greg Ward, and Rob Shakespeare. 1998. Rendering with Radiance: The Art and Science of Lighting Visualization. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

Leaman, Adrian, and Bill Bordass. 1999. “Productivity in Buildings: The ‘killer’ Variables.” Building Research and Information 27 (1): 4–19. doi:10.1080/096132199369615.

Lee, Yoon Soo, and Ali M. Malkawi. 2014. “Simulating Multiple Occupant Behaviors in Buildings: An Agent-Based Modeling Approach.” Energy and Buildings 69. Elsevier B.V.: 407–416. doi:10.1016/j.enbuild.2013.11.020.

Li, Cheng, Tianzhen Hong, and Da Yan. 2014. “An Insight into Actual Energy Use and Its Drivers in High-Performance Buildings.” Applied Energy 131: 394–410.

Li, N, J Li, R Fan, and H Jia. 2015. “Probability of Occupant Operation of Windows during Transition Seasons in Office Buildings.” Renewable Energy 73: 84–91.

Liao, Chenda, Yashen Lin, and Prabir Barooah. 2012. “Agent-Based and Graphical Modelling of Building Occupancy.” Journal of Building Performance Simulation 5 (December 2014): 5–25. doi:10.1080/19401493.2010.531143.

Lin, Hung Wen, and Tianzhen Hong. 2013. “On Variations of Space-Heating Energy Use in Office Buildings.” Applied Energy 111: 515–528. doi:10.1016/j.apenergy.2013.05.040.

230

Lindner, Andreas J M, Sumee Park, and Matthias Mitterhofer. 2017. “Determination of Requirements on Occupant Behavior Models for the Use in Building Performance Simulations,” 861–874.

Loonen, Roel C G M, Fabio Favoino, Jan L M Hensen, and Mauro Overend. 2017. “Review of Current Status, Requirements and Opportunities for Building Performance Simulation of Adaptive Facades.” Journal of Building Performance Simulation 10 (2): 205–223. doi:10.1080/19401493.2016.1152303.

Lutzenhiser, Loren. 1993. “Social and Behavioral Aspects of Energy Use.” Annual Review of Energy and the Environment 18: 247–289.

Macdonald, I.A., J.A. Clarke, and P.A. Strachan. 1999. “Assessing Uncertainty in Building Simulation.” In Proceedings of Building Simulation 99, 683–695. Kyoto, Japan: IBPSA conference.

Mahdavi, A, A Mohammadi, E Kabir, and L Lambeva. 2008. “Occupants’ Operation of Lighting and Shading Systems in Office Buildings.” Journal of Building Performance Simulation 1 (1): 57–65.

Mahdavi, Ardeshir, and Farhang Tahmasebi. 2015. “Predicting People’s Presence in Buildings: An Empirically Based Model Performance Analysis.” Energy and Buildings 86. Elsevier B.V.: 349–355. doi:10.1016/j.enbuild.2014.10.027.

Mahdavi, Ardeshir, and Farhang Tahmasebi. 2016. “On the Quality Evaluation of Behavioural Models for Building Performance Applications.” Journal of Building Performance Simulation 0 (0). Taylor & Francis: 1–11. doi:10.1080/19401493.2016.1230148.

Mahdavi, Ardeshir, and Farhang Tahmasebi. 2017. “On the Quality Evaluation of Behavioural Models for Building Performance Applications.” Journal of Building Performance Simulation 10 (5–6): 554–564. doi:10.1080/19401493.2016.1230148.

Mahdavi, Ardeshir, Farhang Tahmasebi, and Mine Kayalar. 2016. “Prediction of Plug Loads in Office Buildings: Simplified and Probabilistic Methods.” Energy & Buildings 129. Elsevier B.V.: 322–329. doi:10.1016/j.enbuild.2016.08.022.

Maier, T., Marek Krzaczek, and J. Tejchman. 2009. “Comparison of Physical Performances of the Ventilation Systems in Low-Energy Residential Houses.” Energy and Buildings 41: 337–353. doi:10.1016/j.enbuild.2008.10.007.

McLeod, Robert S., Christina J. Hopfe, and Alan Kwan. 2013. “An Investigation into Future Performance and Overheating Risks in Passivhaus Dwellings.” Building and Environment 70. Elsevier Ltd: 189–209. doi:10.1016/j.buildenv.2013.08.024.

Mennen, Lenny. 2018. “Developing a Stepwise Approach for Including Occupant Behavior in Energy Performance Contracts.” Eindhoven University of Technology. http://www.janhensen.nl/team/past/master/Mennen_2018.pdf.

Morbitzer, Christoph Andreas. 2003. “Towards the Integration of Simulation into the Building

231

Design Process.” Regulation. University of Strathclyde.

Morley, Janine, and Mike Hazas. 2011. “The Significance of Difference : Understanding Variation in Household Energy Consumption,” 2037–2046.

Muroni, Antonio. 2017. “Occupant Behavior in Identical Residential Buildings.” Eindhoven University of Technology.

Newsham, G.R. 1994. “Manual Control of Window Blinds and Electric Lighting. Implications for Comfort and Energy Consumption.” Indoor and Built Environment 3: 135–144.

Newsham, G.R. 1995. “Lightswitch: A Stochastic Model for Predicting Office Lighting Energy Consumption.” In Proceedings of Right Light Three, 3rd European Conference on Energy Efficient Lighting, 59–66. Newcastle, UK.

Nicol, J. Fergus. 2001. “Characterising Occupant Behavior in Buildings: Towards a Stochastic Model of Occupant Use of Windows, Lights, Blinds Heaters and Fans.” In Seventh International IBPSA Conference, 1073–1078. Rio de Janeiro, Brazil.

Nicol, J F. 2001. “Characterising Occupant Behaviour in Buildings: Towards a Stochastic Model of Occupant Use of Windows, Lights, Blinds, Heaters and Fans.” In Proceedings of the Seventh International IBPSA Conference, Rio, 2:1073–1078.

Norford, Leslie, Robert H. Socolow, E S Hsieh, and G V Spadaro. 1994. “Two-to-One Discrepancy between Measured and Predicted Performance of a ‘Low-Energy’ Office Building: Insights from a Reconciliation Based on the DOE-2 Model.” Energy and Buildings 21 (2): 121–131.

O’Brien, W, K Kapsis, and A K Athienitis. 2013. “Manually-Operated Window Shade Patterns in Office Buildings: A Critical Review.” Building and Environment 60: 319– 338.

O’Brien, William, Isabella Gaetani, Salvatore Carlucci, Pieter Jan Hoes, and Jan L.M. Hensen. 2017. “On Occupant-Centric Building Performance Metrics.” Building and Environment 122. Elsevier Ltd: 373–385. doi:10.1016/j.buildenv.2017.06.028.

O’Brien, William, Isabella Gaetani, Sara Gilani, Salvatore Carlucci, Pieter-Jan Hoes, and Jan L.M. Hensen. 2016. “International Survey on Current Occupant Modelling Approaches in Building Performance Simulation.” Journal of Building Performance Simulation 1 (0). Taylor & Francis: 1–19. doi:10.1080/19401493.2016.1243731.

O’Brien, William, and H. Burak Gunay. 2014. “The Contextual Factors Contributing to Occupants’ Adaptive Comfort Behaviors in Offices – A Review and Proposed Modeling Framework.” Building and Environment 77: 77–87. doi:10.1016/j.buildenv.2014.03.024.

O’Brien, William, H. Burak Gunay, Farhang Tahmasebi, and Ardeshir Mahdavi. 2016. “A Preliminary Study of Representing the Inter-Occupant Diversity in Occupant Modelling.” Journal of Building Performance Simulation 0 (0). Taylor & Francis: 1–18. doi:10.1080/19401493.2016.1261943.

232

O’Neil, Dennis. 1998. “Human Biological Adaptability: Adapting to Climate Extremes.” https://www2.palomar.edu/anthro/adapt/adapt_2.htm.

Ochoa, Carlos E., Myriam B.C. Aries, and Jan L.M. Hensen. 2012. “State of the Art in Lighting Simulation for Building Science: A Literature Review.” Journal of Building Performance Simulation 5 (April 2015): 209–233. doi:10.1080/19401493.2011.558211.

