The Pennsylvania State University the Graduate School College Of

Home , Autonomous building

The Pennsylvania State University

The Graduate School

College of Engineering

INTEGRATING OCCUPANT VALUES AND PREFERENCES WITH BUILDING SYSTEMS

IN CONDITIONED ENVIRONMENTS

A Dissertation in

Architectural Engineering

Yewande S. Abraham

 2018 Yewande S. Abraham

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

May 2018

The dissertation of Yewande S. Abraham was reviewed and approved* by the following:

Chimay J. Anumba Dean & Professor at the College of Design, Construction and Planning University of Florida Adjunct Professor of Architectural Engineering Pennsylvania State University Dissertation Co-Advisor Co-Chair of Committee

Somayeh Asadi Assistant Professor of Architectural Engineering Pennsylvania State University Dissertation Co-Advisor Co-Chair of Committee

James D. Freihaut Professor of Architectural Engineering Pennsylvania State University

Conrad S. Tucker Associate Professor of Engineering Design and Industrial Engineering Pennsylvania State University

Lisa D. Iulo Associate Professor of Architecture Pennsylvania State University Special Member

Nora El-Gohary Associate Professor of Civil and Environmental Engineering University of Illinois at Urbana-Champagne Special Member

Richard Mistrick Associate Professor of Architectural Engineering Pennsylvania State University

*Signatures are on file in the Graduate School

ABSTRACT

In most countries, including the U.S., buildings are responsible for about 40% of energy consumption and over one-third of greenhouse gas emissions. Buildings consume a significant amount of energy during the operations phase. An acceptable indoor environmental quality (IEQ) is essential for building occupants since poor conditions can impact their perceived health and productivity. People spend about 90% of their time indoors and sometimes take actions to improve their comfort such as adjusting the building systems [i.e., heating, ventilation and air conditioning

(HVAC) systems], using personal devices, or adjusting their layers of clothing. Building systems do not adequately take the occupant preferences into account during the operations phase and the indoor environmental controls do not always accommodate those preferences. Occupant values

(such as thermal comfort and visual comfort) can be better addressed in buildings through improved integration with building controls. Bridging the gap between occupant IEQ needs and the actual indoor conditions can be beneficial to improving building performance and occupant comfort.

This study focused on the development of an approach to integrating occupant values and preferences with building systems to enhance comfort while reducing energy consumption. The objectives of this study were to establish occupant values and the relationships between the values, satisfaction, and behavior. A literature review was conducted to identify research gaps, the potential benefits of improved integration, and the applicability of agent-based modeling (ABM) for modeling occupant behavior and occupant values. Following this, an exploratory study using semi- structured interviews and questionnaires was conducted with professionals in the aerospace, shipbuilding, and automobile industry to assess how end-user values and preferences are accounted for and to identify lessons that can be learned for buildings. Empirical studies were completed using three case study buildings, two office spaces and one residential building in two different climates to understand the building operation and occupant values and behavior in those spaces. Surveys to

iii

establish occupant values, preferences, and satisfaction were also conducted in these buildings.

From the findings in the industry case studies and empirical studies, an approach was proposed for improved integration between occupant values and building systems. An evaluation of the proposed approach by key industry professionals and experts demonstrated the need for improved communication between occupants and building operators, highlighted the importance of occupant education, and emphasized the energy savings that can be realized by eliminating some of the behaviors related to occupant discomfort with indoor conditions.

Recommendations for an integrative occupant-sensitive building operation were proposed following the exploration of other industries and the empirical studies. This thesis contributes to an understanding of occupant behavior and preferences through continuous monitoring of operational residential and office buildings in different climates. Further emphasis is placed on the role of occupants in buildings and the interventions that can allow for improved integration to enhance

IEQ and building energy efficiency.

TABLE OF CONTENTS LIST OF FIGURES ...... ix LIST OF TABLES ...... xii PREFACE ...... xiv ACKNOWLEDGMENTS ...... xv Chapter 1 INTRODUCTION ...... 1 1.1. Background ...... 1 1.2. Problem Statement ...... 5 1.3. Research Overview...... 5 1.2.1 Research Aim and Objectives ...... 6 1.2.2 Research Scope ...... 8 1.4. Research Contributions ...... 8 1.5. Thesis Structure ...... 9 Chapter 2 OVERVIEW OF RESEARCH METHODOLOGY ...... 10 2.1. Introduction ...... 10 2.2. Summary of Research Methods ...... 12 2.2.1. Quantitative Research Methods ...... 13 2.2.2. Qualitative Research Methods ...... 14 2.3. Approaches to Building Energy Management Research ...... 15 2.4. Approaches to Energy Modeling and Simulation Research ...... 17 2.5. Choice and Justification of Research Methods ...... 17 2.6. Approaches to Data Analysis ...... 24 2.7. Summary ...... 26 Chapter 3 END-USER VALUES IN CONDITIONED ENVIRONMENTS ...... 27 3.1. Introduction ...... 27 3.2. Building Energy Consumption ...... 28 3.2.1. Overview of Energy Consumption by Various End-use Categories ...... 30 3.2.2. Impact of Human Behavior on Building Energy Consumption ...... 31 3.2.3. Energy Use Measurement and Monitoring in Buildings ...... 34 3.3. Occupant Values and Indoor Environmental Conditions ...... 35 3.3.1. Defining Value ...... 37 3.3.2. Value for Building Design and Operation ...... 38 3.3.3. Defining Occupant Values ...... 39 3.4. Building Automation and Control Systems ...... 42 3.4.1. Types of Building Control Systems ...... 43 v

3.4.2. Intelligent Building Control Systems ...... 44 3.4.3. End-user Values and Building Control Systems ...... 46 3.5. Building Energy Modeling and Simulation ...... 47 3.5.1. Introduction to Energy Modeling and Simulation ...... 48 3.5.2. Comparison of Building Energy Modeling and Simulation Tools ...... 50 3.5.3. Applications of Building Energy Modeling and Simulation...... 52 3.6. Agent-based Modeling and Simulation ...... 54 3.6.1. Agent-based Modeling of Building Occupants ...... 54 3.6.2. Comparison of Agent-based Modeling Tools ...... 57 3.7. Summary ...... 58 Chapter 4 LEARNING FROM OTHER INDUSTRY SECTORS ...... 60 4.1. Introduction ...... 60 4.2. Accounting for End-user Values and Preferences in the Transportation Sector ...... 62 4.2.1. Aerospace Industry ...... 65 4.2.2. Automotive Industry ...... 67 4.2.3. Ship-building Industry ...... 70 4.3. Methodology for Learning from Other Industries ...... 71 4.4. Industry Case Studies ...... 72 4.4.1. Cross-case Analysis ...... 76 4.4.2. Enabling and Activating Factors ...... 77 4.4.3. Barriers or Restraining Factors ...... 77 4.5. Lessons Learned for the Building Sector from Other Industry Sectors ...... 78 4.6. Discussion of Other Industry Sectors ...... 79 4.7. Summary ...... 79 Chapter 5 BUILDING MONITORING: AN INTERACTIVE APPROACH ...... 81 5.1. Introduction ...... 81 5.2. Building Energy Monitoring ...... 82 5.3. Instrumentation Plan Development and Deployment ...... 85 5.3.1. Considerations for Data Sensing System ...... 87 5.3.2. Indoor and Outdoor Environmental Conditions...... 89 5.3.3. Energy Use Measurements ...... 89 5.3.4. Occupant Feedback System Development ...... 90 5.4. Pilot Studies ...... 93 5.5. Summary ...... 94 Chapter 6 BUILDING ENERGY MONITORING CASE STUDIES ...... 95

6.1. Introduction ...... 95 6.2. Case Study 1- U.S. Office Building ...... 97 6.3. Case Study 2- Qatar Office Building ...... 101 6.4. Case Study 3- U.S. Residential Building ...... 104 6.5. Occupant Value Elicitation ...... 105 6.6. Occupant Preference Monitoring ...... 108 6.7. Approaches to Data Analysis for Empirical Energy Studies ...... 109 6.8. Cross-case Analysis of the Office Buildings ...... 111 6.8.1. Comparison of Occupant Feedback on PMA in in the Office Buildings ...... 112 6.8.2. Comparison of Power Consumption Patterns in the Office Buildings ...... 114 6.8.3. Comparison of Average Outdoor Temperature with Power Consumption ... 118 6.8.4. Comparison of Occupant Behavior in the U.S. and Qatar Office Buildings 119 6.9. Results from U.S. Residential Building ...... 120 6.10. Discussion of Results ...... 122 6.11. Implications of Data Collected ...... 124 6.12. Summary ...... 124 Chapter 7 A VALUE-SENSITIVE AGENT-BASED MODELING APPROACH ...... 126 7.1. Introduction ...... 126 7.2. Definition of Key Terms ...... 127 7.3. Applications of Agent-based Modeling in Different Industries ...... 127 7.3.1. Safety and Egress Design Applications ...... 127 7.3.2. Construction Industry Applications ...... 128 7.3.3. Building Energy Analysis ...... 128 7.3.4. Other Applications of Agent-based Modeling ...... 129 7.4. Buildings and Occupant-related Factors ...... 129 7.5. Overview of Occupant Behavior Modeling ...... 132 7.6. Rapid Prototyping of Agent-based Model ...... 133 7.6.1. ABM Development Objectives ...... 136 7.6.2. Agent-based Modeling Architecture for Building Operations ...... 136 7.6.3. System Design Requirements ...... 137 7.6.4. Model Assumptions ...... 139 7.6.5. Operation and Testing ...... 139 7.7. Agent-based Modeling Application Scenarios in a Building ...... 140 7.7.1. Occupant in a Private Office without Control over Building Systems ...... 141 7.7.2. Occupant in a Private Office with Control over Building Systems ...... 142

vii

7.7.3. Modeling Effect of Change of Behavior on Different Occupants ...... 143 7.8. Preliminary Simulation Results ...... 144 7.9. Evaluation of the Occupant-value-sensitive ABM Approach ...... 148 7.9.1. Evaluation Objectives ...... 149 7.9.2. Evaluation Process ...... 149 7.9.3. Evaluation Results and Discussion ...... 151 7.9.4. Recommendations from Evaluation ...... 156 7.10. Potential Benefits for Value-sensitive Occupant Behavior Modeling ...... 158 7.11. Summary ...... 158 Chapter 8 CONCLUSIONS AND RECOMMENDATIONS ...... 160 8.1. Overview of Research ...... 160 8.2. Summary of How Objectives were Achieved ...... 161 8.3. Contributions to Knowledge ...... 163 8.4. Implications for the Industry ...... 164 8.5. Research Limitations ...... 165 8.6. Recommendations for Future Work ...... 167 8.7. Concluding Remarks ...... 168 REFERENCES ...... 170 APPENDICES ...... 194 Appendix A- Questionnaire for Other Industries ...... 194 Appendix B- Survey Questions ...... 196 Appendix C- Sensor Specifications ...... 203 Appendix D- Instrumentation Plans ...... 204 Appendix E- Background VET Results ...... 210 Appendix F- Occupant Profile Information ...... 214 Appendix G- Evaluation Questionnaire ...... 215 Appendix H- IRB Exemption ...... 218

viii

LIST OF FIGURES

Figure 1-1: U.S. Energy Consumption by Sector in 2016 (U.S. EIA, 2017) ...... 1

Figure 1-2: Energy Use Per Capita in China, Qatar, and USA (OECD/IEA, 2014) ...... 3

Figure 1-3: Trends of GDP Per Capita in China, Qatar, and the U.S. (OECD/IEA 2014) ..... 3

Figure 1-4: Overall Research Framework ...... 7

Figure 2-1: Conceptual Framework for Research Methodology ...... 18

Figure 2-2: Research Tasks including Data Collection Approach ...... 19

Figure 2-3: Process for Industry Sector Interactions to Develop Recommendations for Buildings ...... 20

Figure 2-4: Research Steps for Task 3 ...... 21

Figure 2-5: Components of Building Energy and Environmental Monitoring System ...... 23

Figure 3-1: Breakdown of U.S. Energy Consumption (a) Single Family Home- 2009 (U.S. EIA, 2013), (b) Office Energy Use- 2012 (U.S. EIA, 2016) ...... 31

Figure 3-2: Life Cycle Stages of a Building ...... 38

Figure 3-3: Building Automation and Control Systems ...... 42

Figure 3-5: The Callaghan-Clark-Chin (3C) Model (Ball & Callaghan, 2012) ...... 45

Figure 4-1: Comparison of Energy Consumptions by Different Sectors (Architecture 2030, 2014) ...... 62

Figure 4-2: Comparison of Growth Areas and Emissions, 1980-2015 (U.S. EPA, 2017) ...... 63

Figure 4-3: Extent of Integration of Occupant Values with ECS ...... 72

Figure 4-4: End-user Control of ECS ...... 74

Figure 5-1: Levels of Energy Use in Buildings ...... 90

Figure 5-2: Screenshots of the PMA on a Smartphone ...... 91

Figure 6-1: Parameters for Interactive Sensing Plan for the Case Studies ...... 95

Figure 6-2: Occupant Feedback Collection Tools (VET and PMA) ...... 96

Figure 6-3: U.S. Office Building Location and Building Elevation ...... 98

Figure 6-4: Screenshots of U.S. Office Data Collection Interface and BMS on Coresight .... 98 ix

Figure 6-5: Sensor Layout in U.S. Office (Modified from Coresight) ...... 99

Figure 6-6: Sample Sensors Installed in the Office Building (a) IEQ sensor, (b) Power Monitoring Solution, (c) Temperature and RH Sensor ...... 100

Figure 6-7: Screenshot of the CNAQ BMS ...... 101

Figure 6-8: Qatar Office Building Location and Building Elevation ...... 102

Figure 6-9: Illuminance Sensor Layout in Qatar Office ...... 102

Figure 6-10: Residential Building Aerial View and Rear Elevation ...... 104

Figure 6-11: Sensors and Measuring Devices for Residential Building ...... 105

Figure 6-12: Response Rates on PMA over 12 Months ...... 108

Figure 6-13: PMA Breakdown in Office Buildings over 12 Months (a) By Number of Participants (b) By Number of Responses ...... 108

Figure 6-14: Number of Responses on PMA per Occupant over 12 Months (a) Qatar Occupants (b) U.S Occupants ...... 109

Figure 6-15: Indoor Temperature vs. Perception (a) Occupant 1 (b) Occupant 2 ...... 110

Figure 6-16: Plot of Indoor Temperature vs. Satisfaction (a) Occupant 1 (b) Occupant 2 ..... 111

Figure 6-17: Maximum and Minimum Monthly Outdoor Temperatures- Doha and Philadelphia (Weatherspark, 2017) ...... 112

Figure 6-18: Feedback on the Perception of the IEQ Parameters ...... 113

Figure 6-19: Feedback on the Impact of the IEQ Parameters on Perceived Health and Personal Productivity ...... 113

Figure 6-20: Occupant Responses on Satisfaction with IEQ Parameters ...... 114

Figure 6-21: Energy Use Breakdown- June 2016 (a) Qatar Office (b) U.S. Office ...... 114

Figure 6-22: Weekly Power Consumption Profile Qatar Office- June 12-19, 2016 ...... 115

Figure 6-23: Daily (Weekday) Power Consumption Profile Qatar Office- June 14, 2016 ..... 116

Figure 6-24: Lighting Power (a) Average Illuminance Measurements in Qatar Office- June 2016, (b) Illuminance and Lighting Power vs. Time- June 14, 2016 ...... 116

Figure 6-25: Breakdown of Power Consumption in a Typical Week in the U.S.- June 12- 19, 2016 ...... 117

Figure 6-26: Daily (Weekday) Power Consumption Profile U.S. Office- June 14, 2016 ...... 118 x

Figure 6-27: Hourly Average Outdoor Temperature vs. Cooling Power Demand Qatar- June 2016 ...... 118

Figure 6-28: Hourly Outdoor Temperature vs. Cooling Power Demand U.S.- June 2016 ..... 119

Figure 6-29: U.S. Residential Building Electricity Consumption- October 2016 ...... 121

Figure 6-30: Indoor Temperature, RH, and CO2 in Family Room for October 2-10, 2016 .... 121

Figure 6-31: Indoor Temperature, RH, and CO2 in the Bedroom for October 2-10, 2016 ..... 122

Figure 7-1: ABM Methodology for Value-sensitive Analysis...... 134

Figure 7-2: Agent-based Model Architecture (Adapted from Chen et al. (2017)) ...... 137

Figure 7-3: Interface of the Prototype System ...... 138

Figure 7-4: View of Building Floor Layout and Plots showing Occupants Interaction in One Day ...... 138

Figure 7-5: Scenarios of Occupants Interaction with Building Systems ...... 140

Figure 7-6: Proposed Simulation Framework ...... 144

Figure 7-7: State Charts for Model Scenarios ...... 145

Figure 7-8: Showing Parameters Assigned to an Occupant within the Model ...... 146

Figure 7-9: Demonstrating Effect of Intervention- High Effectiveness ...... 146

Figure 7-10: Demonstrating the Effect of Intervention- Moderate Effectiveness ...... 147