Paatero, Jukka V., and Peter D. Lund. 2006. “A Model for Generating Household Electricity Load Profiles.” International Journal of Energy Research 30: 273–290. doi:10.1002/er.1136.

Page, J., D. Robinson, N. Morel, and J. L. Scartezzini. 2008. “A Generalised Stochastic Model for the Simulation of Occupant Presence.” Energy and Buildings 40: 83–98. doi:10.1016/j.enbuild.2007.01.018.

Page, J, D Robinson, N Morel, and J L Scartezzini. 2008. “A Generalised Stochastic Model for the Simulation of Occupant Presence.” Energy and Buildings 40 (2): 83–98.

Page, Jessen. 2007. “Simulating Occupant Presence and Behaviour in Buildings.” EPFL.

Parys, Wout, Dirk Saelens, and Hugo Hens. 2011. “Coupling of Dynamic Building Simulation with Stochastic Modelling of Occupant Behaviour in Offices – a Review-Based Integrated Methodology.” Journal of Building Performance Simulation 4 (December 2014): 339–358. doi:10.1080/19401493.2010.524711.

Parys, Wout, Bernard Souyri, and Monika Woloszyn. 2014. “Agent-Based Behavioural Models for Residential Buildings in Dynamic Building Simulation: State-of-the-Art and Integrated Model Assembly.” In IBPSA-FR 2014, 1–8.

Pfafferott, J., and S. Herkel. 2005. “Statistical Simulation of User Behaviour in Low-Energy Office Buildings.” In International Conference “Passive and Low Energy Cooling for the Built Environment,” 81:676–682. doi:10.1016/j.solener.2006.08.011.

Pidd, Michael. 1999. “Just Modeling Through: A Rough Guide to Modeling.” Interfaces 29 (April): 118–132.

Pitt, Mark a., and In Jae Myung. 2002. “When a Good Fit Can Be Bad.” Trends in Cognitive Sciences 6 (10): 421–425. doi:10.1016/S1364-6613(02)01964-2.

Polinder, Henk, Marcel Schweiker, Ad Van Der Aa, Karin Schakib-Ekbatan, Valentina Fabi, Rune Andersen, Naomi Morishita, et al. 2013. Final Report Annex 53 - Occupant Behavior and Modeling (Separate Document Volume II).

Ramponi, Rubina, Isabella Gaetani, and Adriana Angelotti. 2014. “Influence of the Urban Environment on the Effectiveness of Natural Night-Ventilation of an Office Building.” Energy and Buildings 78: 25–34.

Reinhart, C F, J Mardaljevic, and Z Rogers. 2006. “Dynamic Daylight Performance Metrics for Sustainable Building Design.” Leukos 3 (1): 7–31.

233

Reinhart, C, and K Voss. 2003. “Monitoring Manual Control of Electric Lighting and Blinds.” Lighting Research and Technology 35 (2003): 243–260. doi:10.1191/1365782803li064oa.

Reinhart, Christoph F. 2004. “Lightswitch-2002: A Model for Manual and Automated Control of Electric Lighting and Blinds.” Solar Energy 77: 15–28. doi:10.1016/j.solener.2004.04.003.

Rezaee, Roya, Jason Brown, Godfried Augenbroe, and Jinsol Kim. 2015. “Assessment of Uncertainty and Confidence in Building Design Exploration.” Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29 (04): 429–441. doi:10.1017/S0890060415000426.

Richardson, Ian, Murray Thomson, and David Infield. 2008. “A High-Resolution Domestic Building Occupancy Model for Energy Demand Simulations.” Energy and Buildings 40: 1560–1566. doi:10.1016/j.enbuild.2008.02.006.

Richardson, Ian, Murray Thomson, David Infield, and Conor Clifford. 2010. “Domestic Electricity Use: A High-Resolution Energy Demand Model.” Energy and Buildings 42 (10): 1878–1887.

Rijal, Hom B., P. Tuohy, J. Fergus Nicol, and Michael A. Humphreys. 2008. “Development of an Adaptive Window-Opening Algorithm to Predict the Thermal Comfort, Energy Use and Overheating in Buildings.” Journal of Building Performance Simulation 1: 17–30.

Robinson, Darren, Urs Wilke, and Frédéric Haldi. 2011. “Multi Agent Simulation of Occupants’Presence and Behaviour.” Proceedings of Building Simulation, 14–16. http://www.ibpsa.org/proceedings/BS2011/P_1671.pdf.

Robinson, Stewart. 2011. “Choosing the Right Model: Conceptual Modeling for Simulation.” In 2011 Winter Simulation Conference, 1428–1440.

Rysanek, Adam Martin, and Ruchi Choudhary. 2015. “DELORES - an Open-Source Tool for Stochastic Prediction of Occupant Services Demand.” Journal of Building Performance Simulation 8 (2): 97–118. doi:10.1080/19401493.2014.888595.

Salt, John D. 1993. “Keynote Address: Simulation Should Be Easy and Fun!” In Proceedings of the 1993 Winter Simulation Conference.

Sameni, Seyed M. T., Mark Gaterell, Azadeh Montazami, and Abdullahi Ahmed. 2015. “Overheating Investigation in UK Social Housing Flats Built to the Passivhaus Standard.” Building and Environment 92. Elsevier Ltd: 222–235. doi:10.1016/j.buildenv.2015.03.030.

Schweiker, Marcel, Frédéric Haldi, Masanori Shukuya, and Darren Robinson. 2012. “Verification of Stochastic Models of Window Opening Behaviour for Residential Buildings.” Journal of Building Performance Simulation 5 (January 2015): 55–74. doi:10.1080/19401493.2011.567422.

Schweiker, Marcel, and Andreas Wagner. 2016. “The Effect of Occupancy on Perceived

234

Control , Neutral Temperature , and Behavioral Patterns.” Energy & Buildings 117. Elsevier B.V.: 246–259. doi:10.1016/j.enbuild.2015.10.051.

Sonderegger, Robert C. 1978. “Movers and Stayers: The Resident’s Contribution to Variation across Houses in Energy Consumption for Space Heating.” Energy and Buildings 1 (3): 313–324.

Song, Yunshang. 2016. “Influence of Occupant Behavior on Building Energy Performance: A Case Study of Heijmans ONE Residence.” Eindhoven University of Technology.

Stokes, Melody, Mark Rylatt, and Kevin Lomas. 2004. “A Simple Model of Domestic Lighting Demand.” Energy and Buildings 36 (October 2003): 103–116. doi:10.1016/j.enbuild.2003.10.007.

Sun, Kaiyu, and Tianzhen Hong. 2016. “A Simulation Approach to Estimate Energy Savings Potential of Occupant Behavior Measures.” Energy and Buildings 136. Elsevier B.V.: (Article in press). doi:10.1016/j.enbuild.2016.12.010.

Tabak, Vincent, and Bauke de Vries. 2010. “Methods for the Prediction of Intermediate Activities by Office Occupants.” Building and Environment 45 (6). Elsevier Ltd: 1366– 1372. doi:10.1016/j.buildenv.2009.11.018.

Tahmasebi, Farhang, and Ardeshir Mahdavi. 2015. “The Sensitivity of Building Performance Simulation Results to the Choice of Occupants’ Presence Models: A Case Study.” Journal of Building Performance Simulation 1493 (December): 1–11. doi:10.1080/19401493.2015.1117528.

Tahmasebi, Farhang, and Ardeshir Mahdavi. 2016. “An Inquiry into the Reliability of Window Operation Models in Building Performance Simulation.” Building and Environment 105. Elsevier Ltd: 343–357. doi:10.1016/j.buildenv.2016.06.013.

Tahmasebi, Farhang, Sepideh Mostofi, and Ardeshir Mahdavi. 2015. “Exploring the Implications of Different Occupancy Modelling Approaches for Building Performance Simulation Results.” In Energy Procedia 78, 567–572. doi:10.1016/j.egypro.2015.11.737.

Tanimoto, Jun, Aya Hagishima, and Hiroki Sagara. 2008a. “Validation of Methodology for Utility Demand Prediction Considering Actual Variations in Inhabitant Behaviour Schedules.” Journal of Building Performance Simulation 1 (January 2015): 31–42. doi:10.1080/19401490701868471.

Tanimoto, Jun, Aya Hagishima, and Hiroki Sagara. 2008b. “Validation of Probabilistic Methodology for Generating Actual Inhabitants’ Behavior Schedules for Accurate Prediction of Maximum Energy Requirements.” Energy and Buildings 40: 316–322. doi:10.1016/j.enbuild.2007.02.032.

Trčka, Marija, and Jan L M Hensen. 2010. “Overview of HVAC System Simulation.” Automation in Construction 19: 93–99. doi:10.1016/j.autcon.2009.11.019.

U.S. Department of Energy (DOE). “Commercial Reference Buildings.”

235

http://energy.gov/eere/buildings/commercial-reference-buildings.

U.S. Department of Energy (DOE). 2015. Application Guide for EMS Energy Management System User Guide. EnergyPlus Documentation. https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v8.3.0/EMS_ Application_Guide.pd%5Cn%5Cnf. van der Aa, R. 2012. NEN 7120 + C2: Energy Performance of Buildings: Determination Method. Nederlands Normalisatie-insituut (NEN). van Dronkelaar, Chris, Mark Dowson, E. Burman, Catalina Spataru, and Dejan Mumovic. 2016. “A Review of the Energy Performance Gap and Its Underlying Causes in Non- Domestic Buildings.” Frontiers in Mechanical Engineering 1 (January): 1–14. doi:10.3389/fmech.2015.00017.

Van Eck, Randy. 2016. “Investigating the Influence of Occupants on the Indoor Climate and Heating Energy for the ‘Morgen Woningen.’” Eindhoven University of Technology.

Walkenhorst, Oliver, Joachim Luther, Christoph Reinhart, and Jens Timmer. 2002. “Dynamic Annual Daylight Simulations Based on One-Hour and One-Minute Means of Irradiance Data.” Solar Energy 72 (5): 385–395. doi:https://doi.org/10.1016/S0038-092X(02)00019- 1.

Wang, Chuang, Da Yan, and Yi Jiang. 2011. “A Novel Approach for Building Occupancy Simulation.” Building Simulation 4: 149–167. doi:10.1007/s12273-011-0044-5.

Wang, Chuang, Da Yan, Hongsan Sun, and Yi Jiang. 2016. “A Generalized Probabilistic Formula Relating Occupant Behavior to Environmental Conditions.” Building and Environment 95. Elsevier Ltd: 53–62. doi:10.1016/j.buildenv.2015.09.004.

Wang, Danni, Clifford C. Federspiel, and Francis Rubinstein. 2005. “Modeling Occupancy in Single Person Offices.” Energy and Buildings 37: 121–126. doi:10.1016/j.enbuild.2004.06.015.

Wang, Liping, Paul Mathew, and Xiufeng Pang. 2012. “Uncertainties in Energy Consumption Introduced by Building Operations and Weather for a Medium-Size Office Building.” Energy and Buildings 53 (September): 152–158.

Widén, Joakim, Annica M. Nilsson, and Ewa Wäckelgård. 2009. “A Combined Markov-Chain and Bottom-up Approach to Modelling of Domestic Lighting Demand.” Energy and Buildings 41: 1001–1012. doi:10.1016/j.enbuild.2009.05.002.

Widén, Joakim, and Ewa Wäckelgård. 2010. “A High-Resolution Stochastic Model of Domestic Activity Patterns and Electricity Demand.” Applied Energy 87 (6). Elsevier Ltd: 1880– 1892. doi:10.1016/j.apenergy.2009.11.006.

Wienold, J, and J Christoffersen. 2006. “Evaluation Methods and Development of a New Glare Prediction Model for Daylight Environments with the Use of CCD Cameras.” Energy and Buildings 38 (7): 743–757.

236

Wienold, Jan. 2009. “Daylight Glare in Offices.” Karlsruhe University of Technology (KTH).

Wilke, Urs, Frédéric Haldi, Jean Louis Scartezzini, and Darren Robinson. 2013. “A Bottom-up Stochastic Model to Predict Building Occupants’ Time-Dependent Activities.” Building and Environment 60. Elsevier Ltd: 254–264. doi:10.1016/j.buildenv.2012.10.021.

Yamaguchi, Yohei, Yoshiyuki Shimoda, and Minoru Mizuno. 2003. “Development of District Energy System Simulation Model Based on Detailed Energy Demand Model.” Proceeding of Eighth International IBPSA Conference, 1443–1450.

Yamaguchi, Yohei, Marcos Tanaka, and Yoshiyuki Shimoda. 2012. “Comparison of Occupant Behavior Models Applied to a Household.” In Proceedings of the Asim2012 - 1st Asia Conference of International Building Performance Simulation Association. Shanghai, China.

Yan, Da, and Tianzhen Hong. 2018. EBC Annex 66 Final Report - Definition and Simulation of Occupant Behavior in Buildings.

Yan, Da, Tianzhen Hong, Bing Dong, Ardeshir Mahdavi, Simona D’Oca, Isabella Gaetani, and Xiaohang Feng. 2017. “IEA EBC Annex 66: Definition and Simulation of Occupant Behavior in Buildings.” Energy & Buildings 156. Elsevier B.V.: 258–270. doi:10.1016/j.enbuild.2017.09.084.

Yilmaz, Selin, Steven K Firth, and David Allinson. 2017. “Occupant Behaviour Modelling in Domestic Buildings : The Case of Household Electrical Appliances” 1493. doi:10.1080/19401493.2017.1287775.

Yu, Tina. 2010. “Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings.” In 2010 Ninth International Conference on Machine Learning and Applications. doi:10.1109/ICMLA.2010.111.

Yun, G Y, P Tuohy, and K Steemers. 2009. “Thermal Performance of a Naturally Ventilated Building Using a Combined Algorithm of Probabilistic Occupant Behaviour and Deterministic Heat and Mass Balance Models.” Energy and Buildings 41: 489–499.

Yun, Geun Young, and Koen Steemers. 2008. “Time-Dependent Occupant Behaviour Models of Window Control in Summer.” Building and Environment 43: 1471–1482. doi:10.1016/j.buildenv.2007.08.001.

Zeigler, B.P., T.G. Kim, and H. Praehofer. 2000. Theory of Modelling and Simulation. 2nd ed. New York, New York, USA: Academic Press.

Zhang, Yufan, and Peter Barrett. 2012. “Factors Influencing the Occupants’ Window Opening Behaviour in a Naturally Ventilated Office Building.” Building and Environment 50: 125–134. doi:10.1016/j.buildenv.2011.10.018.

Zhou, Xin, Da Yan, Tianzhen Hong, and Xiaoxin Ren. 2015. “Data Analysis and Stochastic Modeling of Lighting Energy Use in Large Office Buildings in China.” Energy and Buildings 86. Elsevier B.V.: 275–287. doi:10.1016/j.enbuild.2014.09.071.

237

Zimmermann, Gerhard. 2007. “Modeling and Simulation of Individual User Behavior for Building Performance Predictions.” In Proceedings of the 2007 Summer Computer Simulation Conference, 913–920. San Diego, California.

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Curriculum Vitae

Isabella Gaetani (1988) holds a BSc in Interior Design and an MSc in Energy Engineering from Politecnico di Milano, Italy. She was a recipient of the Alta Scuola Politecnica scholarship, dedicated to the top 1% students. Prior to joining a PhD program at Eindhoven University of Technology (TU/e), she had work experiences in various practices related to research and the built environment, such as the online platform ResearchGate and the architecture practice Stefano Boeri Architetti. Her PhD research – carried out at the Unit Building Physics and Services at TU/e – focused on a strategy to enable the appropriate modelling of occupant behavior in building performance simulation. Isabella was an active member of IEA EBC Annex 66 “Definition and Simulation of Occupant Behavior in Buildings”. To date, she (co-)authored 7 ISI journal publications, 2 conference papers and a book chapter. She acts as a reviewer for a number of international scientific journals, including Journal of Building Performance Simulation, Energy and Buildings, Building and Environment, Applied Energy and Energy Efficiency. From May 2019, Isabella is a Senior Research Scientist at Arup, London.