Figure 7-11: Demonstrating the Effect of Intervention- Low Effectiveness ...... 148

Figure 7-12: Evaluation Steps ...... 151

Figure 7-13: Usefulness of the ABM Approach ...... 151

Figure 7-14: Proposed Scenarios for Testing the Approach ...... 152

Figure 7-15: Potential for Improved Integration of Occupant Values and Building Systems ...... 152

Figure 7-16: Rating of Performance of the ABM Approach ...... 153

Figure 7-17: Importance of Providing Notification to Different Parties ...... 154

Figure 7-18: Importance of the Features of an Occupant Feedback Dashboard ...... 154

LIST OF TABLES

Table 2-1: Combination of Knowledge Claims, Strategies of Inquiry, and Methods (Reproduced from Creswell, 2003) ...... 11

Table 2-2: Differences between Quantitative and Qualitative Research Methods (Leedy & Ormrod, 2005, p. 96) ...... 12

Table 2-3: Approaches to Data Analysis for Research Tasks ...... 24

Table 3-1: Recommended Levels for Various Indoor Environmental Parameters (ASHRAE, 2010; Autodesk, 2017) ...... 42

Table 3-2: Comparison of the Features of Energy Modeling and Simulation Tools (U.S. DOE, 2011)...... 50

Table 3-3: Comparison of the Features of Agent-based Modeling Tools [Extracted from Kravari & Bassiliades, (2015)] ...... 57

Table 4-1: Time Spent in Cabins ...... 63

Table 4-2: End-user Controls from Initial Review ...... 64

Table 4-3: Organizations Selected for the Case Studies ...... 71

Table 4-4: Contributing Factors to End-user Comfort in Various Sectors ...... 77

Table 5-1: Parameters for the Interactive Sensing System ...... 88

Table 5-2: Parameters Measured and Rating Scales ...... 92

Table 6-1: Description of Case Study Buildings...... 97

Table 6-2: Instrumentation Plan Showing the Sensors Used in the U.S. Case Study Buildings ...... 100

Table 6-3: Instrumentation Plan Showing the Sensors Used in the Qatar Office Case Study ...... 103

Table 6-4: Occupant Values and Background Information on VET ...... 107

Table 6-5: Variability of Qatar Occupant Responses on Temperature Perception ...... 117

Table 6-6: Comparison of Occupant Behavior from PMA- June 2016 ...... 120

Table 7-1: Occupants’ Response to Uncomfortable Indoor Environmental Conditions- Adapted from Hong et al. (2016) and Abraham et al. (2017b) ...... 130

Table 7-2: Possible Representation of Occupant Behavior in ABM- Adapted from Lee & Malkawi (2014) ...... 131 xii

Table 7-3: Selected ABM Studies of Occupant-related Building Energy Use with Anylogic ...... 135

Table 7-4: Occupant Types and Possible Actions Based on Level of Control ...... 141

Table 7-5: Occupant Distributions and Initial Weighting Factors based on Occupant Utility ...... 142

Table 7-6: Model Scenarios ...... 143

xiii

PREFACE

Chapter 3 contains sections of a paper that was published at the 7th International

Conference on Sustainable Development in Building and Environment (SuDBE) Conference as

Abraham, Amasyali, Anumba and El-Gohary (2015). Chapter 5 contains a version of a paper that was previously published at the International Workshop on Computing in Civil Engineering

(IWCCE) Conference of the American Society of Civil Engineers (ASCE) as Abraham, Anumba,

& Asadi (2017). Chapter 6 includes sections of a paper published at the Lean and Computing in

Construction Congress (LC3) as Abraham, Zhao, Anumba and Asadi (2017). The authors have the right to include the material in the dissertation.

xiv

ACKNOWLEDGMENTS

I will like to thank all those made it possible for me to complete my Ph.D. My appreciation goes to my advisor, Prof. Chimay Anumba, who supported me throughout this journey. Your mentorship and support have been very beneficial to my growth. It has been a period of learning, gaining a diverse set of skills, and experience. My appreciation also goes to my co-advisor, Dr.

Somayeh Asadi, thank you for being encouraging, understanding, and for providing guidance on my work. I will like to thank my dissertation committee members, Dr. James Freihaut, Prof. Lisa

Iulo, Dr. Conrad Tucker, and Dr. Nora El-Gohary for their valuable contributions to my research.

I will also like to thank Dr. Cynthia Reed for providing feedback on my dissertation and the Penn

State AE Department faculty, staff, and my colleagues for their support.

My gratitude goes to Qatar Foundation for the resources, collaboration, and funding for this research, I also thank the members of my research project team for providing a positive work environment. I will like to thank those who helped with this study including Mr. Mark Stutman of

Penn State at the Navy Yard and Mr. Corey Wilkinson for the technological support. I will like to express my gratitude to those who participated in the study and others that I had the pleasure of working with during this period.

I will like to especially thank my family for motivating and encouraging me on this path.

Most importantly, my special gratitude also goes to my parents Pastor and Mrs. Abraham, my siblings Oluwatoni, Tolulope, and Temitope, and my uncle Mr. Samuel Adeyemi and his family. I am grateful for your unwavering love, relentless support, encouragement, and constant prayers. I will also like to thank my mentor Dr. Abiola Akanmu and my dear friends and well-wishers for the tremendous support.

Thank you for inspiring me! I am so blessed you have you all in my life.

Chapter 1

INTRODUCTION

1.1. Background

Energy efficiency became a national priority after the oil crisis in the 1970s; governments began to impose mandates and call for improved energy efficiency measures (Eley, 2000). Energy consumption goals were set for different sectors and building energy management systems were introduced in the U.S. (Kazmi et al., 2014). Other countries lagged for different reasons, such as lack of awareness, inadequate resources, and unavailable technology. Over the years, end-user involvement has become more pervasive with increased monitoring and metering technologies.

In most International Energy Agency (IEA) countries, including the U.S., buildings consume about 40% of total primary energy most of which is from non-renewable sources (IEA,

2013). According to the U.S. Energy Information Administration (U.S. EIA), the transportation and industrial sectors consume less energy than the building sector, and the transportation sector has recorded a slowdown in energy demand over the building and industrial sectors (2015). The breakdown of energy consumption by different sectors is presented in Figure 1-1.

Total= 97.4 quadrillion British thermal units

Commercial 19% Industrial 31%

Residential 21% Transportation 29%

Figure 1-1: U.S. Energy Consumption by Sector in 2016 (U.S. EIA, 2017) 1

Architecture 2030 (2015) reported the breakdown of energy consumption by the buildings sector as 47.6% (45.2 QBtu) of total energy indicating that buildings in the U.S. accounted for almost half of energy consumption, the majority of which is used during the operations phase. In

2012, the U.S. consumed 18% of the world’s total primary energy with a total primary energy consumption per capita of 312 million British thermal units (Btu) in 2011 compared to the world energy consumption per capita which was 75 million Btu (U.S. EIA, 2017). Also, buildings contribute 32% of greenhouse gas emissions and about one-third of total direct and indirect energy- related carbon dioxide emissions which cause global warming and other climate change effects

(IEA, 2013). Future projections show that energy consumption will continue to rise significantly in the building sector if the trend of energy use continues at the current rate. Thus, there would be a need for energy efficiency improvements in the building sector (Perez-Lombard et al., 2008; U.S.

EIA, 2014). Regional and global organizations also emphasize the need for measures to improve building energy efficiency and reduce associated carbon emissions (IEA, 2013).

Although the U.S. consumes a large amount of energy worldwide, Qatar has the highest energy consumption per capita in the world (World Bank Group, 2018). Realizing the need for energy efficiency in buildings, various interventions geared toward reducing energy demand and increasing the use of renewable energy sources have been employed (QEERI, 2015). The U.S. had the second highest total energy consumption in the world after China; the U.S. consumes about two-thirds of the energy consumed by China even though the U.S. population is a quarter of the population of China (Enerdata, 2015). Comparing the energy consumption per capita of China and

U.S. with Qatar, it is observed that from 2015 onwards, Qatar had the highest energy consumption per capita and the highest Gross domestic product (GDP) per capita followed by the United States then China (Figure 1-2). This observation could depict a relationship between energy consumption and economic prosperity of a country.

Qatar USA China

25,000

20,000

15,000

10,000

5,000

Energy Energy use (kg of equivalent/capita) oil 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year

Figure 1-2: Energy Use Per Capita in China, Qatar, and USA (OECD/IEA, 2014)

Figure 1-3 presents the trend of GDP per capita over 14 years for the U.S. and Qatar. While the U.S. shows a decrease in energy use per capita with an increase in GDP per capita, Qatar showed an increase in energy use per capita with an increase in GDP per capita which indicates the need to address this trend of energy use in Qatar as the country experiences economic growth.

Qatar USA China

100,000

80,000

60,000

40,000

GDP/Capita ($/person) 20,000

0 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year

Figure 1-3: Trends of GDP Per Capita in China, Qatar, and the U.S. (OECD/IEA 2014)

The building industry, being the highest energy consumer of the three major sectors, provides opportunities for energy savings. The Qatar Environment and Energy Research Institute

(QEERI) was established in 2011 to support various sustainability goals including energy and water efficiency. One of the grand challenges of the Qatar Environment and Energy Research Institute is to promote energy efficiency in buildings without sacrificing the health, comfort, and well-being of the occupants in the building (QEERI, 2015). This thesis was part of a research project funded by the Qatar National Research Fund (QNRF) which was established by Qatar Foundation. Qatar is fast growing in its research and development efforts in energy efficiency at a globally competitive level (Grueber et al., 2013).

End-user values indoors should be maintained as energy efficiency is improved.

Discomfort in indoor environments can lead to reduced productivity and cause negative health impacts. Since building occupants spend about 90% of their time indoors, there is a need to maintain the preferences and values of most of the occupants (at least 80% satisfaction with thermal conditions recommended by ASHRAE) while improving the energy efficiency of buildings.

Occupant values are the things that are of worth to people in a building such as thermal comfort, lighting comfort, and good air quality. These values are mostly dynamic and can be difficult to predict but involving the end-users by interactively monitoring their preferences and behavior as well as energy use in buildings could improve understanding of the values. Various aspects of the indoor environment affect the health of the occupants such as poor lighting, thermal discomfort, and the presence of indoor pollutants. Health impacts such as sick building syndrome and building related illnesses are some of the resultant effects of poor indoor environmental conditions

(Clements-Croome, 2013). Some of the energy used in buildings is wasted through different processes including occupant-behavior related activities during the operations phase. For instance, from the CBECS survey, the energy consumption for space heating accounted for the highest energy consumption in the U.S. in 2012 (U.S. EIA, 2016). 4

Building control systems that can adapt to the dynamic needs of the occupants are necessary to improve indoor environmental conditions while reducing energy consumption. Lucon et al. (2014) mentioned the need for a holistic approach integrating all stages of the building’s lifecycle through continuous monitoring and post-occupancy evaluations (POEs) during the operations phase. This approach will provide information on how the building is performing and identify ways to bridge the gap between the intended design and the actual operation of the building.

It can also improve understanding of the occupant side of building operation.

1.2. Problem Statement

Improving energy efficiency in buildings is of high priority given the amount of energy consumed by the building sector. In countries like Qatar and the U.S. that are experiencing economic growth, energy consumption could keep increasing if measures are not taken to address these trends. Building occupants are sometimes made to trade comfort for energy savings which is undesirable. Given the health and productivity benefits that can be accrued from improved occupant satisfaction with IEQ, it is critical to ensure their comfort needs can be met. Improved comfort can curb or eliminate behaviors that lead to energy waste. Most of the approaches to building operation do not adequately account for building occupants’ individual preferences. They can be improved through end-user value-sensitive building operation. There is a need to enhance how occupant values are integrated with building systems. This study contributes to the body of knowledge to address this integration using a variety of research methods.

1.3. Research Overview

While the U.S. has made some improvements in the building sector, there are opportunities for energy savings with the existing building stock. Most new buildings are constructed according to standards that are sometimes not adequate to meet energy efficiency goals. Existing standards

are mostly prescriptive and not performance-based, so they do not sufficiently address the needs of the occupants during the operations phase. With the increasing push for more sustainable buildings, the new technologies used in high-efficiency buildings are costly to install and maintain, and the systems are sometimes not easy to operate by the occupants and the building managers. The satisfaction of at least 80% of the occupants with thermal conditions is also not guaranteed.

There are gaps in understanding how buildings consider the values and needs of end users and how building systems can be designed to meet the preferences of the occupants in residential and commercial settings. While some studies have been performed on occupancy, occupant behavior, and energy consumption, very few studies have been able to incorporate occupant values and preferences with the building systems. In this regard, it would be beneficial to investigate the approaches used in other industry sectors to incorporate end-user values and preferences in conditioned spaces such as automobiles, aircraft, and ships. Also, emphasizing the need to address individual preferences in buildings through simulation modeling approaches and developing recommendations for the building sector will be important contributions.

1.2.1 Research Aim and Objectives

This study aims to investigate the potential for the improved integration of occupant values and preferences with building systems operation while seeking to enhance building performance and occupant satisfaction indoors.

Research Objectives

1. Establish key occupant values and preferences and explore the relationship between these

values and energy consumption in buildings;

2. Investigate approaches to integrating end-user values and preferences with control systems in

conditioned environments looking to other industries (such as automotive, aircraft, and cruise

ships) and develop recommendations for buildings;

3. Undertake comparative analyses of occupant values and preferences in different indoor

environmental conditions based on empirical studies in commercial and residential buildings

in Doha, Qatar (a hot desert climate) and Pennsylvania, USA (a humid continental climate with

four distinct seasons);

4. Create end-user profiles/typologies for occupants based on their values and preferences which

can be used for the simulation of what-if scenarios using agent-based modeling (ABM) related

to the identified typologies.

The overall research framework is presented in Figure 1-4. Each task corresponds to the listed research objectives.

Task 1: Identification of building occupant values and preferences

Occupant values and relationships between the values

Task 2: Investigation of approaches used in other industries

Structured interviews Develop Case studies Analyze findings and questionnaires recommendations

Task 3: Comparative analysis of occupant behavior in different buildings

Empirical studies Comparative analysis Statistical analysis

Task 4: Creation of occupant profiles and simulation of different scenarios

Occupant typologies ABM scenarios development

Figure 1-4: Overall Research Framework

1.2.2 Research Scope

This study covers aspects of energy use in residential and office buildings and occupant values and behavior. The initial focus is on existing buildings in the operations phase, and the opportunities for tuning and improving energy use in these buildings, it could be further extended to the design phase. Occupant values and behavior are further explored in the case studies to gain an understanding of how these affect satisfaction and energy use indoors. The study looks to extract the lessons that can be learned to inform improved building operation.

1.4. Research Contributions

Occupant values are not well addressed in buildings, the main contributions of this study are to capture the needs of building occupants and propose an approach to integrate them with building systems. The research contributions are discussed in Chapter 8.

1. This research will improve understanding of the end-user preferences and values in other

industry sectors and buildings. It will provide insight into the differences between building

occupants in a hot desert climate and those in a humid continental climate.

2. As part of this research, a value-sensitive approach for integrating building occupant values

in conditioned environments is explored. Building occupants are considered based on

occupant profiles rather than occupancy using an agent-based simulation model

considering a variety of scenarios.

3. Recommendations were developed for buildings in relation to how occupant values can be

better integrated with building systems. These were deduced from the findings of the study

of other industries and the empirical energy studies.

1.5. Thesis Structure

Chapter 1 introduces the research aim and objectives and emphasizes the need to integrate occupant values and preferences with building control systems. Chapter 2 provides an overview of the research methodology and the choice and justification of research methods to address the objectives. Chapter 3 presents a literature review of the different research themes including building energy consumption and occupant behavior, occupant values, indoor environmental conditions, building control systems, and energy simulation. Chapter 4 reviews other sectors and presents studies into different industry sectors to identify the lessons that can be learned and adapted to the building industry. Chapter 5 presents the development of an interactive system for sensing building energy consumption, indoor environmental monitoring, and occupant values, preferences, and behavior. Chapter 6 examines case studies of buildings selected for interactive monitoring and presents the observations and findings from the empirical studies. Chapter 7 looks into the development of a simulation approach for value-sensitive building operation using agent-based modeling. The research contributions, recommendations, and conclusions are discussed and presented in Chapter 8.

Chapter 2

OVERVIEW OF RESEARCH METHODOLOGY

2.1. Introduction

In the field of research, various approaches are used for scientific inquiry. The three main methods of inquiry are qualitative, quantitative, and mixed methods or triangulation. In qualitative research, the subject is explored to gain an understanding of a problem without prior formulations.

Quantitative research involves the formulation of objectives from a study of literature to define the research hypothesis (Fellows & Liu, 2008). Mixed methods or triangulation use a combination of qualitative and quantitative approaches. When appropriate, mixed methods could produce robust results since the strengths of both approaches can be incorporated into the study.