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Acknowledgements

Everybody has won and all must have prizes. Lewis Carroll, Alice's Adventures in Wonderland

Some time ago I read a book that inspired me. Its perspective on life has stayed with me, as a confidant, a friend, and at times a hero in my daily life. The small book I am thinking of was titled The Pleasure of Finding Things Out, and its author was Richard Feynman, the great physicist. The book is a transcript of some interviews Feynman gave to BBC, and it is by all means a good read. But, it is the title that I mostly relate to. The reasons for pursuing a PhD degree can be extremely varied: it may be a required step for a desired career, a way to achieve the title of doctor, curiosity, or even pure chance. For me, most of all, it was a desire to keep a certain pleasure alive. This pleasure is precisely the pleasure of finding things out. There are many moments in a PhD program where one produces a great deal of data; mostly, this data is the output of experiments carried out to prove a certain theory or intuition. Here you are with the intuition on one hand, and the data on the other. And then the pleasure begins. One looks at the data, analyses it, finds patterns and correlations, creates colourful graphs, looks at them, and finally one finds things out. At times there is nothing to find out because “it doesn’t work”, and one begins questioning whose fault it is. Is it the software or is it the human? Is it the design of the experiment? Is it the PhD candidate or the supervisor? Or the second cousin of the supervisor? Luckily, it is often a mix of the design of the experiment and the PhD candidate, which makes matters rather easy to solve. But then, sometimes, “it works”. The experiment proves the intuition, it is extended and generalized so that the intuition can become a theory, and tested on other cases so that one feels reasonably confident that the theory may simply be true. And this is a great moment because one has created something that was not there before. This something is not subject to taste or opinion and it does not lose its strength if you translate it to Chinese, but rather it is true until proven otherwise. In this sense, it is a purely intellectual battle, where one is looking forward to meeting who will prove it otherwise. And this is, to me, the pleasure of science. I take a look at this manuscript and I think: it could have been better; it should have been better. I see its faults, and see where it could have gone, given more time. But at the same time, I see that it does have something that makes it interesting, some tiny moments of true intuition, and some seeds of what will hopefully, one day, be the state of the art. Most of all, it makes me think of those moments where I finally found out things. Those were the moments of pleasure, and for those moments I wish to thank a few very important people.

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First of all, I wish to express my sincere, deep gratitude to my first promotor, Prof.dr.ir. Jan L.M. Hensen. Thank you Jan for being a constant source of inspiration through your comments, your silences, your decisions and your actions. Thank you for giving me this opportunity, for dealing with my – at times “special” – needs, for trusting me, and simply for being a great teacher. Thank you for sharing your knowledge, and for putting the “why” before the “how” in every situation. This is a lesson I will not forget.

Secondly, I would like to thank dr.ir. Pieter-Jan Hoes for the many hours he spent as my “daily supervisor”: I learnt a lot from you and I do think we make a great team. You have the ability to always bring up the most important points of a project without losing yourself in the details, and as a result you steer one’s thoughts in the right direction.

Thank you to the members of the committee of my doctoral dissertation: Prof. Laure Itard, Prof. Andreas Wagner, Prof. Bauke de Vries and our dean, Prof. Theo Salet. I am grateful for your patience, your expertise and your invaluable comments.

This research would have been half as successful, and half as much fun, if it wasn’t for my participation in IEA EBC Annex 66. Every Annex meeting ignited in me renewed energy, passion, and deep conviction that indeed occupant behaviour modelling is the most relevant topic in the world. I am grateful to all Annex 66 participants whom I was lucky enough to meet, and I wish I could thank them all personally for creating such an inspiring environment for knowledge sharing and cooperation. I will mention here just a few people, but you all deserve credit. Thank you to our operating agents, Da Yan and Tianzhen Hong, for running a highly cooperative Annex and for your exemplary management. Tianzhen, thank you for providing a living example of how professional excellence and human kindness can, and should, coexist. Thank you Liam O’Brien for your passion, your knowledge, and your enthusiasm. You are able to turn writing a scientific paper together into a fun activity, which I hope to repeat soon. Thank you Prof. Mahdavi and Farhang Tahmasebi for the great conversations ever since the very first meeting in Berkeley. It is a pleasure cooperating with you, and I admire your comprehensive knowledge. Thank you Burak Gunay for providing a (very hard to reach) benchmark. Thank you – in rigorous alphabetic order! – Rune K. Andersen, Verena Barthelmes, Salvatore Carlucci, Chien-fei Chen, Quentin Darakdjian, Zsofia Deme Belafi, Simona D’Oca, Bing Dong, Valentina Fabi, Sara Gilani, András Reith, Marcel Schweiker, David Shipworth, Yohei Yamaguchi, for the enriching exchanges and the fun times. Thank you Julia K. Day and Holly W. Samuelson for providing the girl power of our subtask. I look forward to our women-only consultancy.

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My colleagues at the group of Building Performance Simulation at TU Eindhoven deserve a very special mention. Thank you Vojta for being so much fun, for your knowledge, and for your shared love of MATLAB. I had the best time being your neighbour. Thank you Rajesh for your infinite kindness, patience and attention. You have always been there in the moments of need and I cannot be grateful enough. Thank you Roel for your hidden world, for your limitless knowledge, for your kindness, and for knowing – and pushing – your limits. Thank you Zahra for being the best host I ever met, and for providing me with a home away from home, or rather a 5-star hotel away from home; thank you for your friendship and for our conversations. Thank you Luyi for your friendship, for our talks about fit-for-purpose, for sharing a room in a houseboat and for co-founding the Lunch Club, a true institution. Thank you Antía for the ice- cream, no matter how cloudy the weather is. Thank you Ádám for your point of view, which is never predictable. Thanks to all of you who contributed to creating such a pleasant work environment in the 6th floor: Adam, Alessio, Agata, Asit, Bin, Christina, Christos, Dmitry, Edward, Evangelos, Fotis, Hemshikha, Ignacio, Johann, Marcel, Manuel, Marie, Mohamed, Oindrila, Olga, Parisa, Raul, Rongling, Samuel, Sanket, Tomaž, Veronica, Zhizhuo. Thank you Léontine – our secretary – for always being there for me, and for all of us, with a smile, a cookie, a problem solved or a fun email. Thank you to the ladies of the coffee space downstairs: we still miss you and we couldn’t have done without the breakfast deal.

I would like to take this opportunity to thank the individuals who acted as a user group for this research: Ronald Hennekeij and Paul Korthof from HOMIJ Technische Installaties, Steven Mast from Smits van Burgst and Wim Plokker from VABI Software; thank you for your time and for your insightful, user-oriented feedback.

During the few years spent as a PhD candidate at TU Eindhoven I had the privilege of supervising a number of students who were working towards their MSc theses; I am grateful for this opportunity, which was incredibly enriching on the professional and personal side. Thank you Eva van Enk, Randy van Eck, Yunshang Song, Bas Giskes, Antonio Muroni, Lenny Mennen and Remco van Woensel.

Thank you Rubina Ramponi for introducing me to the wonders of research in general, and of TU Eindhoven in particular. You will make a wonderful paranymph, besides being an experienced life coach. Thank you Duncan for understanding what I meant, and for making sure that the rest of the world understands it too. Thank you to the many friends who – directly or indirectly – were there to support this endeavour.

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Thank you to my family, or I should say my families: my first one – thank you mamma, papà, nonna, Ghira e Ni for always being there, and always being proud; my acquired one – Agi, Anna, Christina, Georg, Louisa, Ludmila, Peter: thank you for being a real family; and my brand new one – thank you Felix, thank you Leo.