The research methodology involves the strategies to obtain outcomes from the selected research methods. The quantitative approach involves the examination of variables and the relationships among variables through experiments and surveys. Post-positivist assumptions that all observations are fallible and that all observations are reversible serve as the core of quantitative approaches (Trochim, 2006). The qualitative approach involves making knowledge claims based on multiple meanings of individual experiences through narratives, case studies, or grounded theory studies (Creswell, 2003). The constructivist assumptions are based on how people come to knowledge using ethnographic design where case studies and observations are used (Taber, 2014).

Emancipatory assumptions are also used in qualitative design through narrative styles. This approach looks for ways to inspire a change in the study participants by enabling them to have a voice (Alzheimer Europe, 2009). In mixed methods approaches, the researcher uses a method that involves simultaneous data collection (both numbers and text) (Creswell, 2003) through a combination of qualitative and quantitative approaches to understand and solve the research problem in a pragmatic way (Table 2-1).

Table 2-1: Combination of Knowledge Claims, Strategies of Inquiry, and Methods (Reproduced from Creswell, 2003)

Research Approach Knowledge Claims Strategy of Inquiry Methods Quantitative Post-positivist Experimental design Measuring attitudes methods assumptions and rating behaviors Constructivist Ethnographic design Field observations Qualitative assumptions methods Emancipatory Narrative design Open-ended assumptions interviewing Pragmatic assumptions Mixed methods design Closed-ended Mixed measures and open- methods ended observations

Appropriate research methods should be implemented to ensure integrity, thoroughness, rigor, and to assess how the goals of the study can be achieved. Since the findings of different studies are intended to contribute to an improved understanding of different phenomena, the most suitable methods for the study should be selected. When human subjects are involved in a study, ethical practices should be followed. The participants should be treated with respect; their privacy should be protected while giving them the right to choose to participate or not participate in the study without penalty.

Empirical research involves conducting experiments or observations to gather facts about a phenomenon. The researcher could manipulate some variables to prove or disprove his hypothesis and gather evidence about his study (Kothari, 2004). A researcher should be objective about his study to minimize or eliminate bias. When designing a study, it is important to consider validation of the research findings regarding credibility, dependability, confirmability, and transferability

(Lincoln & Guba, 1985). Credibility relates to the trustworthiness of the results, dependability is the reliability of the findings, confirmability shows how unbiased the responses are, and transferability demonstrates how the results can be generalized on a wider scale (Trochim, 2006).

This chapter introduces different research methods and summarizes the approaches that were selected for this study.

2.2. Summary of Research Methods

In quantitative research, the data is numerical and can also be in categorical form.

Qualitative research data is non-numerical and can be analyzed using a variety of methods. Other differences between qualitative and quantitative research methods are presented in Table 2-2.

Table 2-2: Differences between Quantitative and Qualitative Research Methods (Leedy & Ormrod, 2005, p. 96) Question Quantitative Qualitative What is the purpose of the To explain and predict To describe and explain research? To confirm and validate To explore and interpret To test theory To build theory What is the nature of the Focused Holistic research process? Known variables Unknown variables Established guidelines Flexible guidelines Predetermined methods Emergent methods Somewhat context-free Context-bound Detached view Personal view What are the data like, and Numerical data Textual and/or image-based how are they collected? Representative, large sample data Standardized instruments Informative, small sample Loosely structured or non- standardized observations and interviews How are the data analyzed to Statistical analysis Search for themes and determine their meaning? Stress on objectivity categories Deductive reasoning Acknowledgment that analysis is subjective and potentially biased Inductive reasoning How are the findings Numbers Words communicated? Statistics, aggregated data Narratives, individual quotes Formal voice, scientific style Personal voice, literary style

From the table, it can be deduced that these approaches have been well developed to answer different research questions. The suitable research method will be selected based on an understanding of the nature and type of research problem being studied and the anticipated outcomes from the study. Mixed methods use a combination of these to examine and determine the most suitable ways to solve a research problem that may otherwise not be solved by solely focusing on qualitative or quantitative approaches.

2.2.1. Quantitative Research Methods

Quantitative research methods are employed in fields where numerical data is collected to analyze a problem. It involves an objective measurement of data collected in a structured way.

Some variables are selected to determine the relationships between the variables and make inferences from the data. Various tools are used to collect data for quantitative research including surveys, computer simulations, experiments, etc. Quantitative methods allow for comparisons and the methods can be replicated.

The two main types of quantitative research methods are experiments and surveys.

Experiments involve field studies or laboratory experiments while surveys consist of using participant responses based on different rating scales to gain insight into a phenomenon.

Quantitative research designs can be exploratory, descriptive or casual (McNabb, 2008). New concepts can be tested using statistical tools leading to the evolution of a study from being initially exploratory to being more descriptive and the relationships between variables to find cause and effect are tested (McNabb, 2008). Surveys are commonly used in quantitative and qualitative research. Fink (p.1, 2013) describes surveys as a method to ‘describe, compare, or explain individual and societal knowledge, feelings, values, preferences, and behavior.’ In survey research, subjects are asked questions which could be in the form of questionnaires or interviews.

Quantitative research surveys are in the form of questionnaires and sometimes interviews with close-ended responses (Trochim, 2006). Survey validity can be determined by different methods such as predictive (the survey can compare one’s ability to act a certain way), concurrent (the survey can be compared with an acceptable measure), content (if it represents the attitudes it is intended to measure), and construct validity (to determine if the survey can measure the phenomenon in question) (Fink, 2013).

2.2.2. Qualitative Research Methods

Qualitative research, on the other hand, draws from context and is emergent (Marshall &

Rossman, 2016). It requires the use of complex inductive and deductive reasoning (Creswell, 2013).

Qualitative research methods are used to study social phenomena. Qualitative researchers rely on different data sources and find patterns across the datasets to discover themes (Creswell, 2013).

This could also enable them to check for consistency in the data being collected. Concepts are developed for the research problem from the data collected. There are different typologies of qualitative research which are ethnography, narrative research, phenomenology, grounded theory, and case studies (Creswell, 2013).

In ethnography, the researcher looks at how people work together (i.e., build social organizations through their interactions) (Taylor et al., 2015). They use field-based approaches to discover ways that the study can be beneficial to them and the group under investigation. Narrative research involves story-telling and works well with a small sample. The participants’ responses are preserved as narratives to ensure the meaning is not lost during the interpretation of their responses

(Taylor et al., 2015). Phenomenology believes people act in relation to how they view the world.

Grounded theory is emergent and is derived from the data involving the creativity and intuition of the researcher (Lofland, 1995). Case studies use a detailed contextual study of a subject and can help to build upon, dispute, or challenge a theory. Different case study approaches can be aligned with a researcher’s worldview (Harrison et al., 2017). Such as realist-post positivist where the researcher maintains an objective stance, through triangulation he tries to get as close to the truth as possible. In the pragmatic-constructivist approach, the researcher assumes that reality can be constructed through meanings that are developed subjectively (Merriam, 1998). In a relativist- constructivist/interpretivist approach, the researcher plays a key role in interpreting the results and participates in the study (Harrison et al., 2017). Case study research can involve a combination of qualitative and quantitative methods. One of the limitations of case study research is the ability to 14

make generalizations. However, analytical generalizations may be possible where the logical framework of the case study can be applied to other situations (Yin, 2012).

The results collected from qualitative studies can be coded for analysis to extract the themes from the data using different manual and computer tools. Qualitative research data is collected through structured interviews, semi-structured interviews, and questionnaires. There are different validation strategies for qualitative research which include triangulation (using different sources), collaboration with participants, peer debriefing, comparison, and soliciting feedback from experts in the field (Creswell & Miller, 2000; Maxwell, 2012).

2.3. Approaches to Building Energy Management Research

Research into building energy efficiency and energy management can vary along a wide spectrum ranging from the investigation of the operation of building components to the effects of an energy policy intervention across a city. Quantitative and qualitative research methods can be employed for studies in this field. Some of the commonly used quantitative research methods in energy management are field experiments, surveys, case studies, and cross-sectional and longitudinal studies. Qualitative research methods used in energy management include grounded theory, observational studies, and interviews. In cases of energy monitoring, observational studies may be used to record observed energy-use behaviors.

Field experiments enable a study of different parameters and relationships between variables. Data for field experiments can sometimes be collected from energy meters already installed or utility bills. Depending on the scope of the study, additional instrumentation may be required to measure and collect the data needed for analysis. Data analysis can be performed using different numerical methods and statistical tools. Field experiments are suitable for energy management because it allows the researcher study various aspects of energy consumption and management in its actual operation. One of the challenges with field experiments is the cost of

equipment that might be needed and identifying individuals and organizations that are willing to use their facilities for such studies.

Surveys can be conducted to obtain the participant’s opinion on a subject. For instance, a researcher that is trying to determine consumer satisfaction with energy management practices or to test the effectiveness of energy efficiency measures may use surveys to collect feedback from respondents. Survey methods such as interviews and questionnaires can be employed using online survey tools, paper questionnaires, or telephone interviews. Survey questions should be grounded in theory or experience (Fink, 2013). Surveys can be beneficial in collecting feedback and can be useful for one-time inquiries or studies that require data collection on multiple occasions (i.e., in a longitudinal study). One of the main disadvantages of surveys is the inability to determine if the respondents are truthful in their responses and possible bias of the respondent. Through case studies, a few samples are selected and studied in detail to make inferences about the research problem. Case studies in building energy management research can also involve field experiments and surveys. Depending on the type of research problem being studied, other approaches can be used for energy management. Cross-sectional studies examine a whole spectrum to identify patterns or derive meaning from the data. Longitudinal studies focus on exploring changes in a subject matter over some time.

Grounded theory research looks to develop studies that are grounded in the context of the field being studied. Interviews, like surveys, are used to obtain opinions and perceptions of various aspects of building energy management. They can provide insight on the subject and allow the researcher to ask follow-up questions on the subject matter. The limitation of interviews is the willingness of participants to engage due to time constraints and the interviewer bias.

2.4. Approaches to Energy Modeling and Simulation Research

Energy modeling and simulation uses quantitative research approaches. A virtual model is used to replicate a real-life situation and different scenarios are developed for analysis, for comparison, and to predict the behavior of a system under different conditions and scenarios.

Energy simulation tools model and predict energy consumption and other occupant and system- related factors in buildings. Various energy modeling and simulation tools have been developed to estimate building energy consumption. They are used to observe how building loads might impact energy consumption under different operating conditions taking into account the building characteristics, occupancy, and the building systems. They are also used to simulate flows through the building. During the design phase, energy simulations can help designers decide on the selection of mechanical, electrical and lighting systems for the building. In the operations phase, it can reveal how well a building is operating when compared to the simulation model and explore ways to improve a building’s performance. Scenario analysis can be incorporated into simulations where different scenarios of occupant behavior are explored in buildings. Agent-based modeling, energy modeling and simulation, and system dynamics modeling tools are used for scenario analysis

(Kosow & Gaßner, 2008).

In modeling human behavior, simulation models and agent-based models have been used.

The ‘agents’ in the model are assigned different characteristics based on the simulation objectives.

The agent interactions and responses to changes in environmental conditions based on the rules of the system are computed by the simulation engine. Model development for energy simulations is mostly computer-based. Actual measurements may be used to validate the model outputs.

2.5. Choice and Justification of Research Methods

Marshall and Rossman (2016) stated that the research questions and conceptual framework of the study should inform the selection and justification of the research methods. While selecting

the research methods, attention was paid to the ethical issues that might arise during the study since it also involved human subjects. Institutional Review Board Requirements were followed to ensure the study used the right procedures. For the research process, mixed methods were found to be suitable since the research problem involves a combination of quantitative and qualitative research methods and data types. The conceptual framework for the research is presented in Figure 2-1.

Objective 1 Quantitative Determination of end-user values in Surveys buildings

Objective 2 Mixed methods Develop recommendations for Semi-structured interviews and buildings based on findings from survey questionnaires

other industry sectors Improving Improving the integration of Objective 3 Quantitative Empirical energy studies Experimental data and variables

Objective 4 Quantitative Development of occupant profiles Statistical analysis, profile

and ABM development, and simulation Research Problem: ResearchProblem:

occupant values and preferences with buiding systems Figure 2-1: Conceptual Framework for Research Methodology

This research primarily follows quantitative (i.e. experiments, surveys) and qualitative (i.e. case studies and interviews) research approaches. Here, empirical measurements and observations are made on selected variables in the building and corresponding human behavior or response is tracked through sensors and obtained using surveys. Deductive reasoning is used to verify theories in the study. For instance, to identify the relationship between energy use and occupant behavior, inferences can be made about the population being studied, but not in an attempt to generalize the results. The longitudinal surveys were analyzed using statistical and analytical methods. Various factors influence the methodologies employed in this study such as the economy of the research design where reliable but cost-effective equipment was used. It was also important to ensure

minimal disruption to participants and their environments by installing less intrusive equipment.

Different data collection methods were used including web-based and digital applications, surveys, interviews, and measurements. Objective 1 provides a foundation for the study. Objective 2 looks into other industries (i.e., the transportation sector) and draws out lessons for the building industry.

Objective 3 involves empirical studies using case study buildings to determine different energy use characteristics, while objective 4 explores agent-based modeling for occupant-related energy use behavior. The four main research tasks are related to the research objectives and are presented in

Figure 2-2.

Task 1: Identification of building occupant values and preferences

•Literature review •Occupant feedback questionnaires

Task 2: Investigation of approaches used in other industries

•Literature review •Case studies •Structured interviews •Document analysis

Task 3: Comparative analyses of occupant behavior in different scenarios

•Literature review •Empirical energy studies •Comparative techniques •Longitudinal studies •Statistical analysis •Feedback survey application

Task 4: Creation of occupant profiles based on their values and behavior

•Literature review •Occupant profiles •Simulation approach

Figure 2-2: Research Tasks including Data Collection Approach

Task 1: Identification of building occupant values and preferences important for improving building energy efficiency.

This task was based on a review of the literature and a survey of building occupants through questionnaires to identify their key values. It involved the use of a value elicitation tool through an online survey questionnaire. The questions were ranked based on the importance level of values and satisfaction with values. This task is addressed in Chapters 3 and 6.

Task 2: Investigation of approaches for integrating end-user values and preferences in conditioned environments.

An outline of the steps taken to complete this task is shown in Figure 2-3. This task was completed through an initial literature review to identify methodologies in place to account for end- user values in the automobile, aerospace, and shipbuilding industries. Industry experts in the automobile, shipbuilding, and aircraft sectors were asked to complete questionnaires and participate in semi-structured interviews on how the integration of end-user values with indoor environmental systems is achieved.

Literature review

Semi-structured interviews

Sort responses and validate

Extract lessons learned

Develop recommendations for buildings

Figure 2-3: Process for Industry Sector Interactions to Develop Recommendations for Buildings

Referrals to relevant literature and sources were requested during the interviews to refine the initial literature review. Interviews provided the means for soliciting information from the respondents. Questionnaires were also beneficial for collecting information from those that were unable to participate in the interviews. The questions were then sorted and checked for errors. The lessons learned were extracted and recommendations for buildings were provided.

Task 3: Comparative analyses of occupant responses and behavior in different indoor environmental conditions.

Several subtasks are assigned to Task 3 (Figure 2-4). Various research methods were employed including literature review, empirical energy studies (energy monitoring experiments) using three case study buildings, comparative methods, statistical analysis, online surveys, and simulations. During experimentation, treatments or interventions were applied for a period to solicit responses and determine the impact of indoor conditions on occupant behavior. For instance, the supply air temperature was altered in the case study buildings to observe occupant behavior in different indoor environmental conditions. An outline of the steps taken for the empirical energy studies is shown in Figure 2-4.

Instrumentation and Experiments- Data Analysis and Building Survey Pilot Studies Building Energy Implications of Selection Development Monitoring Data Collected

Figure 2-4: Research Steps for Task 3

Building Selection

Three case study buildings were selected for this research. They include one residential and one commercial building in Pennsylvania, USA, and one commercial building in Doha, Qatar.

A few buildings are considered for this study, but several parameters are examined for each building to get an in-depth understanding of occupant behavior, IEQ, and energy consumption (Fellows &

Liu, 2008). The selection criteria for the buildings and further details about the case study buildings are presented in Chapters 5 and 6 of this thesis.

Instrumentation Plan and Survey Development

An interactive sensing approach was used comprising energy measurement devices, sensors for monitoring indoor environmental conditions, and an occupant feedback system

(Appendix D). The Preference Monitoring Application (PMA), the feedback system, was developed using Qualtrics® a survey development tool. It was important that the questions avoided the use of biased words so that the respondents were not coerced to answer the survey in a particular way. Incentives and interventions were used to keep the respondents interested and the incentives served as a reward for their time and effort in completing the surveys.

Pilot Studies

Pilot studies were conducted to test the equipment installations and make modifications to the instrumentation plan and the Preference Monitoring Application (PMA). The equipment installed for measurements were adjusted based on the initial pilot studies. The PMA was pilot tested for content validity, flow, and ease of understanding the questions. It was also tested before being fully administered, for clarity, and to make sure the right information was captured as intended by the researcher (Fink, 2013).