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Publication List

Journal articles

1. Isabella Gaetani, Pieter-Jan Hoes, Jan L.M. Hensen (2018). Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run, Applied Energy, 223, 159-171 2. Da Yan, Tianzhen Hong, Bing Dong, Ardeshir Mahdavi, Simona D'Oca, Isabella Gaetani, Xiaohang Feng (2017). IEA EBC Annex 66: Definition and Simulation of Building Occupant Behavior, Energy and Buildings, 156, 258-270 (SI) 3. Liam O’Brien, Isabella Gaetani, Salvatore Carlucci, Pieter-Jan Hoes, Jan L.M. Hensen (2017). On occupant-centric building performance metrics, Building and Environment, 122, 373-385 4. Isabella Gaetani, Pieter-Jan Hoes, Jan L.M. Hensen (2016). On the sensitivity to different aspects of occupant behavior for selecting the appropriate modelling complexity in building performance predictions, Journal of Building Performance Simulation, 10, 601-611 (SI) 5. Liam O’Brien, Isabella Gaetani, Sara Gilani, Salvatore Carlucci, Pieter-Jan Hoes, Jan L.M. Hensen (2016). International perspective on current occupant modeling approaches in building simulation practice, Journal of Building Performance Simulation, 10, 653-671 (SI) 6. Isabella Gaetani, Pieter-Jan Hoes, Jan L.M. Hensen (2016). Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy. Energy and Buildings, 121, 188-204 (SI) Among most cited articles of Energy and Buildings since 2016 https://www.journals.elsevier.com/energy-and-buildings/most-cited- articles 7. Rubina Ramponi, Isabella Gaetani, Adriana Angelotti (2014). Influence of the urban environment on the effectiveness of natural night-ventilation of an office building, Energy and Buildings, 78, 25-34

Journal articles under review

1. Isabella Gaetani, Pieter-Jan Hoes, Jan L.M. Hensen (2019). A stepwise approach for appropriate occupant behavior modeling in building performance simulation

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2. Antonio Muroni, Isabella Gaetani, Pieter-Jan Hoes, Jan L.M. Hensen (2019). Occupant Behavior in identical residential buildings: A case study for occupancy profiles extraction and application to building performance simulation

Journal articles in preparation

1. Isabella Gaetani, Farhang Tahmasebi, Pieter-Jan Hoes, Ardeshir Mahdavi, Jan L.M. Hensen (2019). A real-life application of the fit-for-purpose occupant behavior modeling framework

Conference contributions

1. Isabella Gaetani, Pieter-Jan Hoes, Jan L.M. Hensen (2019). Occupant behavior modelling to support the development of adaptive facade control strategies, accepted for Proceedings of the 16th International IBPSA Conference (Building Simulation 2019), Rome 2. Isabella Gaetani, Pieter-Jan Hoes, Jan L.M. Hensen (2017). Introducing and testing a strategy for fit-for-purpose occupant behavior modeling in a simulation-aided building design process, Proceedings of the 15th International IBPSA Conference (Building Simulation 2017), San Francisco

Professional journals and magazines

1. Randy van Eck, Isabella Gaetani, Pieter-Jan Hoes, Ronald Hennekeij, Jan L.M. Hensen (2016). Energie- en comfortprestaties van energiezuinige woningen. TVVL Magazine, (10), 10-12

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Bouwstenen is een publicatiereeks van de Faculteit Bouwkunde, Technische Universiteit Eindhoven. Zij presenteert resultaten van onderzoek en andere activiteiten op het vakgebied der Bouwkunde, uitgevoerd in het kader van deze Faculteit.

Bouwstenen en andere proefschriften van de TU/e zijn online beschikbaar via: https://research.tue.nl/

Kernredactie MTOZ Reeds verschenen in de serie Bouwstenen nr 1 nr 9 Elan: A Computer Model for Building Strukturering en Verwerking van Energy Design: Theory and Validation Tijdgegevens voor de Uitvoering Martin H. de Wit van Bouwwerken H.H. Driessen ir. W.F. Schaefer R.M.M. van der Velden P.A. Erkelens nr 2 nr 10 Kwaliteit, Keuzevrijheid en Kosten: Stedebouw en de Vorming van Evaluatie van Experiment Klarendal, een Speciale Wetenschap Arnhem K. Doevendans J. Smeets C. le Nobel nr 11 M. Broos Informatica en Ondersteuning J. Frenken van Ruimtelijke Besluitvorming A. v.d. Sanden G.G. van der Meulen nr 3 nr 12 Crooswijk: Staal in de Woningbouw, Van ‘Bijzonder’ naar ‘Gewoon’ Korrosie-Bescherming van Vincent Smit de Begane Grondvloer Kees Noort Edwin J.F. Delsing nr 4 nr 13 Staal in de Woningbouw Een Thermisch Model voor de Edwin J.F. Delsing Berekening van Staalplaatbetonvloeren onder Brandomstandigheden nr 5 A.F. Hamerlinck Mathematical Theory of Stressed Skin Action in Profiled Sheeting with nr 14 Various Edge Conditions De Wijkgedachte in Nederland: Andre W.A.M.J. van den Bogaard Gemeenschapsstreven in een Stedebouwkundige Context nr 6 K. Doevendans Hoe Berekenbaar en Betrouwbaar is R. Stolzenburg de Coëfficiënt k in x-ksigma en x-ks? K.B. Lub nr 15 A.J. Bosch Diaphragm Effect of Trapezoidally Profiled Steel Sheets: nr 7 Experimental Research into the Het Typologisch Gereedschap: Influence of Force Application Een Verkennende Studie Omtrent Andre W.A.M.J. van den Bogaard Typologie en Omtrent de Aanpak van Typologisch Onderzoek nr 16 J.H. Luiten Versterken met Spuit-Ferrocement: Het Mechanische Gedrag van met nr 8 Spuit-Ferrocement Versterkte Informatievoorziening en Beheerprocessen Gewapend Betonbalken A. Nauta K.B. Lubir Jos Smeets (red.) M.C.G. van Wanroy Helga Fassbinder (projectleider) Adrie Proveniers J. v.d. Moosdijk nr 17 nr 27 De Tractaten van Het Woonmilieu op Begrip Gebracht: Jean Nicolas Louis Durand Een Speurtocht naar de Betekenis van het G. van Zeyl Begrip 'Woonmilieu' Jaap Ketelaar nr 18 Wonen onder een Plat Dak: nr 28 Drie Opstellen over Enkele Urban Environment in Developing Countries Vooronderstellingen van de editors: Peter A. Erkelens Stedebouw George G. van der Meulen (red.) K. Doevendans nr 29 nr 19 Stategische Plannen voor de Stad: Supporting Decision Making Processes: Onderzoek en Planning in Drie Steden A Graphical and Interactive Analysis of prof.dr. H. Fassbinder (red.) Multivariate Data H. Rikhof (red.) W. Adams nr 30 nr 20 Stedebouwkunde en Stadsbestuur Self-Help Building Productivity: Piet Beekman A Method for Improving House Building by Low-Income Groups Applied to Kenya nr 31 1990-2000 De Architectuur van Djenné: P. A. Erkelens Een Onderzoek naar de Historische Stad P.C.M. Maas nr 21 De Verdeling van Woningen: nr 32 Een Kwestie van Onderhandelen Conjoint Experiments and Retail Planning Vincent Smit Harmen Oppewal nr 22 nr 33 Flexibiliteit en Kosten in het Ontwerpproces: Strukturformen Indonesischer Bautechnik: Een Besluitvormingondersteunend Model Entwicklung Methodischer Grundlagen M. Prins für eine ‘Konstruktive Pattern Language’ in Indonesien nr 23 Heinz Frick arch. SIA Spontane Nederzettingen Begeleid: Voorwaarden en Criteria in Sri Lanka nr 34 Po Hin Thung Styles of Architectural Designing: Empirical Research on Working Styles nr 24 and Personality Dispositions Fundamentals of the Design of Anton P.M. van Bakel Bamboo Structures Oscar Arce-Villalobos nr 35 Conjoint Choice Models for Urban nr 25 Tourism Planning and Marketing Concepten van de Bouwkunde Benedict Dellaert M.F.Th. Bax (red.) H.M.G.J. Trum (red.) nr 36 Stedelijke Planvorming als Co-Produktie nr 26 Helga Fassbinder (red.) Meaning of the Site Xiaodong Li nr 37 nr 48 Design Research in the Netherlands Concrete Behaviour in Multiaxial editors: R.M. Oxman Compression M.F.Th. Bax Erik van Geel H.H. Achten nr 49 nr 38 Modelling Site Selection Communication in the Building Industry Frank Witlox Bauke de Vries nr 50 nr 39 Ecolemma Model Optimaal Dimensioneren van Ferdinand Beetstra Gelaste Plaatliggers J.B.W. Stark nr 51 F. van Pelt Conjoint Approaches to Developing L.F.M. van Gorp Activity-Based Models B.W.E.M. van Hove Donggen Wang nr 40 nr 52 Huisvesting en Overwinning van Armoede On the Effectiveness of Ventilation P.H. Thung Ad Roos P. Beekman (red.) nr 53 nr 41 Conjoint Modeling Approaches for Urban Habitat: Residential Group preferences The Environment of Tomorrow Eric Molin George G. van der Meulen Peter A. Erkelens nr 54 Modelling Architectural Design nr 42 Information by Features A Typology of Joints Jos van Leeuwen John C.M. Olie nr 55 nr 43 A Spatial Decision Support System for Modeling Constraints-Based Choices the Planning of Retail and Service Facilities for Leisure Mobility Planning Theo Arentze Marcus P. Stemerding nr 56 nr 44 Integrated Lighting System Assistant Activity-Based Travel Demand Modeling Ellie de Groot Dick Ettema nr 57 nr 45 Ontwerpend Leren, Leren Ontwerpen Wind-Induced Pressure Fluctuations J.T. Boekholt on Building Facades Chris Geurts nr 58 Temporal Aspects of Theme Park Choice nr 46 Behavior Generic Representations Astrid Kemperman Henri Achten nr 59 nr 47 Ontwerp van een Geïndustrialiseerde Johann Santini Aichel: Funderingswijze Architectuur en Ambiguiteit Faas Moonen Dirk De Meyer nr 60 nr 72 Merlin: A Decision Support System Moisture Transfer Properties of for Outdoor Leisure Planning Coated Gypsum Manon van Middelkoop Emile Goossens nr 61 nr 73 The Aura of Modernity Plybamboo Wall-Panels for Housing Jos Bosman Guillermo E. González-Beltrán nr 62 nr 74 Urban Form and Activity-Travel Patterns The Future Site-Proceedings Daniëlle Snellen Ger Maas Frans van Gassel nr 63 Design Research in the Netherlands 2000 nr 75 Henri Achten Radon transport in Autoclaved Aerated Concrete nr 64 Michel van der Pal Computer Aided Dimensional Control in Building Construction nr 76 Rui Wu The Reliability and Validity of Interactive Virtual Reality Computer Experiments nr 65 Amy Tan Beyond Sustainable Building editors: Peter A. Erkelens nr 77 Sander de Jonge Measuring Housing Preferences Using August A.M. van Vliet Virtual Reality and Belief Networks co-editor: Ruth J.G. Verhagen Maciej A. Orzechowski nr 66 nr 78 Das Globalrecyclingfähige Haus Computational Representations of Words Hans Löfflad and Associations in Architectural Design Nicole Segers nr 67 Cool Schools for Hot Suburbs nr 79 René J. Dierkx Measuring and Predicting Adaptation in Multidimensional Activity-Travel Patterns nr 68 Chang-Hyeon Joh A Bamboo Building Design Decision Support Tool nr 80 Fitri Mardjono Strategic Briefing Fayez Al Hassan nr 69 Driving Rain on Building Envelopes nr 81 Fabien van Mook Well Being in Hospitals Simona Di Cicco nr 70 Heating Monumental Churches nr 82 Henk Schellen Solares Bauen: Implementierungs- und Umsetzungs- nr 71 Aspekte in der Hochschulausbildung Van Woningverhuurder naar in Österreich Aanbieder van Woongenot Gerhard Schuster Patrick Dogge nr 83 nr 94 Supporting Strategic Design of Human Lighting Demands: Workplace Environments with Healthy Lighting in an Office Environment Case-Based Reasoning Myriam Aries Shauna Mallory-Hill nr 95 nr 84 A Spatial Decision Support System for ACCEL: A Tool for Supporting Concept the Provision and Monitoring of Urban Generation in the Early Design Phase Greenspace Maxim Ivashkov Claudia Pelizaro nr 85 nr 96 Brick-Mortar Interaction in Masonry Leren Creëren under Compression Adri Proveniers Ad Vermeltfoort nr 97 nr 86 Simlandscape Zelfredzaam Wonen Rob de Waard Guus van Vliet nr 98 nr 87 Design Team Communication Een Ensemble met Grootstedelijke Allure Ad den Otter Jos Bosman Hans Schippers nr 99 Humaan-Ecologisch nr 88 Georiënteerde Woningbouw On the Computation of Well-Structured Juri Czabanowski Graphic Representations in Architectural Design nr 100 Henri Achten Hambase Martin de Wit nr 89 De Evolutie van een West-Afrikaanse nr 101 Vernaculaire Architectuur Sound Transmission through Pipe Wolf Schijns Systems and into Building Structures Susanne Bron-van der Jagt nr 90 ROMBO Tactiek nr 102 Christoph Maria Ravesloot Het Bouwkundig Contrapunt Jan Francis Boelen nr 91 External Coupling between Building nr 103 Energy Simulation and Computational A Framework for a Multi-Agent Fluid Dynamics Planning Support System Ery Djunaedy Dick Saarloos nr 92 nr 104 Design Research in the Netherlands 2005 Bracing Steel Frames with Calcium editors: Henri Achten Silicate Element Walls Kees Dorst Bright Mweene Ng’andu Pieter Jan Stappers Bauke de Vries nr 105 Naar een Nieuwe Houtskeletbouw nr 93 F.N.G. De Medts Ein Modell zur Baulichen Transformation Jalil H. Saber Zaimian nr 106 and 107 nr 120 Niet gepubliceerd A Multi-Agent Planning Support System for Assessing Externalities nr 108 of Urban Form Scenarios Geborgenheid Rachel Katoshevski-Cavari T.E.L. van Pinxteren nr 121 nr 109 Den Schulbau Neu Denken, Modelling