Experiments for Building Energy and Environmental Monitoring

This study involved three significant areas for interactively measuring energy use (Figure

2-5). The first is energy use measurements where measurements of energy consumption by primary end-use categories (e.g., lighting, plug loads, heating, and cooling) were undertaken through sub- metering with sensors and data loggers at regular intervals.

The second set of measurements was for the indoor and outdoor environmental conditions, which involved measurements of the indoor temperature, relative humidity, carbon dioxide, air quality, and illumination level. The outdoor temperature and relative humidity were also considered and data was collected from local weather stations. These measurements were obtained using the sensors and data loggers, typically at 15-minute intervals. The third component was the occupant feedback application- PMA which is a survey that contained questions on the occupant’s perception of the indoor environment, their satisfaction, and their energy use behavior.

Energy Use Measurements (Plug loads, Lighting, Heating and Cooling)

Indoor and Outdoor Environmental Condition Occupant Feedback Monitoring Survey (i.e. Temperature, Humidity, (Interactive monitoring- Carbon dioxide and Particle PMA) Count)

Figure 2-5: Components of Building Energy and Environmental Monitoring System

Task 4: Creation of occupant profiles and agent-based simulation approach.

For this task, occupant profiles were created from the measurements of indoor environmental parameters and the surveys. An agent-based simulation approach was explored for different indoor conditions using a variety of ‘what-if’ scenarios. Scenario analysis was selected to observe occupant interaction with building controls in different indoor environmental conditions.

An evaluation of the proposed approach to integration was completed through questionnaires and one-on-one interviews with building operations professionals to solicit their feedback and suggestions for the developed approach.

2.6. Approaches to Data Analysis

The analysis for this study involves a combination of methods since it involved different types of data (Table 2-3). Some software tools were used for the data analysis. These tools vary in complexity and functionality. For instance, simulation methods, numerical modeling, and statistical methods can be used to analyze the different types of data collected.

Table 2-3: Approaches to Data Analysis for Research Tasks Data Type Data collected Approach to data analysis

Task 1 Quantitative Survey Descriptive statistics

Task 2 Qualitative Surveys and interviews Thematic analysis Quantitative Descriptive statistics Inferential statistics

Task 3 Quantitative Occupant feedback Descriptive and inferential statistics Energy use Regression analysis IEQ parameters Comparative data analysis Outdoor parameters Time series analysis

Task 4 Quantitative Profile creation and Theoretical modeling simulations Descriptive and inferential statistics

Survey data is analyzed using a variety of methods, the type of data influences the selection of analysis method. Most of the survey questions in the empirical energy studies used an ordinal scale. A Likert scale is an example of an ordinal scale (Fink, 2013). Non-numerical data was coded for further analysis and more straightforward interpretation. Internal consistency of a survey is tested using the coefficient alpha- Cronbach’s Alpha. Content analysis is used to find common themes in responses to open-ended surveys. It requires coding the comments and responses to open- ended survey questions. Survey data can be analyzed using descriptive statistics providing an overall/general interpretation of the results. Longitudinal case studies are used in Task 3. The case studies have durations of about one year but subsets of the data are presented in the analysis. Several data types are collected in parallel including occupant feedback. A variety of analysis methods are

employed to make inferences from the data collected. Descriptive statistics include the measures of central tendency and the measures of variation. The Spearman rank order correlation can be used for categorical data. It represents the degree of association between different ranks.

Exploratory data analysis reveals the structure of quantitative data while descriptive statistics show the distribution of the data collected. Experimental data from the empirical studies were preprocessed to identify outliers and missing data using spreadsheets. Several dependent and independent variables were considered. The independent variable is what we manipulate, the treatment (x), the dependent variable is the outcome (y) (Trochim, 2006). An example of a dependent variable is energy use while the independent variable is indoor temperature. When treatments such as changes in temperature are applied, the impact on energy consumption and behavior can be determined. Statistical analysis methods are used to test continuous and categorical data. Regression and correlation analysis is used to determine the relationship between two variables. Time series data from the longitudinal experiments enables the observation of changes in behavior and conditions over a period. The sampling interval is a good indication of the type of analysis that can be undertaken. A small sampling interval provides more granular information and will give detailed data while a large sampling interval provides overall high-level information about the parameter being measured. The energy use and indoor environment data were fused with the occupant feedback from the PMA for the time they responded through codes developed in

MATLAB® to develop occupant profiles. The outliers were identified, and the distribution of data was plotted to determine comfort ranges for each parameter.

The validity of the measurement reflects how close the measured results are to the expected results (Leedy & Ormrod, 2005). The data should be reliable and repeatable. Deductive logic starts with a premise that is accepted as truth while inductive reasoning starts with an observation and samples can be used to draw conclusions about the population where the sample came from (Leedy

& Ormrod, 2005). Validity can be tested by a panel of experts, using a multi-trait method to measure 25

different characteristics. Internal validity deals with how well the results allow accurate conclusions to be drawn from the study. External validity looks at how well the conclusions can be generalized beyond the study (Leedy & Ormrod, 2005). Evaluations were completed with experts to collect feedback on the proposed approach.

2.7. Summary

This chapter introduced quantitative, qualitative, and mixed research methods. Following this introduction, the methods that were employed to address the research problem and the four research objectives were discussed. The choice and justification of research methods for each task related to the research objectives were described. The following chapter is a review of literature comprising different themes related to end-user values in conditioned environments.

Chapter 3

END-USER VALUES IN CONDITIONED ENVIRONMENTS

3.1. Introduction

End-user values should be better addressed indoors to ensure comfortable conditions for occupants. Many factors influence occupant comfort indoors including the building operation, outdoor weather conditions, and energy use behavior. Efforts to improve occupant satisfaction indoors do not always yield expected results due to inadequately addressing occupant-related factors. Also, the benefits of incorporating end-user inputs in the design process have been recognized by different industries. The end users are important stakeholders in the building design process, and their expectations should be managed and addressed during the design phase. In buildings, this may not always be possible but identifying a means to better incorporate end-user preferences during building operation may reduce the likelihood of energy wastage related to energy use behavior. Given the amount of time people spend indoors, occupants should feel comfortable and healthy. Achieving 100% comfort may not be feasible for all occupants, so

ASHRAE recommends at least 80% satisfaction with thermal conditions (ASHRAE, 2004).

Occupant values are defined in this study as the parameters that are important for the comfort and well-being of people indoors such as the thermal conditions, lighting, and air quality. Occupant comfort is a primary goal in a building since poor conditions could negatively impact the occupant’s health and productivity indoors. Building energy and environmental monitoring has several benefits including tracking of energy consumption, for fault detection and diagnosis, and to identify opportunities for energy savings.

During the design phase, virtual prototypes and mock-ups are created for visualization and to identify the most efficient designs. Energy simulation tools such as eQuest, EnergyPlus, and

TRANE are commonly used to model flows and estimate energy consumption by building heating,

ventilation and air conditioning (HVAC) systems. These simulation tools address some aspects of occupant-related factors but mostly model occupants collectively (as occupancy) and simplify their preferences (Clarke, 2001). One of the approaches that have been used to represent occupant factors in virtual models is agent-based modeling (Zhang et al., 2011; Kara & Baxendale, 2012). This chapter reviews different aspects of building energy monitoring, controls, and occupant-related factors in buildings, highlighting the potential for improved integration of end-user values and preferences with building systems (Abraham et al., 2015). The following sections present a review of studies covering different aspects of occupant values in relation to indoor environmental conditions and building energy consumption.

3.2. Building Energy Consumption

Building energy monitoring started as far back as 1973 during the Middle East oil embargo

(Eley, 2000). Building systems work together to provide the preferred indoor conditions for occupants such as the mechanical systems, electrical systems, and fire alarm systems (Grondzik &

Kwok, 2015). Some of these systems directly affect the indoor environmental quality. A considerable amount of energy is consumed during the operations phase of a building, and associated greenhouse gas emissions from the building sector are also quite high - about 12% in

2015, and globally, CO2 emissions are about 76% of total greenhouse gas emissions (U.S. EPA,

2015). Decreasing building energy demand by using passive strategies have been employed in different countries and mandates have been set to minimize building energy demand in various countries (Grondzik & Kwok, 2015; Lucon et al., 2014).

Countries in the Gulf Cooperation Council (GCC), of which Qatar is a member, experienced increasing energy consumption over between 1972 and 2012 (Wogan et al., 2017). In

2014, Qatar had the highest energy consumption and CO2 emissions per capita in the world while the U.S. had the 10th highest energy consumption per capita (World Bank Group, 2018). Of the

major industry sectors, buildings are the highest energy consumers in the U.S. and they contribute significantly to greenhouse gas emissions while 60% of the electricity generated in Qatar is used by buildings (QEERI, 2015). Residential and commercial buildings in the U.S. account for 21% and 19% of primary building energy consumption respectively (Figure 1-1). According to the U.S.

Department of Energy (U.S. DOE), the U.S. building stock is about 120 million residential buildings and 5 million commercial buildings (4% of the building stock) (U.S. EIA, 2016). The

Qatar residential building stock accounts for about 86% while commercial buildings account for about 12% of the buildings (Statistics Authority, 2010).

According to Yu et al., (2011), different factors influence energy consumption in buildings namely climatic conditions, building and occupant-related characteristics, building systems and operation, social and economic factors, and indoor environmental quality. Energy used in buildings is derived from various renewable sources such as hydro, wind, and solar and non-renewable sources such as coal and natural gas. In most countries, the building sector continually strives to reduce its consumption by switching to renewable energy sources, improving the efficiency of building systems, and improving overall performance. Qatar set a goal to achieve 20% energy generation from renewables by 2030 and 20% reduction in per capita electricity consumption by

2017 (Myrsalieva & Barghouth, 2015; Wogan et al., 2017). A few other strategies have been employed to reduce energy consumption by buildings. Sustainable and high-performance buildings use different techniques including passive strategies to reduce building energy demand and proactive building energy management (Crawford et al., 2016). Building systems that account for end-user values can provide energy savings when the systems are right-sized for the building type, occupancy type, and building context.

Identifying what the end-users want, when possible, and integrating these to the design of building control systems has the potential to improve occupant satisfaction. Building systems should be more sensitive to the needs of the occupants to promote healthy environments and 29

improve productivity. Understanding the values and preferences of the end users can help building operators determine the preferred indoor settings and appropriately address occupant needs.

Building energy monitoring helps engineers ascertain if a building is functioning efficiently and consuming an acceptable amount of energy (Eley, 2000). The main aim of building energy monitoring is to reduce operating costs and energy use. It could also increase occupants’ awareness of how much energy they use and help them make better decisions about their energy use behavior.

3.2.1. Overview of Energy Consumption by Various End-use Categories

Building energy consumption is divided into various end-use categories. Space conditioning contributes the most to energy consumption in U.S. buildings ( U.S. EIA, 2016). The breakdown of energy consumption in single-family homes [Figure 3-1(a)] and office buildings

[Figure 3-1(b)] shows that space heating accounts for the highest energy consumption in both building types. By using more efficient equipment and optimizing the operations with control systems, energy savings can be achieved. Qatar has a hot arid climate and the majority of energy consumed in buildings is for air-conditioning. The selection of heating, ventilation, and air- conditioning (HVAC) systems in buildings depends on the climate, the type of conditioning needed

(heating only, cooling only or both), the size of the space to be conditioned, and energy source.

Lighting sources for buildings have improved in efficiency over the years since the development of energy saving lamps. Lighting accounts for 10% of total energy consumption in commercial buildings which is a reduction in energy use by lighting from 2003 (U.S. EIA, 2016). The types of light bulbs are incandescent, light-emitting diodes (LEDs), fluorescent, compact fluorescents

(CFLs), and halogen. LEDs and CFLs are more energy efficient. Switching to energy efficient lighting is one of the ways to realize immediate energy savings in buildings. Lights levels can be adjusted in a space using dimmers or occupancy sensors to control operation by detecting occupant presence and adjusting accordingly.

25% 30% 32% 41%

10% 5% 7% 12% 18% Total 1.240 17% Total 8.145 3% Quadrillion Btu Quadrillion Btu Space heating Water heating Cooling Space Cooling Ventilation heating Refrigerators Other Water Lighting Other heating

(a) (b) Figure 3-1: Breakdown of U.S. Energy Consumption (a) Single Family Home- 2009 (U.S. EIA, 2013), (b) Office Energy Use- 2012 (U.S. EIA, 2016)

With technological advancements, the use of portable plug load devices is on the rise but most of the newer devices are energy efficient rated (i.e., ENERGY STAR). Some large appliances use up a lot of energy while others are low energy consumption appliances and are categorized as miscellaneous appliances. Some plug load devices are simple products which are infrequently used and have no power when not in use (e.g., electric toothbrush), while others are discrete end-use products and have a standby operating mode using vampire loads when on standby mode (e.g.,

TVs, microwaves, computers) (E3, 2012). Plug loads can be measured individually at the receptacles (using plug load meters) or at the panel board (using current sensors).

3.2.2. Impact of Human Behavior on Building Energy Consumption

Occupancy levels impact energy consumption, Azar & Menassa (2012) analyzed the impact of different occupancy parameters on energy consumption and the sensitivity of the model varied based on the building size and the weather conditions. Occupant behavior is dynamic, stochastic, and difficult to predict. Various fields of study have used computing and other advanced analytical approaches to model human behavior and address end users in buildings. Occupant 31

behavior models such as movement and presence models and action models have been developed

(Gaetani et al., 2016). Virote & Neves-Silva (2012) proposed a stochastic model for occupant behavior in buildings for energy and to model space occupancy patterns. They found that they can capture space utilization and predict different scenarios. Hoes et al. (2009) assessed the impact of occupant behavior, presence, and interaction with building systems on energy consumption. They emphasized the importance of addressing occupant behavior in buildings and the potential for modeling approaches to improve how this can be predicted in buildings. Other studies have also observed a strong correlation between occupant behavior and energy consumption (Clevenger &

Haymaker, 2006; Yu et al., 2011; Bonte et al., 2014). Different approaches have been explored to modify energy use behavior in buildings such as through incentives, education (Ebinger et al.,

2010), encouraging conservative habits, and providing the occupant with feedback on their energy consumption. Lucon et al. (2014) listed various behavioral impacts on energy consumption in buildings. They mentioned the fact that energy use in buildings of similar functions can vary by up to a factor of 10. The need to design building services to meet the occupant requirements, behavior, and values have been discussed (Lucon et al., 2014). The study also listed the energy savings that can be achieved in buildings through changing the usage of some appliances, for instance, they estimate a possible 70% reduction in lighting energy use by just turning off lights that are not needed. 10-30% energy savings can be achieved for space conditioning by adjusting thermostats to be 2°C cooler during the heating season. In addition to the energy wasted through occupant behavior, a lot of energy is also wasted during non-occupied hours. Masoso & Grobler (2010) found that more energy (56%) was used during the non-occupied hours than during the occupied hours

(44%) in hot and dry climate regions. Therefore, operational efficiency and behavioral impacts related to equipment that was left running during unoccupied hours are also important for monitoring.

The comfort needs of occupants indoors include thermal comfort, visual comfort, acoustic comfort, and air quality. Actions of the occupants include their interaction with the building systems to meet their comfort needs or adjust the environmental conditions. People sometimes report their discomfort when they have the opportunity. In some situations, they do not take any action and may leave the space when they are too uncomfortable. They could also engage in other activities to improve their comfort. Various factors such as social and economic factors, culture, indoor environmental quality preferences, and occupant activities contribute to end-user behavior and these factors depend on the values and background of building occupants (Steg, 2008;

Frontczak & Wargocki, 2011). The study of energy use behavior of building occupants is beneficial for modeling energy consumption. End-user behavior focuses on the decisions and actions building occupants engage in within a building related to energy consumption. Energy savings can be realized by changing human behavior toward energy use (Clevenger & Haymaker, 2006; Ebinger et al., 2010; Kavulya & Becerik-Gerber, 2012; Nguyen & Aiello, 2013). Providing building occupants with information on energy consumption levels has the potential to influence their energy consumption patterns (Seligman et al., 1977; Steg, 2008; Onyango & Ciaran, 2014). It could increase awareness of their energy use habits and instill a sense of environmental stewardship in occupants.

Studies by the International Council for Research and Innovation in Building and

Construction (CIB) show that occupants are dissatisfied with indoor environments in modern buildings, so improvements in building energy efficiency and use of modern controls should consider the values and behavior of occupants (ISIAQ & CIB, 2004). People may express dissatisfaction with their indoor environmental conditions by adjusting the HVAC systems to improve comfort, overriding building control systems, adjusting thermostats, or using additional equipment (e.g., portable heating equipment) whenever possible causing an increase in the cost of operating and maintaining the building (Williamson et al., 2010). Providing the right indoor 33

environmental conditions and giving occupants control can improve satisfaction (Frontczak &

Wargocki, 2011) but may increase energy consumption.

Hong et al. (2000) investigated energy use through occupant-behavior-related data collection. They highlighted the importance of understanding human behavior through data analytics, data mining, and modeling and improving building performance through behavioral solutions. The drivers, needs, actions, and systems (DNAS) occupant behavior framework was developed, and it lists drivers behind energy-related occupant behavior, occupant needs, their actions and the systems they interact with (Hong et al., 2015a; 2015b). Occupant profiles can be created based on their attitudes, values, and behavior. They also listed the data needed for occupant behavior studies which include weather data, space, energy, and occupant data. Carmenate et al.

(2015) used a non-invasive approach with infrared sensors in a residential home to track and collect data on occupant presence and occupant distribution. The system collected data on occupant presence but did not identify what the building occupants were doing. It was observed that energy waste was reduced for the periods when occupants were not present in the space.

3.2.3. Energy Use Measurement and Monitoring in Buildings

Energy use can be quantified through empirical measurements, calculations, simulation, or a combination of methods (Wang et al., 2012). Residential and commercial buildings increasingly rely on automated energy management systems to monitor, manage and control energy use. In many countries, there are mandatory metering requirements to track consumption and ensure performance targets are being met. Some of the drivers of energy metering and environmental monitoring include the need to mitigate climate change and provide feedback to the occupants

(Ahmad et al., 2016). Building energy monitoring tools can be used to track and control heating, cooling, lighting, and other building systems that consume energy. Studies have shown that providing building occupants with their energy consumption data could cut energy consumption

by about 20% (NAED, 2011). Smart grids have been employed in recent years and they manage energy consumption profiles to reduce energy demand (LaMarche et al., 2012). Smart meters can be integrated into an energy network to allow users obtain real-time information about their energy use. In buildings, Advanced Metering Infrastructure (AMI) is the measurement and data collection system that includes a meter at the customer site, communications networks between the customer and a service provider (utility), and data reception and management systems that provide the information to the service provider (Simchak & Ungar, 2011). Booth et al. (2010) stated that by

2019, AMI and smart meters would enable utilities to reap operational benefits of about $9 Billion and could bring about significant energy and cost savings. Advanced meters are required to record at hourly and daily intervals, and can be used to measure electricity, gas, and water (Sullivan et al.,

2011). These devices measure energy at whole building or district level while energy consumption within a facility can be performed using sub-metering devices.

A variety of sensing solutions are used for the monitoring of building energy consumption indoors. The tools vary in cost and complexity and have varying functional capabilities. The sensor manufacturers have different operating protocols for the operation and use of these sensors. The objectives of the monitoring should determine the selection of the sensors for energy measurement deployment. The selection of sensors is further discussed in Chapter 5.

3.3. Occupant Values and Indoor Environmental Conditions

The indoor environment comprises various environmental factors such as air quality, thermal comfort, acoustical quality and visual or lighting quality (Bluyssen, 2006). Humans can communicate how they feel through their senses and tell whether they are comfortable, uncomfortable or whether the environmental conditions are acceptable to them (Bluyssen, 2006).

People sometimes respond to discomfort by taking actions to adjust or control their environment like wearing additional layers of clothing, heating, or cooling a space, etc. Several factors make the

indoor environmental conditions conducive to the health and well-being of building occupants including having thermal comfort, good air quality, adequate lighting levels, good ergonomics at the workplace, spatial settings, and no unwanted noise (Clements-Croome, 2013; Bluyssen, 2010).

Identifying occupant values can help designers of building systems and controls to understand better how to meet the needs of different users. A survey conducted on about 600 residential and office building occupants in three states in the U.S. by Amasyali and El-Gohary

(2016) identified values that are important to building occupants and ranked these values in order of their level of importance. For the mean importance ratings for office buildings in Pennsylvania, the occupants ranked health highest, followed by personal productivity, then indoor air quality, visual comfort, thermal comfort, and environmental protection. Energy cost savings was ranked the least important. While health and personal productivity contributes directly to the well-being of the occupants, most occupants responded that environmental protection and energy cost savings were not that important. Improving the top five values that were of importance to them could impact the other two values (environmental protection and energy cost savings). Since these occupant values have been highlighted, the means of maintaining them is an important area for further research.

Uncomfortable conditions can negatively affect the health and well-being of building occupants (Clements-Croome & Li, 2000) which causes them to engage in behavior that leads to energy waste. Federspiel (1998) identified that most of the complaints by occupants are mainly due to thermal sensation complaints from HVAC system operation. HVAC system designs in the U.S. and most countries take account of the factors laid down by the ASHRAE standards. Most designs are compared to baseline conditions that do not accurately address the dynamic behavior of building occupants. Bluyssen (2006) created a framework for health and comfort that contributes to knowledge on how human needs can be accounted for indoors. It was recommended that a combination of human models and performance indices that combine health and comfort should be 36

used. Also, the indoor environment and its parameters, followed by building controls that anticipate and respond to changing requirements can give a holistic and integrated indoor environment management system. The recommended system is intended to cover the interactions between occupants and the environment. Assessment of indoor environmental conditions should start from what humans need and not prescribed standards that do not adequately address end-user preferences.

3.3.1. Defining Value

Value can broadly be defined as the ratio of benefits accrued to the cost required to achieve the benefits (CIOB, 2017). In this context, value is defined as the things that are considered as important or of worth to the building occupants in relation to their comfort indoors. Value should be accrued at all stages of the life cycle of a project providing the social, economic and environmental benefits to stakeholders. The values of building occupants should be defined as early as possible in a project to establish how they can be met during the building operation (use and maintenance) which is, most times, the longest phase in the lifecycle of a building (Figure 3-2).

In buildings, occupants may derive value from having the right level of control over the thermal conditions. During the use phase, a considerable amount of energy is consumed for the operation and maintenance of buildings. There are also associated environmental impacts during this phase. Building operations take up about 42% of U.S. energy consumption (Architecture 2030,

2015). Indoor environmental conditions can impact the health and productivity of the occupants.

People spend about 90% of their time indoors, the majority of this time is spent in residential buildings (68%), and a good amount of time is spent in office buildings (Klepeis et al., 2001).

Figure 3-2: Life Cycle Stages of a Building

3.3.2. Value for Building Design and Operation

Boztepe (2007) explored the definition of user value in design. One of the key features is the evolution of design from just providing basic form and information to enabling user experiences. Across different spheres of industry, there has been an increasing need to provide end- user value for business success and to maintain competitive advantage (Boztepe, 2007). Even as other industries look to add value to customers through their products, the building is also a product and should be able to enhance the experience of the stakeholders especially the occupants who are the end users. The primary attributes that distinguish a successful product are the provision of value to its end users and guaranteed user satisfaction. Different factors influence the user experiences and one way to understand how to provide value to the end user is by collecting their preferences and incorporating them into the design. In engineering design, human input is essential for the determination of suitable inputs to be used in analytical models, for the assessment of the value of 38

a measure, and for making decisions on options to be selected for designs (Hazelrigg, 1998).

Building controls can be configured to respond to the occupant values by using occupant-related data as inputs and using analytics to learn and adapt to meet occupant needs continuously.

The key end-user values for indoor environmental design and operation identified from the literature are listed below (Amasyali & El-Gohary, 2016): (1) Values that may impact energy use behavior and energy consumption level- thermal comfort, lighting/visual comfort, and indoor air quality, (2) Values that may be impacted by the indoor environmental quality- health and personal productivity, and (3) Values that may motivate enhanced energy use behavior towards reduced energy consumption- environmental protection and energy cost saving. The importance attached to each value influences behavior and sometimes a gap exists between human values and preferences and the indoor conditions.

3.3.3. Defining Occupant Values

Thermal comfort: is defined as the “condition of the mind that expresses satisfaction with the thermal environment” (ASHRAE, 2004, p. 4). Thermal comfort models that have been developed include the predictive mean vote (PMV), percentage people dissatisfied (PPD) developed by

Fangers, and the adaptive comfort model (de Dear & Brager, 1998; Humphreys et al., 2016).

Fangers mentioned six factors that influence thermal comfort namely air temperature, humidity, mean radiant temperature, air velocity, clothing, and metabolic rate (ASHRAE, 2004). Humphreys et al., (2016) highlighted some of the concerns with the PMV/PPD thermal comfort model including its inability to account for people that have comfort temperatures that are outside the range (i.e. people that live in hot climates). Using the PMV requires information about the clothing and activity levels of the occupants and continuously collecting that information is not always feasible. An adaptive model was developed for thermal comfort since it was observed that people

have a wider range of comfort than addressed by design standards (de Dear & Brager, 1998; 2001).

Energy is used in the operation of mechanical HVAC systems to adjust the thermal environment.

Visual comfort or lighting comfort: is influenced by the quantity and distribution of light. It is determined by parameters such as illuminance, luminance levels, color, and glare (Veitch &

Newsham, 1998). Visual comfort means having adequate lighting through natural and artificial lighting sources. Lighting comfort can be determined by the absence of glare, adequate daylighting, and having outside views (Autodesk, 2017). Lighting comfort has been linked to productivity and performance (Gligor, 2004).

Indoor air quality: is the cleanliness and freshness of the air. Poor air quality can cause health effects such as asthma. Inhaling tiny particulate matter (PM) can cause adverse health effects which are difficult to detect, the health effects are worse in the presence of PM2.5 and PM10. Airborne PM is ‘dangerous’ and total volatile organic compounds (TVOCs) can also have harmful health effects on humans (U.S. EPA, 2016). Microbes are environmental organisms that can impact health. There are health and productivity benefits from providing conducive indoor environmental conditions for building occupants. ASHRAE standard 62 defines the ventilation requirements to ensure good air quality in buildings (ASHRAE, 2010).

Perceived health: can be impacted by the IEQ. Sick building syndrome (SBS) and building-related illnesses are some of the terminologies that describe the health effects related to buildings. Fisk

(2000) reviewed the health and productivity gains from better indoor environmental conditions and found that, in 2000, the estimated potential savings from reducing respiratory disease was about

$14 billion and about $30 billion from reduced SBS. Different health symptoms have been linked to SBS such as irritation of the eyes, headaches, tiredness, and fatigue, irritation of the nose, etc.

(Fisk, 2000). Fisk also reviewed studies that examined the linkages between thermal conditions and lighting on performance indoors and he found significant positive relationships between them. SBS

affects the indoor air quality that can impact perceived health. Building-related illnesses affect the indoor conditions and are associated with the time people spend in a building.

Personal productivity: can be affected by the indoor conditions. Absenteeism is linked to the IEQ

(Clements-Croome, 2013). A person’s frame of mind can be affected when the indoor conditions are not conducive. In the United States, the financial gains from achieving good IEQ in a workplace are estimated to be up to USD 700 per employee/company/year since staff are more productive and have fewer days off work due to illness (Fisk & Seppänen, 2007; Lucon et al., 2014). There are associated benefits of improving energy efficiency on workplace productivity through fewer lost work days due to building-related illnesses (i.e., respiratory allergies, less stress and improved performance from improved IEQ).

Environmental protection: relates to the concern the occupants have for the environment and managing resources while being mindful of the impact of their activities and decisions on the environment. This can be at a personal or organizational level. The Environmental Protection

Agency (EPA) is an organization that was established in the United States to protect human and environmental health.

Energy cost savings: describe the importance people attach to the cost of energy and whether they care about saving on their energy costs. Residential and office occupants may have different perceptions since most residential occupants are responsible for their energy consumption while office occupants do not pay utility bills but may still care about their energy use.

Occupant values can be assessed through post-occupancy evaluations (POE) where the end users provide feedback on their satisfaction with different aspects of the indoor environment.

Satisfaction is the utility a person derives from something and it depends on a persons’ unique preferences and perceptions (Andrews et al., 2011). Building standards have recommendations for different indoor environmental parameters as presented in Table 3-1.

Table 3-1: Recommended Levels for Various Indoor Environmental Parameters (ASHRAE, 2010; Autodesk, 2017)

Recommended Recommended Values Values Parameter Occupied Unoccupied Unit Temperature 20- 27 (68-81) 12.8- 37.2 (55-98) °C (°F) Thermal Comfort Humidity Maximum 65 - %

Carbon Dioxide <800* - ppm Indoor Air (CO2) Quality Particulate Matter <20 - µg/m3 (

*Estimated levels (different for each person)

3.4. Building Automation and Control Systems

Building control systems monitor indoor environmental conditions and are configured to respond based on specified criteria to meet the comfort needs of the occupants while also minimizing energy waste (CIBSE, 2009). Controls are integrated with mechanical, electrical, and plumbing (MEP) systems in buildings. Examples of components with controls in buildings are presented in Figure 3-3. Indoor climate control systems can be automatic or manually operated allowing for end-user control.

Heating

Lighting Cooling

Building Automation and Power Control Systems Ventilation Monitoring

Windows Hot Water

Figure 3-3: Building Automation and Control Systems 42

Occupant-driven parameters can impact energy use in buildings and the study of these parameters is beneficial for the improvement of building operation and controls (Clevenger &

Haymaker, 2006). Energy is sometimes wasted when controls are not properly configured or operated as designed. Building control systems can be more accurately modeled with user-behavior and values as inputs (Klein et al., 2012; Yang & Wang, 2013). Building automation systems consist of sensors which measure the parameters, the controller which is the brain of the system and determines the response based on the sensed parameters, the output device (actuator) which acts based on the controllers, the communication protocol which enables communication within the network, and the dashboard that reports information on the data (Grondzik & Kwok, 2015).

3.4.1. Types of Building Control Systems

There are two broad categories of controllers namely conventional and intelligent controllers (Levermore, 2000; Shaikh et al., 2014). Conventional controllers are proportional integral and proportional integral derivative (PID) and they are mainly focused on energy savings.

Intelligent controllers use learning methods, model predictive control, and agent-based controls to account for occupant preferences (Yang & Wang., 2013; Shaikh et al., 2014). Adaptive, predictive, and optimal controllers are an improvement from conventional controllers. Adaptive controllers can learn a building’s characteristics and environmental state and adapt to different environmental conditions and are non-linear dynamic systems (Shaikh et al., 2014). Predictive controllers use variables such as occupancy information and solar radiation to improve thermal comfort. A model predictive control scheme was developed and evaluated considering occupant’s thermal comfort and energy savings using different case studies (Freire et al., 2008).

3.4.2. Intelligent Building Control Systems

Intelligent buildings use advanced technology and computing to manage building energy performance and improve the indoor conditions. Sustainable intelligent buildings use a combination of approaches to enhance the building efficiency while working towards improved comfort for the occupants. They sometimes employ holistic and integrated approaches to achieve the performance objectives. Intelligent controllers focus on learning based methods such as artificial intelligence, fuzzy systems, neural networks-fuzzy with conventional controls, and adaptive fuzzy neural networks (ANFIS) systems (Passino, 2001). Fuzzy logic controls have multi- criteria control strategy incorporating an expert system; they can map non-linear model characteristics of system performance (Shaikh et al., 2014). Fuzzy controllers can be combined with PID controllers, they can adapt to different environments, and are model dependent. An improvement in energy consumption and response of controlled variables (i.e., thermal comfort, visual comfort) was observed when an adaptive fuzzy PD type controller was implemented

(Kolokotsa et al., 2001). Artificial neural network (ANN) and adaptive neuro-fuzzy interference systems (ANFIS) have been used as control tools to predict environmental parameters including occupant behavior modeling (Shaikh et al., 2014). Model-based predictive control (MPC) techniques can improve energy efficiency without compromising indoor environment conditions for comfort (Freire et al., 2008). It is capable of optimization but requires an integrated building control to optimize energy and cost (Shaikh et al., 2014). Yang and Wang (2013) used multi-agent- based intelligent control systems to meet the needs of occupants by responding to their requests and obtaining feedback based on occupant behavior. They demonstrated that intelligent buildings could manage the indoor environment. With improvements in building automation systems, the conflict between building occupants’ needs and their energy consumption should be easier to resolve. For energy and comfort management in buildings, buildings should be able to interact with the occupants and vice-versa for user-centered control. 44

Jazizadeh et al. (2014) proposed a framework for personalized comfort-driven systems using

HVAC systems. They used a fuzzy predictive model (which learns about occupant comfort profiles) to control the HVAC system and observed an improvement in the comfort of occupants for centrally controlled office buildings. Nguyen and Aiello (2013) recommend that since occupant activities and behaviors have a large impact on energy consumption in buildings, the actual energy needs of the building should be regulated based on occupant requirements to reduce the overdesign of equipment and HVAC. Also, the activities of the users based on the context of their environment should be identified. They studied intelligent building systems in relation to occupant activities and energy conservation and observed that about 40% energy for HVAC systems could be saved by using occupancy-based controls. Shaikh et al. (2014) identified some of the challenges of incorporating real-time interface and computation to take account of occupant preferences and attitudes. They stressed the need for control systems to process dynamic input and mentioned the importance of accounting for building occupants’ behavior, activities, and preferences for smooth building automation. A model for user attitude to intelligent environments demonstrates how a user or an agent may respond to the availability of building controls (Ball & Callaghan, 2012) (Figure

3-4).