Strategic Behaviour in Fühlen und Wollen Anticipation of Congestion Urs Christian Maurer-Dietrich Qi Han nr 122 nr 110 Peter Eisenman Theories and Reflecties op het Woondomein Practices Fred Sanders Bernhard Kormoss nr 111 nr 123 On Assessment of Wind Comfort User Simulation of Space Utilisation by Sand Erosion Vincent Tabak Gábor Dezsö nr 125 nr 112 In Search of a Complex System Model Bench Heating in Monumental Churches Oswald Devisch Dionne Limpens-Neilen nr 126 nr 113 Lighting at Work: RE. Architecture Environmental Study of Direct Effects Ana Pereira Roders of Lighting Level and Spectrum on Psycho-Physiological Variables nr 114 Grazyna Górnicka Toward Applicable Green Architecture Usama El Fiky nr 127 Flanking Sound Transmission through nr 115 Lightweight Framed Double Leaf Walls Knowledge Representation under Stefan Schoenwald Inherent Uncertainty in a Multi-Agent System for Land Use Planning nr 128˙ Liying Ma Bounded Rationality and Spatio-Temporal Pedestrian Shopping Behavior nr 116 Wei Zhu Integrated Heat Air and Moisture Modeling and Simulation nr 129 Jos van Schijndel Travel Information: Impact on Activity Travel Pattern nr 117 Zhongwei Sun Concrete Behaviour in Multiaxial Compression nr 130 J.P.W. Bongers Co-Simulation for Performance Prediction of Innovative Integrated nr 118 Mechanical Energy Systems in Buildings The Image of the Urban Landscape Marija Trcka� Ana Moya Pellitero nr 131 nr 119 Niet gepubliceerd The Self-Organizing City in Vietnam Stephanie Geertman nr 132 nr 143 Architectural Cue Model in Evacuation Modelling Life Trajectories and Transport Simulation for Underground Space Design Mode Choice Using Bayesian Belief Networks Chengyu Sun Marloes Verhoeven nr 133 nr 144 Uncertainty and Sensitivity Analysis in Assessing Construction Project Building Performance Simulation for Performance in Ghana Decision Support and Design Optimization William Gyadu-Asiedu Christina Hopfe nr 145 nr 134 Empowering Seniors through Facilitating Distributed Collaboration Domotic Homes in the AEC/FM Sector Using Semantic Masi Mohammadi Web Technologies Jacob Beetz nr 146 An Integral Design Concept for nr 135 Ecological Self-Compacting Concrete Circumferentially Adhesive Bonded Glass Martin Hunger Panes for Bracing Steel Frame in Façades Edwin Huveners nr 147 Governing Multi-Actor Decision Processes nr 136 in Dutch Industrial Area Redevelopment Influence of Temperature on Concrete Erik Blokhuis Beams Strengthened in Flexure with CFRP nr 148 Ernst-Lucas Klamer A Multifunctional Design Approach for Sustainable Concrete nr 137 Götz Hüsken Sturen op Klantwaarde Jos Smeets nr 149 Quality Monitoring in Infrastructural nr 139 Design-Build Projects Lateral Behavior of Steel Frames Ruben Favié with Discretely Connected Precast Concrete Infill Panels nr 150 Paul Teewen Assessment Matrix for Conservation of Valuable Timber Structures nr 140 Michael Abels Integral Design Method in the Context of Sustainable Building Design nr 151 Perica Savanovic´ Co-simulation of Building Energy Simulation and Computational Fluid Dynamics for nr 141 Whole-Building Heat, Air and Moisture Household Activity-Travel Behavior: Engineering Implementation of Within-Household Mohammad Mirsadeghi Interactions Renni Anggraini nr 152 External Coupling of Building Energy nr 142 Simulation and Building Element Heat, Design Research in the Netherlands 2010 Air and Moisture Simulation Henri Achten Daniel Cóstola nr 153 nr 165 Adaptive Decision Making In Beyond Uniform Thermal Comfort Multi-Stakeholder Retail Planning on the Effects of Non-Uniformity and Ingrid Janssen Individual Physiology Lisje Schellen nr 154 Landscape Generator nr 166 Kymo Slager Sustainable Residential Districts Gaby Abdalla nr 155 Constraint Specification in Architecture nr 167 Remco Niemeijer Towards a Performance Assessment Methodology using Computational nr 156 Simulation for Air Distribution System A Need-Based Approach to Designs in Operating Rooms Dynamic Activity Generation Mônica do Amaral Melhado Linda Nijland nr 168 nr 157 Strategic Decision Modeling in Modeling Office Firm Dynamics in an Brownfield Redevelopment Agent-Based Micro Simulation Framework Brano Glumac Gustavo Garcia Manzato nr 169 nr 158 Pamela: A Parking Analysis Model Lightweight Floor System for for Predicting Effects in Local Areas Vibration Comfort Peter van der Waerden Sander Zegers nr 170 nr 159 A Vision Driven Wayfinding Simulation-System Aanpasbaarheid van de Draagstructuur Based on the Architectural Features Perceived Roel Gijsbers in the Office Environment Qunli Chen nr 160 'Village in the City' in Guangzhou, China nr 171 Yanliu Lin Measuring Mental Representations Underlying Activity-Travel Choices nr 161 Oliver Horeni Climate Risk Assessment in Museums Marco Martens nr 172 Modelling the Effects of Social Networks nr 162 on Activity and Travel Behaviour Social Activity-Travel Patterns Nicole Ronald Pauline van den Berg nr 173 nr 163 Uncertainty Propagation and Sensitivity Sound Concentration Caused by Analysis Techniques in Building Performance Curved Surfaces Simulation to Support Conceptual Building Martijn Vercammen and System Design Christian Struck nr 164 Design of Environmentally Friendly nr 174 Calcium Sulfate-Based Building Materials: Numerical Modeling of Micro-Scale Towards an Improved Indoor Air Quality Wind-Induced Pollutant Dispersion Qingliang Yu in the Built Environment Pierre Gousseau nr 175 nr 185 Modeling Recreation Choices A Distributed Dynamic Simulation over the Family Lifecycle Mechanism for Buildings Automation Anna Beatriz Grigolon and Control Systems Azzedine Yahiaoui nr 176 Experimental and Numerical Analysis of nr 186 Mixing Ventilation at Laminar, Transitional Modeling Cognitive Learning of Urban and Turbulent Slot Reynolds Numbers Networks in Daily Activity-Travel Behavior Twan van Hooff Sehnaz¸ Cenani Durmazoglu� nr 177 nr 187 Collaborative Design Support: Functionality and Adaptability of Design Workshops to Stimulate Interaction and Solutions for Public Apartment Buildings Knowledge Exchange Between Practitioners in Ghana Emile M.C.J. Quanjel Stephen Agyefi-Mensah nr 178 nr 188 Future-Proof Platforms for Aging-in-Place A Construction Waste Generation Model Michiel Brink for Developing Countries Lilliana Abarca-Guerrero nr 179 Motivate: nr 189 A Context-Aware Mobile Application for Synchronizing Networks: Physical Activity Promotion The Modeling of Supernetworks for Yuzhong Lin Activity-Travel Behavior Feixiong Liao nr 180 Experience the City: nr 190 Analysis of Space-Time Behaviour and Time and Money Allocation Decisions Spatial Learning in Out-of-Home Leisure Activity Choices Anastasia Moiseeva Gamze Zeynep Dane nr 181 nr 191 Unbonded Post-Tensioned Shear Walls of How to Measure Added Value of CRE and Calcium Silicate Element Masonry Building Design Lex van der Meer Rianne Appel-Meulenbroek nr 182 nr 192 Construction and Demolition Waste Secondary Materials in Cement-Based Recycling into Innovative Building Materials Products: for Sustainable Construction in Tanzania Treatment, Modeling and Environmental Mwita M. Sabai Interaction Miruna Florea nr 183 Durability of Concrete nr 193 with Emphasis on Chloride Migration Concepts for the Robustness Improvement Przemys�aw Spiesz of Self-Compacting Concrete: Effects of Admixtures and Mixture nr 184 Components on the Rheology and Early Computational Modeling of Urban Hydration at Varying Temperatures Wind Flow and Natural Ventilation Potential Wolfram Schmidt of Buildings Rubina Ramponi nr 194 nr 204 Modelling and Simulation of Virtual Natural Geometry and Ventilation: Lighting Solutions in Buildings Evaluation of the Leeward Sawtooth Roof Rizki A. Mangkuto Potential in the Natural Ventilation of Buildings nr 195 Jorge Isaac Perén Montero Nano-Silica Production at Low Temperatures from the Dissolution of Olivine - Synthesis, nr 205 Tailoring and Modelling Computational Modelling of Evaporative Alberto Lazaro Garcia Cooling as a Climate Change Adaptation Measure at the Spatial Scale of Buildings nr 196 and Streets Building Energy Simulation Based Hamid Montazeri Assessment of Industrial Halls for Design Support nr 206 Bruno Lee Local Buckling of Aluminium Beams in Fire Conditions nr 197 Ronald van der Meulen Computational Performance Prediction of the Potential of Hybrid Adaptable nr 207 Thermal Storage Concepts for Lightweight Historic Urban Landscapes: Low-Energy Houses Framing the Integration of Urban and Pieter-Jan Hoes Heritage Planning in Multilevel Governance Loes Veldpaus nr 198 Application of Nano-Silica in Concrete nr 208 George Quercia Bianchi Sustainable Transformation of the Cities: Urban Design Pragmatics to Achieve a nr 199 Sustainable City Dynamics of Social Networks and Activity Ernesto Antonio Zumelzu Scheel Travel Behaviour Fariya Sharmeen nr 209 Development of Sustainable Protective nr 200 Ultra-High Performance Fibre Reinforced