Figure 3-4: The Callaghan-Clark-Chin (3C) Model (Ball & Callaghan, 2012) 45

The model captures different user responses to automated (agent-driven) and end-user (user- driven) controls. Using a fully automated system might deprive occupants of their preferred comfort but allowing for complete end user control can lead to misuse and energy waste. It is important to find a healthy balance between these different control scenarios that allow for occupant comfort and energy savings to be achieved simultaneously.

3.4.3. End-user Values and Building Control Systems

Bordass et al. (1993) discussed the need for building control systems that are more manageable and have a good user interface. The need for controls to be able to respond fast to the occupant comfort needs indoors was mentioned. They observed an increase in productivity when some control was given to office building occupants (Bordass et al., 1993). When the occupants had very limited or no control, they performed less than the average occupant.

While it has been observed that more advanced controls do not necessarily reduce energy waste or improve comfort, automated building controls are increasingly being installed in residential and commercial buildings. Although controls seem to have become more sophisticated with improved protocols, there are still problems with its ability to accurately respond to the needs of the occupants. When control is taken away from the end users, there are often more complaints and management/ facilities operators are not able to adequately provide more satisfactory comfort levels for the occupants (Bordass et al., 1993) which may be detrimental to comfort and energy efficiency. Furthermore, buildings are not operated as they are intended and the management team may not fully understand the controls expectations for the building. While it might be beneficial to limit occupant access to controls to reduce energy waste, providing some control might give them a level of autonomy where they can achieve their desired comfort using personalized controls.

A study by Callaghan on intelligent environments proposes the provision of some control to end users rather than making intelligent buildings completely autonomous (Callaghan, 2013).

End-user driven controls allow the user to program the controls for the agent interactions and automation. Rather than letting the agent decide what the occupant’s preferences are, these end- user controls allow users to program the systems through an easy to understand interface. Finding a balance between autonomous agents and end-user driven agents remains a subject for debate but it highlights the importance of meeting the needs of the end users (Ball & Callaghan, 2012). They investigated how automated agents can be user adjustable to determine the level of control the autonomous agent has over the users.

Human-Computer Interaction (HCI) and artificial intelligence (AI) have been used in different studies to improve building controls. AI is used in computer science and was developed to replicate human intelligence. Intelligent systems should be able to learn and adapt to new situations. AI techniques include expert systems such as Fuzzy logic, artificial neural networks

(ANN) and evolutionary computing (genetic algorithms) (Oancea & Caluianu, 2013). Expert systems are based on knowledge from human experts and may be incomplete due to human uncertainty. Fuzzy logic aims to imitate human logic using fuzzy sets and rules; it is increasingly used in building controls. ANN is adaptive and works well with large datasets that can be used for training the model. Genetic algorithms find the optimum solution from a set of given parameters

(Oancea & Caluianu, 2013).

3.5. Building Energy Modeling and Simulation

Computational models are used to study different instances of a complex system using computation or simulation tools (Sun, 2008). Computational models are sometimes called mathematical models. Energy modeling results can be verified by comparing results from computational models with actual measured utility data. Energy modeling is also used to predict energy use for financial budgeting, estimate the cost of utilities, and to identify solutions that lower costs for building owners. Breaking down the energy consumption by fuel type, end use, and

building component allows designers to focus on the major drivers of energy use and identify areas where energy savings can be realized. Energy models can ascertain how the building uses energy and how the levels of occupancy and the use of the building affect energy consumption.

Energy modeling and simulation tools can be used to predict the performance of a building.

Energy models are used for measurement and verification to determine if energy savings are achievable. Early application of energy modeling provides opportunities to make informed decisions in the design process before construction begins. Azar and Menassa (2012) highlighted the importance of using modeling and simulation tools to determine the sizing of building systems during the design phase and to predict energy use during the operation phase.

The integration of building information modeling (BIM) with energy modeling can be used to estimate and lower greenhouse gas emissions, compare different efficiency options, and select equipment for heating and cooling. It also determines the feasibility of equipment installation. The challenge of information exchange with building information models and simulation programs can hinder the exploitation of the full potential of BIM (Kim & Woo, 2011). It is sometimes difficult to import a 3D geometry model for energy analysis and not all the functionalities of the models are supported. Improving the ease of information exchange can bring about the smoother integration of BIM with energy modeling and simulation and result in building designs with improved energy efficiency.

3.5.1. Introduction to Energy Modeling and Simulation

Energy simulation tools help designers, engineers, and building operators determine how well a building operates; they consist of a simulation engine and a graphical user interface (Maile et al., 2007). Several parameters are input to the energy simulation engines such as the building geometry, weather conditions, HVAC systems, internal loads on the building, and the operation schedule (Maile et al., 2007; Yu et al., 2011). The equations involved in energy simulation

calculations are complicated and could be quite cumbersome; simplifications are made to the equations by making some assumptions for the simulation engine (Clarke, 2001). For improved simulation accuracy, the assumptions that simplify the real scenario should be as precise as possible so a close estimate of the actual performance of the building can be obtained.

The graphical user interface (GUI) enables users to enter simulation parameters and view simulation results interactively. It allows for easier and more understandable input of information and presentation of results. The input could be processed in separate files or directly entered and the outputs could be presented in a separate output file. Examples of the user interface are

DesignBuilder for EnergyPlus and eQUEST for DOE2.2 (Welle, 2009). Gaetani et al. (2016) listed the possible factors that can determine the choice of a modeling technique namely, the building function, building characteristics, climate, goal of simulation, and performance indicators which encompass building loads, lighting, and comfort.

Rosenbaum (2003) outlined the following as inputs required for energy modeling. They are the location, envelope properties, internal gains, schedules, and systems. The values of some of the variables used in the simulation program are available from sources like ASHRAE handbooks and the ANSI/ASHRAE/IES standards. Most of the parameters depend on the location and the specific building being considered. Weather files are used as one of the inputs for energy simulations. They are available as typical meteorological year (TMY) and actual meteorological year (AMY) files, other versions also exist. The TMY files were created by the U.S. Department of Energy to provide more comprehensive information about the climate for improved design of

HVAC systems (Weather Analytics, 2015). TMY files are used to obtain a profile of the typical weather at a location while the AMY files are actual historical measurements of the weather condition of a location over a period. They are used to monitor, manage, or confirm the actual energy performance of a building. The weather files should be obtained from a location as close as possible to the building being modeled to obtain reliable accuracy. Weather stations can be installed 49

locally to measure weather data. AMY weather data might not be suitable to predict the future weather when the conditions for the year under consideration was anomalous (Hemsath &

Bandhosseini, 2018). Data is provided at different intervals (e.g., minutes, hourly or daily) depending on the collecting station.

TMY files are created based on a weighted average of some weather variables for a particular location over a number of years. Various TMY files are available, for instance, TMY 2 files use 30 years of data and TMY 3 files are based on 15 years of data and include solar radiation.

TMY files do not consider extreme weather conditions and are suitable for prescriptive and not performance-based design (Hong et al., 2013). Weather data can be obtained from airports, the

National Oceanographic and Atmospheric Administration (NOAA), weather analytics, weather underground, and local weather stations.

Building energy simulation finds application in the designing buildings for code compliance. It is beneficial for cost analysis, comparing energy savings options, and determining if the equipment is operating according to the design and indoor environmental quality requirements. Energy simulation programs should be selected by matching the use of the simulation results with the capabilities of the program.

3.5.2. Comparison of Building Energy Modeling and Simulation Tools

A comparison of energy modeling and simulation tools is presented in this section. A selection of three tools is made for the comparison namely eQuest, integrated environmental solutions (IES), and EnergyPlus in Table 3-2. The tools are compared based on different features.

Some of these tools are open source and allow for co-simulation with different software to extend its functionality and allow for other parameters to be modeled.

Table 3-2: Comparison of the Features of Energy Modeling and Simulation Tools (U.S. DOE, 2011). Features EnergyPlus eQuest IES VE Version 8.7.0 3.65 2017 Validation/ IEA BESTest building ASHRAE Standard 140 ASHRAE 140, BESTEST, Testing load and HVAC tests CIBSE TM33, EU EN13791, EPACT Qualified Expertise High level of computer Wizard-based use, no Training is required Required literacy not required; experience with energy engineering background analysis is necessary but helpful for analysis for detailed interface, portions knowledge of building technology is required. Audience Architects, engineers, and Building designers, Architects and engineers researchers operators, owners, energy consultants, researchers Input Simple ASCII input file, Various inputs Geometrical building data Allows zone List for depending on the level may be imported from a steam and other of detail range of CAD/BIM systems equipment internal gains Output Component model sizing Graphical summaries, Tabular, graphical, 3D diagnostics for easily comparative results, geometric visualization of comparing user-specified parametric tabular analysis results, and full 3D values to auto-sized reports, interval immersive results viewers. values simulation results Programming Fortran 2003 Interface: C++, DOE-2.2 C++ (MFC, STL, .NET), C#, Language engine: FORTRAN Fortran, HTML, XML, JavaScript Strengths Innovative simulation Evaluates whole Comprehensive analysis capabilities: time-steps building performance options offered across a wide less than an hour, throughout the entire range of metrics, Simulation modular systems and design process. The results are linked between plant integrated with heat energy performance of modules. Convenient data balance-based zone design concepts can be manipulation coupled with simulation, multi-zone air explored at an early the ability to undertake what- flow, thermal comfort, design phase. The if assessments at the design water use, natural detailed interface calculation stage. Integrated ventilation, and supports detailed data model means that photovoltaic systems. analysis throughout the design changes are WeatherConverter construction documents, immediately updated produces 2013 design day commissioning, and elsewhere. Unrivaled files for appropriate post-occupancy phases. interoperability with other locations. Interfaces with Fast execution of large CAD/BIM platforms. DesignBuilder which models, automated uses, CAD, templates, checks of simulation wizards and compact air inputs and results, system configurations. automated savings by design analysis

Weaknesses Text input may make it Defaults and automated Linux environment is not more difficult to use than compliance analysis has supported graphical interfaces not yet been extended

from California Title 24 to ASHRAE 90.1, simplified natural ventilation models Availability Free Free Free limited trial version Function Models heating, cooling, Calculates heating or Measures energy lighting, ventilation, other cooling loads for various performance, energy energy flows, and water building factors, energy consumption, HVAC, use performance and daylighting, load calculation, tabulates energy use for solar shading, egress, various end uses. ingress, natural ventilation Occupant Schedules for behavior, Schedules for behavior, Schedules for behavior, use Representation use of appliances and use of appliances and of appliances and occupancy occupancy occupancy

3.5.3. Applications of Building Energy Modeling and Simulation

Building energy simulation tools are used to estimate energy use by building systems such as the heating, ventilation, and air-conditioning (HVAC) systems and lighting systems. They are used for sizing equipment to provide estimates of energy use based on the inputs provided to the simulation tool. They can predict the building performance over time (e.g., annually) and can be used at various phases of a building from conceptual design phase for a new building to the operations phase (i.e., for retrofit in an existing building). Energy performance is calculated including outdoor weather conditions, occupancy, building type and other building parameters

(Macal & North, 2007). Energy simulation tools are based on algorithms that are integrated with the simulation program engine.

Energy models are not very sensitive to occupant behavior (Azar & Menassa, 2012).

Occupant parameters are sometimes entered incorrectly and are oversimplified in energy simulation tools. People are seen as static and are represented based on occupancy levels- the number of people in a zone, rather than the individual occupant’s preferences and needs. This assumption leads to building systems that are oversized which provide indoor environmental conditions that are not

well-suited to the users of the space thus leading to energy wastage. It has been demonstrated that up to 30% of energy savings can be achieved by taking occupant behavior into account in energy simulations (Hong et al., 2016). Clevenger and Haymaker (2006) stated that wrongly entering a single parameter in an energy modeling tool can inaccurately estimate energy use by up to 40%.

To improve simulation accuracy, dynamic schedules are sometimes used, but they still do not adequately reflect the needs of individual occupants. Open source energy simulation tools enable the integration of other algorithms to improve the occupant-related energy use prediction to better model different behaviors. Hong et al., (2016) identified three stochastic occupant behavior representations namely the Bernoulli process, Markov chain, and survival analysis. In the Bernoulli process, occupant behavior does not change with the state of the system but the Markov chain approach takes the past into account while the survival analysis method is used to predict longevity.

The Markov chain model is used in agent-based modeling (ABM), and it was found that for energy use predictions it is important to be able to include the past behavior of occupants to predict future behavior and impact on energy consumption and satisfaction (Khazaii, 2016). In determining occupant satisfaction and energy use behavior, it is vital to capture behavior through monitoring strategies so that a more accurate representation of occupancy and occupant characteristics is determined (Andrews et al., 2011) while taking the external influencing factors into account.

There is a high level of uncertainty related to occupant behavior in buildings for energy modeling and differences in the sensitivity of the model to changes in parameter values (Clevenger

& Haymaker, 2006). High-performance buildings integrate different features in a building including energy efficiency, performance, and productivity of the occupants (Kibert, 2013). These buildings still do not meet up to the expectations even with sophisticated simulations. There are also usability problems, where buildings are not operated as intended (Andrews et al., 2011).

3.6. Agent-based Modeling and Simulation

Agent-based modeling (ABM) techniques are versatile and have been used in various disciplines to depict human behavior. Through ABM approaches, real-life problems can be simulated in the agent environment by observing and computing changes sensed by the agent. ABM tools find application in marketing to study financial markets, in transportation for traffic management (Bonabeau, 2002), and in organizations for decision making (Macal & North, 2007).

They are also used in the building industry for fire safety egress design (Kuligowski et al., 2010), for construction site operations and to design for energy conservation in buildings. ABM tools have been beneficial in depicting human behavior to improve the safety of systems, gain a better understanding of processes and enhance end-user satisfaction in different contexts. Agents are virtual models built in a virtual space/environment that operate in accordance with the rules assigned to them (Macal & North, 2007). ABM uses different kinds of logic, for instance, Bayesian network and fuzzy logic. ABM is a robust approach that can mimic human behavior and deal with complex systems.

ABM is sometimes used by coupling with other simulators. They can predict individual level energy consumption and are stochastic models. The capabilities of existing simulation tools can be extended to enable the inclusion of occupant values while estimating building energy consumption. A critique of available agent-based modeling platforms highlights the opportunities for occupant-sensitive energy consumption predictions

3.6.1. Agent-based Modeling of Building Occupants

Agent-based models have been around for several years and are derived from complex adaptive systems (CAS) (Macal & North, 2007). CAS aim to understand how complex behavior is derived from agents. According to Holland (1995), complex adaptive systems allow for integration, they are nonlinear and allow the flow of information. The agents are diverse and can behave

differently from each other. CAS is widely used in the biological sciences to model complex and emergent behavior (Holland, 1995). This modeling approach is also used in different domains such as social, economic, physiological, ecological, physical, and transportation systems.

With the evolution of ABM, increased complexity and functionality have been incorporated into the systems. ABM tools can model systems that comprise autonomous interacting agents. Multi-agent systems (MAS), which comprise multiple interacting agents, have also seen increasing applications in various fields of study to predict, analyze or manage different phenomena. Individual agent behavior is modeled to determine the global behavior of the agents as they interact with each other. Intelligent agents can predict human behavior, perceive the environment, and act accordingly with minimal human input (Arciszewski et al., 2005).

Agents are autonomous and are capable of acting alone, interacting with each other and interacting with their environment (Bonabeau, 2002). They can respond based on their behavior or based on their interaction with other agents. Bonabeau (2002) listed the best scenarios to use ABM namely when the agents are complex, heterogeneous, and not fixed. Building occupant behavior can be represented in these agent scenarios. The agents are assigned different characteristics/attributes and respond based on the rules of the system. One of the main advantages of ABM includes the ability to reach logical conclusions in situations where it is not feasible and cost-effective to perform actual experiments (Khazaii, 2016).

An increasing number of studies use an agent-based approach to model building occupant behavior and assess the impact of occupant behavior on energy consumption. Zhang et al., (2011) applied ABM to electricity consumption by computers and lights. The objective was to integrate organization energy management policies, energy management technologies, electrical appliances, and occupant behavior in an office into one model. They also developed a multi-agent framework using multiple agents to study practical energy management issues and found it could be a useful tool for energy management in offices. In the study conducted by Azar and Menassa (2012), a 25% 55

variation was found between traditional modeling approaches and ABM by changing the occupant characteristics using three different categories of occupants. The categories are high energy consumers (HEC), medium energy consumers (MEC) and low energy consumers (LEC) adapted from a study by Accenture (2010). They validated their tool using a simulated case study building and mentioned the need for using actual energy measurements to validate the tool.

Kara and Baxendale (2012) created an agent architecture that comprised two types of agents. They represented human and electrical loads integrated with dynamic load control (DLC) algorithms using different agent-based models which showed good potential for managing building loads considering humans. Lee and Malkawi (2014) developed an agent-based model and identified how the agent handles different behaviors. The approach used was a simulation by coupling a numerical computation tool and an open source simulation tool. They considered how the agent adapts to dynamic thermal changes and optimizes comfort and energy savings. They also emphasized the need for validation with actual measured data from buildings and occupants. Cao et al., (2015) used ABM as a scheduling framework to determine the prioritization of maintenance by facilities managers in buildings. Impressively, they observed a 30% and 97% increase in occupant satisfaction and building energy efficiency respectively.

ABM has been explored for incorporation into the design of building control systems in

HVAC applications (Treado & Delgoshaei, 2010). They mentioned the potential of these tools to be used for control and configuration of autonomous building systems. The use of ABM to simulate the influence of energy saving policies among different occupants was explored by Bastani et al.,

(2016) and they found a 13-20% variation in energy savings between two ABM approaches that were used. Making an accurate prediction of occupant behavior remains a challenge for analyzing occupant impact on building energy performance. Also exploring the relationships between the occupant and their interactions with different building systems to improve their comfort can give

an insight into suitable operation strategies for the building to ensure occupant comfort and minimize wasteful behaviors.

3.6.2. Comparison of Agent-based Modeling Tools

As previously described, ABM tools model agents, agent behavior, and the relationships between agents based on the rules of the system (Macal & North, 2007). Several tools exist for

ABM, three agent-based modeling tools were selected for comparison namely Anylogic

(AnyLogic, 2017), NetLogo (Wilensky, 1999), and REcursive Porous Agent Simulation Toolkit

(Repast) Simphony (Argonne National Laboratory, 2017) and are presented in Table 3-3.

Table 3-3: Comparison of the Features of Agent-based Modeling Tools [Extracted from Kravari & Bassiliades, (2015)]

Features Anylogic NetLogo Repast Simphony Version 7 6.0.2 2.4.0 Primary Domain General purpose, For social and natural Social science, supply distributed agent-based sciences chain, and consumer simulation, discrete products event and systems dynamic Programming Language Java Scala and some parts are Java written in Java Execution Speed Fast Intermediate Fast 3D Visualization Yes Yes Yes User Interface Very good Good Good Integration with other Yes - Yes tools Open Source Extensible and Yes Yes customizable platform Cost Free learning version Free Free Technical Support High Good Average support Usability Good GUI, averagely Simple and easy to learn Simple and easy to simple and easy to learn learn Scalability High Good Good Performance High Good High Robustness High Average High

The tools were compared based on a selection of features such as their visualization capabilities, programming requirements, and their usability (Kravari & Bassiliades, 2015). The evaluation was based on a comparison of the tools with one another. Most of the features are self- explanatory but others that are not very clear are defined as follows: usability denotes to suitability for agent-based modeling, scalability refers to the capability of the model to handle different volumes of information and types of problems, while robustness demonstrates how well the model handles errors during execution. A tool for modeling is selected based on this comparison. The features that were compared are all important to inform the selection of a suitable tool for modeling occupant behavior.

3.7. Summary

Energy consumption in residential and commercial buildings has been explored and the need for building operations to better address occupant values was discussed. Different aspects of building design and operation were reviewed to provide a background and an understanding of the approaches that can be employed to better integrate occupant values and preferences with building systems. The following gaps were identified from the literature review. First, there are insufficient studies on energy use behavior and end-use energy consumption in the Qatar building context.

Longitudinal studies using actual buildings to explore the interactions between energy use behavior and building energy consumption in residential and office buildings in Qatar and the U.S. can provide insight on the interventions to improve energy use behavior and energy efficiency. Second, several studies focus on occupancy rather than individual occupants. The efforts to incorporate end- user values in building systems have been highlighted from different studies, however; the limitations of existing tools and inadequate information about end users have stood as barriers to advancement. Third, in relation to occupant comfort needs, ASHRAE recommends at least 80% of the occupants should be satisfied (2004). Occupant behavior was identified as one of the factors

that contribute to energy waste in buildings. Some of this behavior is related to dissatisfaction with occupant values. There are opportunities to enhance building controls and building operations to better address occupant-related factors and improve satisfaction.

Clements-Croome (2013), listed the important factors for effective integration of building systems with occupant needs which include communication, information sharing, interoperability of systems and processes, well-defined processes, and clear goals. For integration to be continuous, that is, integrative; it should be continuously adding value to the stakeholders throughout the life cycle of the building especially the end users. Following the literature review, this research explored some case studies. In Chapter 4, a study of other sectors (i.e., the transportation industry) to gain insight on how end user values are integrated with the environmental control systems in cabins will be completed to draw out lessons on how improved integration can be achieved in buildings. Also, case studies of operational buildings using sensors to monitor and track energy use and occupant-related factors (preferences and behavior) are conducted in Chapters 5 and 6. Studies have demonstrated that energy simulation tools do not adequately address occupant factors, so an exploratory study using a simulation approach to address end-user values in indoors looking into the controls is conducted in Chapter 7.

Chapter 4

LEARNING FROM OTHER INDUSTRY SECTORS

4.1. Introduction

In Chapter 3, the approaches to accounting for end-user values in buildings were reviewed.

For this study, it was worthwhile to examine other industries that seem to have been more successful at providing end-user satisfaction while improving energy efficiency so that beneficial recommendations for buildings can be identified.

In business ventures, the stakeholders, including the end users or customers, have different requirements and expectations for products and services. It is important to identify the needs of the customers and ensure they derive value from the product. In buildings, the end users are sometimes the client, the owner or tenants, and their needs should be included in the design process to provide an environment that promotes and enhances their well-being and productivity. Integrated process models have been developed for the design of buildings to highlight the activities required for a successful project; this model includes various stakeholders and information flows (Sanvido &

Norton, 1994). Kamara et al. (2002) developed a tool - ClientPro, to collect client requirements in construction projects; those requirements can be used throughout the life cycle of the project. There is a need to collect information in projects and ensure this information is appropriately incorporated into the project throughout the lifecycle. Continuous data collection throughout the project to assess how the needs of end users are met and the approaches to ensure continuous improvement is beneficial.

In the transportation sector, various approaches have been used to account for consumers in the design process. For instance, in the automobile industry, rigorous testing and verification are conducted before the final design and manufacturing of a vehicle. Although the transportation sector and building industry are in different domains, some lessons can be learned from the

processes used in the transportation sector and applied to buildings. Emes et al. (2012) reported the lessons that can be learned from spacecraft systems engineering for the construction industry from their comparisons of space and construction domains.

End-user values and satisfaction have been discussed in marketing and manufacturing. In the transportation sector, value for end users in relation to comfort can be derived from having the

‘right’ cabin environment. Environmental control systems (ECS) monitor and control the temperature, humidity, airflow, and pressure in aircraft, cruise ship, or automobile cabins (National

Research Council, 2002). These systems are sometimes automated to simultaneously ensure the comfort of occupants is enhanced while improving energy efficiency. Examples of industries within the transportation sector that emphasize cabin conditions are the automotive industry (design of cars and buses), the aerospace industry (design of passenger aircraft), and the shipbuilding industry

(design of cruise ships). Design for end-user values and customer requirements in these industries uses techniques such as total quality management (TQM), quality function deployment (QFD), value stream mapping, reliability analysis and Taguchi methods (Kamara et al., 2002). House of

Quality is a matrix that is used in manufacturing to translate a customer’s requirements into a product (Reliability HotWire, 2008). Quality function deployment is used in Six Sigma to address quality requirements and to improve customer satisfaction. Reliability is concerned with the extended quality of the life cycle of a product (Reliability HotWire, 2008). Hazelrigg (1998) created a framework for a decision-based design where the designs are made to maximize the value of products to customers. The designs are based on a variety of sources, not just engineering knowledge. Extensive end-user satisfaction surveys are conducted to establish user values and preferences, which may then be used as inputs during the design process.

This chapter considers the transportation sector, investigating how the automobiles, ship, and aircraft provide value (regarding IEQ) to the end users. As introduced in previous chapters, the end-user values of concern are the thermal comfort, which is determined by several factors 61

including temperature and humidity. The illuminance levels relate to lighting or visual comfort.

The air quality is the cleanliness of the air and the absence of pollutants and contaminants and is measured by the ventilation rate. This chapter identifies the approaches adopted in the transportation industry, presents the case studies, and provides recommendations for buildings.

4.2. Accounting for End-user Values and Preferences in the Transportation Sector

The transportation sector strives to increase energy efficiency and works towards continuous improvement in design, fuel efficiency while seeking ways to improve safety and maintain customer satisfaction. The automobile industry consists of different vehicle types such as light-duty vehicles, i.e., sedans, sport utility vehicles (SUVs), and trucks. Air transportation comprises aircraft, helicopters, and sea transportation comprises- ship, submarines, etc. Although energy use by the transportation sector has more than doubled since 1970 (road transport responsible for the majority), there has been improved efficiency and reduced emissions in this sector (Figure 4-1). Increasingly stringent standards in the sector allowed for innovation and radical improvements in the sector (U.S. EPA, 2017).

Figure 4-1: Comparison of Energy Consumptions by Different Sectors (Architecture 2030, 2014) 62

In addition to this, a comparison of growth areas and emissions in the transportation sector between 1980 and 2015 is presented in Figure 4-2. Although there was a population increase and more vehicle miles were traveled over the years, there was an increase in Gross Domestic Product

(GDP) and a decrease in emissions which further demonstrates the progress made in the transportation sector. The transportation sector is explored to reveal how end-user values are accounted for and how the industry works toward improved efficiency.

Figure 4-2: Comparison of Growth Areas and Emissions, 1980-2015 (U.S. EPA, 2017)

Automobiles, aircraft, and cruise ship have cabins inhabited by people and an estimate of the time spent in cabins are presented in Table 4-1. Human factors engineering is incorporated to understand what their customers want and how they are likely to use the cabin including predicting their comfort requirements.

Table 4-1: Time Spent in Cabins

Industry Type Duration Aerospace Aircraft ~17 hours Automobile Cars and SUVs A few minutes to several hours (~10 hours non-stop) Ship-building Cruise Ships Days to almost one year

In the manufacture of vehicles and aircraft, a continuous improvement loop that incorporates end-user feedback to improve the final product is used. The industries considered are distinct and operate in different environments which introduce some variations in the mode of operation of the HVAC systems. They may not be directly compared to buildings, for instance, automobiles can be single occupancy, ship cabins can be single or multi-occupancy while aircraft, most times are multi-occupancy. They all operate in different environments, but some lessons can be learned from the processes used in these industries. Overall, their priority is to provide a conducive environment for their customers/ passengers and maximize their profits. The findings from an initial review on the amount of control given to passengers in their cabins are presented in

Table 4-2 below. The end users can sometimes adjust the cabin conditions in these industries.

Table 4-2: End-user Controls from Initial Review

Comfort Aerospace Automotive Ship Building Parameter (Passenger Planes) (Cars and SUVs) (Cruise Ships) Temperature End users can not Users may have individual Sometimes centrally adjust ambient vents to adjust airflow; the controlled and users may temperature, but air temperature is centrally not have the ability to vents can be used controlled make changes Humidity Humidity can be quite Humidity can be Humidity can sometimes low and difficult to controlled through the air be quite high and difficult control conditioning system to control Airflow Passengers have Users can control airflow Users may have some personal vents to with the windows level of control adjust airflow depending on the ship design and type of cabin Lighting Task lighting is User controlled interior Some ships are controlled available and shading lighting and limited by lighting sensors and device can completely shading shading to block out light block out exterior light in some cases

There is a need for the building industry to work towards total user experience (TUE), buildings are a product of design and construction. TUE considers the encounters the user will have with the product. The TUE model includes the following steps: acquire, prepare to use, use,

maintain, get support, and terminate a product. In every phase of the model, the end user should be central and they should be based on an integrated approach (Adikari et al., 2011; Jacko, 2012).

Existing trends are presented for each industry.

4.2.1. Aerospace Industry

In the aerospace industry, material reduction has enabled an improvement in the environmental conditions in the aircraft cabin. Airplanes can fly at lower altitudes thereby allowing for better atmospheric conditions during flight and more flexible choice for lighter environmental control mechanical systems. A study conducted by the Atmosphere Research Group shows that

90% of U.S. flyers care about comfort on the aircraft (Harteveldt, 2016). Comfort is used more broadly here. Most of the complaints in the aircraft industry are overshadowed by factors such as delays, lost luggage, and in-flight entertainment; as such, the interior cabin climate is ranked low among the problems encountered by users (Vink & Brauer, 2011). According to a survey conducted by Vink and Brauer (2011), on 10,032 passengers, less than 5% of them felt the climate inside the aircraft was a problem to them (95% satisfaction level). Most of them felt the attendants were willing to adjust the temperature when they felt uncomfortable. Lee et al. (1999), studied 16 aircraft in Hong Kong and measured various parameters such as Carbon dioxide (CO2), humidity, Carbon monoxide (CO), Ozone (O3), bacteria, fungus, and respirable suspended particulate (RSP) to determine the cabin conditions. They found that low humidity caused discomfort to the cabin crew.

Time spent in aircraft can vary from a few minutes to several hours depending on the duration of the flight (Table 4-1), so it is important to ensure occupant satisfaction with the cabin conditions especially for longer flights.

The ECS includes the seating, lighting, cabin pressurization and daylighting (Cole, 2004).

Commercial aircraft are primarily focused on the transportation of people and goods. The ECS in an aircraft ensures a comfortable environment during transportation. This system helps with cabin

conditioning (pressure, temperature, ventilation, and humidity) and fire protection (smoke detection). Aircraft encounter large variations in external temperature, pressure, and humidity, and require a suitable control system to maintain comfortable conditions for the occupants (National

Research Council, 2002). The ECS team is tasked with finding the right cabin environment for the end users looking at the effects on the heart. The National Research Council (U.S.) Committee on

Air Quality in Passenger Cabins of Commercial Aircraft (2002) outlined the critical factors in aircraft cabins such as monitoring air quality and air pressure, tracking the effects of these on health, and the importance of having the right ventilation rate for improved air quality. Bleed air systems are used for the delivery of compressed air for cabin pressurization and air conditioning. ASHRAE standard 161 defines requirements for air quality in aircraft and includes requirements to check contamination of bleed air from the engine (ASHRAE, 2013b). The standard covers requirements for different conditions when in the air and on the ground. It considers cabin pressure, temperature, air speed, ventilation, specific contaminants, addressing contamination events, and measurements for specific variables.

Some of the considerations for user satisfaction in aircraft cabins are safety, cleanliness, air quality, thermal comfort, seating comfort, acoustic comfort, ambient lighting, cabin pressure, and overall appearance (Airbus, 2015). The aircraft industry continually improves to stay ahead of the competition and meet the needs of their customers while continuing to ensure safety. Controls for airflow and lights are provided for each passenger in most airplanes and can be adjusted per zone and optimized for the comfort of the passengers and crew. Control of humidity in an aircraft cabin is one of the main concerns and noise is minimized by using quieter engines. Aircraft passenger capacity has increased over the years (i.e., the Airbus, A380 and Boeing’s latest addition, the 787 Dreamliner) to carry more passengers. Newer aircraft use cabin lighting to mimic daylighting, provide personalized controls for individual passengers so they can adjust the airflow, control task lighting, and adjust the blinds. 66

4.2.2. Automotive Industry

Since the invention of automobiles by Carl Benz in 1886, automobiles have become more adaptable to human use and include features to improve the comfort of drivers and passengers such as better brakes and improved climatic controls. Historically, the automobile industry caused a lot of air, noise, and visual pollution (Melosi, 2010). Over the years, considerable improvements have been made in the automobile industry to improve performance, reduce fuel consumption and emissions using more integrated systems. There have also been a lot of changes to the interior of the vehicles to improve the comfort of the users. World global leaders in vehicle production such as Germany, Japan, and the U.S. continue to invest in R&D for motor vehicle production.

Automotive manufacturers identified the need for a more user-centered approach for the design of automobiles by understanding the things that are valued by the end-users to differentiate themselves from competition (Bryant & Wrigley, 2015). One proposed approach is the use of personas that are fictional characters which represent the behaviors, goals, expectations, and motivations of the end users thus removing the disconnect between the product and the end users (Bryant & Wrigley,

2015). The automobile industry has regular maintenance schedules to ensure the smooth running of automobiles and diagnosis of problems that may occur. Quality controls are very rigorous in the transportation industry due to strict regulations and safety requirements. Automobiles undergo rigorous testing several times in the design and post-design process also giving the end users the opportunity to test drive the vehicle before purchase. Products that have design faults are recalled, even after they have been in circulation, for safety reasons.

Thermal comfort models are used in the automobile industry to simulate the experiences of users of the car. Virtual reality, computer models, and simulation have also been employed including continuous testing before and after production. Thermal manikins are sometimes used in place of humans to test automobiles, manikin physiology control and predictive comfort (Manikin

PC) software is a closed-loop feedback control system that mimics the human thermoregulatory 67

system and provides metrics for comfort and sensation (CBE, 2014). The Building Sciences group at CBE Berkeley developed the advanced comfort model validated by auto manufacturers for improving comfort in auto cabins (CBE, 2014). This tool was also used by the research team in building science research for occupant comfort. Akamatsu et al., (2013) discussed the advancements in automotive human factors over the years and the shift in focusing on what the driver does to tasks he might be faced with in the automobile.