Building Structural Design Generation and Concrete (UHPFRC): Optimisation including Spatial Modification Design, Assessment and Modeling Juan Manuel Davila Delgado Rui Yu nr 201 nr 210 Hydration and Thermal Decomposition of Uncertainty in Modeling Activity-Travel Cement/Calcium-Sulphate Based Materials Demand in Complex Uban Systems Ariën de Korte Soora Rasouli nr 202 nr 211 Republiek van Beelden: Simulation-based Performance Assessment De Politieke Werkingen van het Ontwerp in of Climate Adaptive Greenhouse Shells Regionale Planvorming Chul-sung Lee Bart de Zwart nr 212 nr 203 Green Cities: Effects of Energy Price Increases on Modelling the Spatial Transformation of Individual Activity-Travel Repertoires and the Urban Environment using Renewable Energy Consumption Energy Technologies Dujuan Yang Saleh Mohammadi nr 213 nr 223 A Bounded Rationality Model of Short and Personalized Route Finding in Multimodal Long-Term Dynamics of Activity-Travel Transportation Networks Behavior Jianwe Zhang Ifigeneia Psarra nr 224 nr 214 The Design of an Adaptive Healing Room Effects of Pricing Strategies on Dynamic for Stroke Patients Repertoires of Activity-Travel Behaviour Elke Daemen Elaheh Khademi nr 225 nr 215 Experimental and Numerical Analysis of Handstorm Principles for Creative and Climate Change Induced Risks to Historic Collaborative Working Buildings and Collections Frans van Gassel Zara Huijbregts nr 216 nr 226 Light Conditions in Nursing Homes: Wind Flow Modeling in Urban Areas Through Visual Comfort and Visual Functioning of Experimental and Numerical Techniques Residents Alessio Ricci Marianne M. Sinoo nr 227 nr 217 Clever Climate Control for Culture: Woonsporen: Energy Efficient Indoor Climate Control De Sociale en Ruimtelijke Biografie van Strategies for Museums Respecting een Stedelijk Bouwblok in de Amsterdamse Collection Preservation and Thermal Transvaalbuurt Comfort of Visitors Hüseyin Hüsnü Yegenoglu Rick Kramer nr 218 nr 228 Studies on User Control in Ambient Fatigue Life Estimation of Metal Structures Intelligent Systems Based on Damage Modeling Berent Willem Meerbeek Sarmediran Silitonga nr 219 nr 229 Daily Livings in a Smart Home: A multi-agents and occupancy based Users’ Living Preference Modeling of Smart strategy for energy management and Homes process control on the room-level Erfaneh Allameh Timilehin Moses Labeodan nr 220 nr 230 Smart Home Design: Environmental assessment of Building Spatial Preference Modeling of Smart Integrated Photovoltaics: Homes Numerical and Experimental Carrying Mohammadali Heidari Jozam Capacity Based Approach Michiel Ritzen nr 221 Wonen: nr 231 Discoursen, Praktijken, Perspectieven Performance of Admixture and Secondary Jos Smeets Minerals in Alkali Activated Concrete: Sustaining a Concrete Future nr 222 Arno Keulen Personal Control over Indoor Climate in Offices: Impact on Comfort, Health and Productivity Atze Christiaan Boerstra nr 232 nr 241 World Heritage Cities and Sustainable Gap-Theoretical Analyses of Residential Urban Development: Satisfaction and Intention to Move Bridging Global and Local Levels in Monitor- Wen Jiang ing the Sustainable Urban Development of World Heritage Cities nr 242 Paloma C. Guzman Molina Travel Satisfaction and Subjective Well-Being: A Behavioral Modeling Perspective nr 233 Yanan Gao Stage Acoustics and Sound Exposure in Performance and Rehearsal Spaces for nr 243 Orchestras: Building Energy Modelling to Support Methods for Physical Measurements the Commissioning of Holistic Data Centre Remy Wenmaekers Operation Vojtech Zavrel nr 234 Municipal Solid Waste Incineration (MSWI) nr 244 Bottom Ash: Regret-Based Travel Behavior Modeling: From Waste to Value Characterization, An Extended Framework Treatments and Application Sunghoon Jang Pei Tang nr 245 nr 235 Towards Robust Low-Energy Houses: Large Eddy Simulations Applied to Wind A Computational Approach for Performance Loading and Pollutant Dispersion Robustness Assessment using Scenario Mattia Ricci Analysis Rajesh Reddy Kotireddy nr 236 Alkali Activated Slag-Fly Ash Binders: nr 246 Design, Modeling and Application Development of sustainable and Xu Gao functionalized inorganic binder-biofiber composites nr 237 Guillaume Doudart de la Grée Sodium Carbonate Activated Slag: Reaction Analysis, Microstructural nr 247 Modification & Engineering Application A Multiscale Analysis of the Urban Heat Bo Yuan Island Effect: From City Averaged Temperatures to the Energy Demand of nr 238 Individual Buildings Shopping Behavior in Malls Yasin Toparlar Widiyani nr 248 nr 239 Design Method for Adaptive Daylight Smart Grid-Building Energy Interactions: Systems for buildings covered by large Demand Side Power Flexibility in Office (span) roofs Buildings Florian Heinzelmann Kennedy Otieno Aduda nr 249 nr 240 Hardening, high-temperature resistance and Modeling Taxis Dynamic Behavior in acid resistance of one-part geopolymers Uncertain Urban Environments Patrick Sturm Zheng Zhong nr 250 nr 259 Effects of the built environment on dynamic A Sustainable Industrial Site Redevelop- repertoires of activity-travel behaviour ment Planning Support System Aida Pontes de Aquino Tong Wang nr 251 nr 260 Modeling for auralization of urban Efficient storage and retrieval of detailed environments: Incorporation of directivity in building models: Multi-disciplinary and sound propagation and analysis of a frame- long-term use of geometric and semantic work for auralizing a car pass-by construction information Fotis Georgiou Thomas Ferdinand Krijnen nr 252 nr 261 Wind Loads on Heliostats and Photovoltaic The users’ value of business center concepts Trackers for knowledge sharing and networking be- Andreas Pfahl havior within and between organizations Minou Weijs-Perrée nr 253 Approaches for computational performance nr 262 optimization of innovative adaptive façade Characterization and improvement of aero- concepts dynamic performance of vertical axis Roel Loonen wind turbines using computational fluid dynamics (CFD) nr 254 Abdolrahim Rezaeiha Multi-scale FEM-DEM Model for Granular Materials: Micro-scale boundary conditions, nr 263 Statics, and Dynamics In-situ characterization of the acoustic Jiadun Liu impedance of vegetated roofs Chang Liu nr 255 Bending Moment - Shear Force Interaction nr 264 of Rolled I-Shaped Steel Sections Occupancy-based lighting control: Develop- Rianne Willie Adriana Dekker ing an energy saving strategy that ensures office workers’ comfort nr 256 Christel de Bakker Paralympic tandem cycling and hand- cycling: Computational and wind tunnel nr 265 analysis of aerodynamic performance Stakeholders-Oriented Spatial Decision Paul Fionn Mannion Support System Cahyono Susetyo nr 257 Experimental characterization and nu- nr 266 merical modelling of 3D printed concrete: Climate-induced damage in oak museum Controlling structural behaviour in the fresh objects and hardened state Rianne Aleida Luimes Robert Johannes Maria Wolfs nr 267 nr 258 Towards individual thermal comfort: Requirement checking in the building indus- Model predictive personalized control of try: Enabling modularized and extensible heating systems requirement checking systems based on Katarina Katic semantic web technologies Chi Zhang nr 268 Modelling and Measuring Quality of Urban Life: Housing, Neighborhood, Transport and Job Lida Aminian nr 269 Optimization of an aquifer thermal energy storage system through integrated modelling of aquifer, HVAC systems and building Basar Bozkaya nr 270 Numerical modeling for urban sound propagation: developments in wave-based and energy-based methods Raúl Pagán Muñoz nr 271 Lighting in multi-user office environments: improving employee wellbeing through personal control Sanae van der Vleuten-Chraibi Occupant behavior (OB), deﬁned as the presence and actions of occupants that affect building energy use, is recognized as a main source of discrepancy between simulation predictions and actual building performance. For this reason, researchers are attempting to offer more realistic alternatives to the simplistic assumptions regarding occupants that are commonly made in BPS models. Literature shows a proliferation of increasingly complex, data-based models that describe occupant presence and behaviors. However, the use of these models in practice is still very limited. Moreover, simpler models might be preferable, depending on the purpose of the investigation.

This doctoral dissertation explores how computational modelling, simulation and sensitivity analysis techniques can be used to appropriately represent occupants and their behaviors when predicting the energy and comfort performance of buildings. More speciﬁcally, the aim of this work is to develop a computational methodology that can be used to facilitate the model selection of various OB aspects in order to achieve ﬁt-for-purpose OB modelling.

DEPARTMENT OF THE BUILT ENVIRONMENT