General Motors (GM) uses computing to benchmark cars and can learn how the user operates the vehicle (GM, 2015). They test and track the vehicle performance to observe changes in performance. They also use customer inputs to capture a variety of scenarios and check for drivability and customer satisfaction. They learn on actual test drives with the users and observe the changes they need to make in the vehicles to improve the customer experience (usability testing), but buildings do not have a stringent quality control strategy. The automobile industry works in integrated teams with the brand teams, factories group and the development team collaboratively coming up with workable solutions. Cars are also fitted with sensors that detect occupant presence and ensure safety, comfort, and convenience for the users. Personalized driver profiles contain information on preferences of the drivers and the interior environment is adjusted according to the stored preferences (BMW, 2015). These preferences include seating adjustments, lights, temperature settings, etc. The automobile industry focuses on the ergonomics, leg room, and seating in the vehicle. End users are surveyed looking at the positives and negatives of different vehicle features. The key takeaway from each is documented and it informs the design of other vehicles. Quality management methods such as the house of quality and the voice of the customer are used to determine the importance level of each variable being considered. Recommendations for interior styling and other design considerations for the vehicle cabin are then developed. Six

Sigma is a method used in manufacturing and other industries for the measurement of the

probability of manufacturing a product or creating a service with zero defects and high performance

(Yang & El-Haik, 2009).

Mostly transient conditions are observed in automobiles but on a long-term basis, the conditions can be more stable (ASHRAE, 2007). Automobiles have an air handling unit for heating, defrosting, ventilation, and air conditioning (cooling and dehumidification). Human physiological manikins are used for the prediction of human physiological behavior in automobiles. Automobiles also use onboard diagnostics that are valuable to end users. Hétu & Plante (2008) discussed the value of onboard diagnostics to customers, the development engineer, and the mechanic that maintains the vehicle. Sensors provide lots of data that is beneficial to these parties. Sensor data can reveal areas of improvement for future designs, in extreme cases, when evidence of possible safety concerns exist, the vehicle may be recalled which might negatively impact the reputation of the company and lead to financial losses. Onboard diagnostics data is used for fault diagnostics and to alert the customer when the vehicle is due for maintenance. Highly intelligent communication networks in automobiles make it easy to identify faults, and sometimes provide instructions on how the problem may be rectified (Hetu & Plante, 2008). Historical and real-time data are used to improve performance.

The design of the HVAC system of automobiles is based on design standards such as

ASHRAE Standard 62.1. The weight of the system is reduced to improve fuel efficiency. In addition to improved fuel efficiency, cars and sports utility vehicles have improved user experience with interactive 3D dashboards and diagnostics that help the driver visualize the performance and operation of their vehicles. The automotive industry continues pushing boundaries of functionality, integration, and resilience, for instance, the development of autonomous/self-driving vehicles.

Knowledge-based systems are employed to reduce human involvement in systems and industry.

They can learn the behavior of the users over time leading to an increase in the use of autonomous systems (Arciszewski et al., 2005). It is interesting to note that despite the progress made by the 69

automotive industry in accounting for end-user values in the design process, they still seek to shift towards a more user-centered process and improved performance. The building industry could learn from the automotive industry to identify how user-values are accounted for while maintaining improved performance through personalized profiles of end users of the building.

4.2.3. Ship-building Industry

Passenger comfort in a ship is determined by the air temperature, air quality, and interior design of the spaces (Grin, 2015). The cabin interiors in cruise ship require strict hygiene conditions to prevent the spread of infection. Seasickness is a common occurrence on cruises and is sometimes caused by the motion of the ship, vibrations, and noise (Dallinga & Bos, 2010). Cruise ship cabin types vary depending on whether they are in the interior without windows, ocean view with windows, or outside cabins with a balcony. Larger cruise ships (4,000 passenger ships) do not have as much motion induced discomfort as smaller ones (Grin, 2015). Interior conditioning in cruise ships should be able to provide adequate lighting, fire protection, good air quality, and thermal comfort for the occupants. Sensors are used in modern cruise ships to control lighting and energy- saving lighting and using alternative energy systems (Lloyds Register, 2008). Cruise ships are built based on numerous tests and complex research to meet the marine and shipbuilding industry requirements. The importance of integrating human factors with ship operations has been emphasized to provide a safe and healthy environment for the people (Lloyds Register, 2008).

Cruise ships provide unique experiences for passengers on board. Ships are inhabited for long periods of time with the average length of time spent on a cruise being seven days, and the longest cruise can be up to 1 year (Table 7-1). Larger ship vessels with improved efficiency are being constructed. The cabin conditions for crew and passengers on the ship are similar to conditions in buildings when compared with automobile and aircraft. There are challenges with air quality, humidity, and spread of infections in a contaminated space on cruise ships. There is a

continuous investment in research and development to develop new technologies and adopt technologies from different industries. For instance, Boeing uses a gaseous air filtration system

(with HEPA filters) used in hospitals to purify the air in aircraft cabins and minimize illnesses associated with low humidity environments. They employ ways to improve fuel efficiency and provide value to stakeholders especially the end users. The competition pushes them to be more innovative and customer focused.

4.3. Methodology for Learning from Other Industries

A mixed method was used in this study involving semi-structured interviews, observations, case studies, and questionnaires with open and closed-ended questions. The research methods were described in Chapter 2. The questionnaires were distributed to experts in the field that were interested in taking part in the study. Ten people were identified and sent the questionnaires and six responses were collected. The responses are presented in Table 4-3.

Table 4-3: Organizations Selected for the Case Studies

Organization Type of Industry Title of Respondent at Organization Business A Ship-building Cruise ship Managing director B Ship-building Cruise ship Former executive and architect

C Ship-building Cruise ship Technical expert

D Automobile Passenger vehicles Design & release engineer

E Aerospace Passenger plane/ Associate technical fellow- ECS Aircraft expert F Aerospace Passenger plane/ Cabin air comfort expert Aircraft ECS expert The questionnaires were adapted for each industry and they contained a few multiple- choice questions and open-ended questions to enable adequate information to be captured from the respondents. Some of the respondents were also interviewed to solicit additional information. The

questionnaires contained open and closed-ended questions. The closed-ended questions comprised

Likert-scale responses which were coded for analysis using numerical representation. Content analysis was used for the open-ended questions and the interview responses and cross-case analysis was used to find commonalities among different themes.

4.4. Industry Case Studies

The findings from the case studies are presented below (Figure 4-3). To gain an understanding of the level of control the occupants had over different aspects of the HVAC and lighting systems in their mode of transportation, participants were asked about the extent to which end-user preferences were integrated to the environmental control systems.

A- Cruise Ship 1- Never 5 2- Rarely 4 3- Sometimes B- Cruise 4- Usually F- Aircraft 3 Ship 5- Always 2 1

C- Cruise E- Aircraft Ship

D- Automobile

Figure 4-3: Extent of Integration of Occupant Values with ECS

For the cruise ship industry, there were different opinions; one expert felt they were always integrated, another felt they were sometimes integrated and another felt they were rarely integrated.

For the automobile industry, they felt it was sometimes integrated while in aircraft they felt it was usually or always. On average, they felt end-user values were usually integrated into the design of the environmental control systems. End-user values and preferences are integrated into the design

of cruise ships by giving the passengers a feeling of an individually designed cabin while also standardizing the cabin designs during manufacturing. Like most industries, the owners of cruise ship cabins get value from giving the end users what they want. They collect customer feedback and find ways to integrate the feedback into the design. They try to capture as much as possible early in the design since modifications to HVAC systems are often very expensive after installation.

In aircraft, the end-user values are met by following the system specifications which are validated through testing.

It was important to determine the level of control the occupants have in the cabins over the temperature, airflow, and lighting. In cruise ships, the air handling unit provides air based on the

CO2 levels in the space and the design of the system is based on technical requirements. The noise from HVAC systems sometimes poses a problem and one of the main considerations in the design of these systems is the weight, noise, and vibrations. The ECS is used to control the indoor conditions of different cabins. End-user values are captured in cruise ships through international design standards that provide guidelines on the required level of comfort. The owners establish the layout and function of the ships. There are specifications with the required prescriptive and performance standards and they sometimes cross-utilize the systems using an integrative approach.

Satisfaction surveys are also used to determine how comfortable the ship cabins are for the occupants and the feedback is used to improve the cabin conditions. They use fan coil concepts and decentralized units. They also simulate different load conditions for thermal comfort.

Aircraft cabin conditions are assessed through surveys and feedback from crew members, the ASHRAE standards, and other technical standards that are also used for the design of the cabin

HVAC systems. They use systems design and generic knowledge to capture occupant values and preferences. They also conduct flight tests in the lab, on the ground, and in flight with test subject questionnaires and customer support surveys. Thermal comfort simulations are conducted in-house sensation and thermal comfort models are developed using requirements-based engineering tools 73

and airflow simulation tools. Most of these industries use a variety of software tools for modeling.

They stay within the required settings to ensure that design standards and certification guidelines are followed (e.g., minimum outside air requirements). The need for a holistic model to relate the cabin environment to work-related and individual factors was highlighted. In automobiles, this is determined by experience from earlier programs. Vehicles are tested in different weather conditions and measurement and verification of the environmental control systems are conducted.

Figure 4-4 shows the response to the question on the level of control provided to passengers for temperature, airflow, and lighting in ship/car/aircraft cabins. Regarding the level of control provided to the occupants, cruise ships have temperature sensors that are connected to an automatic control system to control the fan speed and to save energy. Passengers cannot completely shut off air conditioning, but they have access to thermostats.

A- Cruise Ship 5 4 F- Aircraft 3 B- Cruise Ship Temperature Airflow 2 Lighting 1 1- None 2- A little E- Aircraft C- Cruise Ship 3- Some 4- A lot 5- Full

D- Automobile

Figure 4-4: End-user Control of ECS

The lighting in cabins is mostly on and off with no dimming. In automobiles, the passenger/driver can adjust the airflow in 4-7 different modes. The temperature can also be adjusted. In aircraft, pilots and crew members on aircraft can control the temperature of different zones in the aircraft. Passengers can control the airflow through air flow nozzles. They can adjust the lighting with the shading devices and personal task lights, in times of takeoff and landing the

crew members override the lights. Overall, it was observed that the end users had some level of control over the temperature, airflow, and lighting and they could adjust them to their preferred setting. Automobiles seemed to provide more end-user control. The levels of control differ where they might have full control including dimming while others may have on/off control.

It was important to determine the influence of energy efficiency on end-user comfort in the cabins. For the cruise ships, there are tradeoffs to comfort and achieving energy efficiency. The architects work with the shipyard and the owner to write design specifications and develop the design. One of the respondents mentioned that efforts to improve energy efficiency could sometimes lead to reduced comfort since there is less fresh air and more odors in the cruise ship cabins, it requires striking a balance. The aircraft cabin experts seek ways to add value, achieve fuel economy, comfort for the passengers and crew members, and improve performance. Bleed air is the outside air flow; they have an allowable requirement per passenger per flight to reduce the engine offtake without negatively impacting thermal comfort. They seek energy saving measures like recirculation of cabin air which improves comfort by avoiding the use of dry outside air, taking advantage of the humidity in the recirculated air and removing unwanted heat during a flight. They mostly felt that improving energy efficiency conflicts with maximizing the comfort of end users.

Cruise ships travel to different climates, so there are moments of temporary discomfort as the systems adjust to a new environment. Some of the cruise companies ask an operator to explain the changes that are taking place and to educate the passengers. In the aircraft industry, they optimize the systems and size them for extreme cases since they are required to meet the code regulations on extreme days. They use sophisticated tools and engineering expertise to carefully balance the challenging and conflicting requirements to reach the optimum comfort concerning the environment and operating costs. All the industries follow ASHRAE standards and other ISO standards relevant to the system being designed.

The key considerations in the design of cruise ship cabins are minimizing weight, noise, time of installation, space constraints, size of the equipment and plant room. For the control and operation, they seek to ensure comfort for the occupants, motion sickness, and seasickness can worsen if the cabins are uncomfortable. Illnesses could also spread rapidly if the right amount of ventilation and hygiene is not maintained. Cruises could last from a few days to several weeks, so it is important to ensure the comfort of the passengers and contain and control the spread of diseases in case there is an outbreak. In vehicles, one of the main concerns is controlling fog and ensuring the screen is clear to have a good view which could be a safety concern. Other considerations are making vehicles lighter to improve the efficiency. They seek to achieve a temperature of 22°C

(71.6°F) quickly even in very low and very high temperatures. In aircraft cabins, they seek ways to add value, fuel economy, and comfortable environment and to improve comfort for the passengers and crew members, meet the design specifications, provide adjustable temperature controls, and individual air outlets. They work toward providing the preferred cabin environment.

4.4.1. Cross-case Analysis

The themes that were observed from the response to the questionnaires and interviews are presented in this section. These themes reveal the factors that contribute to improved end-user satisfaction in these sectors. Although the case studies were limited in number, the responses covered some of the important aspects of environmental control systems and end users in these cabins. The cross-case analysis is used to draw out themes across different case studies and is important as an evaluation tool (Mathison, 2005). Table 4-4 elucidates the themes drawn from each of the case studies. The cross-case analysis shows that some of these themes were discussed through the study responses. The themes were selected based on the literature review and the responses to the questionnaires. In some of the responses, the themes were not explicitly mentioned, but the

industries might have measures in place to address these contributing factors. Also, the sample size is quite small for each case study.

Table 4-4: Contributing Factors to End-user Comfort in Various Sectors

Case Study A B C D E F Satisfaction Surveys ● ● ● ● ● Design Standards ● ● ● ● Individual/personalized Controls ● ● ● ● ● ● Indoor Environmental Comfort ● ● ● ● Value to Customers ● ● ●

Contributing Factors Contributing Testing and Optimization ● ● ●

4.4.2. Enabling and Activating Factors

Several factors have been identified to enable improved integration of end-user values and preferences in the building industry through the preliminary study of other industries. The primary enabling factors are the collaborative approaches to design, the incorporation of end-user feedback, and their quest for providing value to their clients and customers. Competitive benchmarking improve their performance and help them set higher goals. Their response to policy and legislative requirements to improve while also maintaining steady improvements in their industry and working to reduce the impact of their activities on the environment is also an enabling factor. The building sector needs to improve approaches used in buildings to address end-user requirements better.

4.4.3. Barriers or Restraining Factors

The barriers to this integration are lack of holistic approaches to incorporate end-user values with satisfaction and energy efficiency, inadequate information sharing due to propriety reasons, and the need to stay ahead of the competition. This could sometimes hinder advancement where a different organization might be able to contribute to another organization’s tool or approach. The barriers that showed up in the literature were mostly consistent with what was reported by the participants in the questionnaires and during the interviews. 77

4.5. Lessons Learned for the Building Sector from Other Industry Sectors

The recommendations and lessons learned from the study of other industries for adaptation to buildings are summarized as follows:

 There is a continuous evolution of intelligent and high-performance buildings. This sometimes

means less control is provided to end users since buildings are expected to perform according

to prescribed rules. It is vital for buildings to focus on providing value to the end users.

 For indoor environments to provide genuine benefits to occupants, approaches that better

represent occupants should be explored for buildings such as incorporating the right level of

autonomous agent representation (individual end-user preferences).

 Personalized profiles such as those used in automobiles can be used in buildings to predict the

preferred indoor environmental conditions based on the occupant’s inputs.

 The industry should work towards finding the balance between end-user driven controls and

automated controls. To avoid misuse in a case of complete end-user control and extreme

discomfort when the building system does not seem to provide the expected comfort to the

occupants.

 The industry can encourage the use of dashboards to increase awareness of energy usage; the

occupants may be able to make more informed decisions when the information is readily

available to them.

 Building sensor data should be utilized to improve the system operation and diagnostics. The

data should be utilized by the building control system to improve occupant satisfaction.

Valuable data on the building operation is sometimes collected but not fully utilized especially

data that incorporates occupant behavior. Most buildings have smart meters installed that

provide useful data to understand and improve a building operation. Data should be used for

improved fault detection and diagnosis and to enhance performance.

4.6. Discussion of Other Industry Sectors

Although the cabin environment in the aerospace, shipbuilding, and automotive industries cannot be directly compared with buildings, there were some lessons learned that could be applied to buildings. From the key considerations for their designs, these industries seek to provide value to the end users and owners; they also seek to improve energy efficiency. It was observed that design standards are strictly followed, but they also encourage continuous improvement through strict quality controls while striving to maintain a competitive advantage. They endeavor to incorporate end-user feedback to improve their designs. The environmental control systems in cars and aircraft provide a level of control to the end users. They improve end-user experience by enhancing cabin lighting, thermal comfort and providing some microclimatic controls- providing local controls beyond the global controls for the end users.

Rather than take a fragmented approach, different teams work together to design the systems. The building industry should push for more integrative approaches to working in teams while involving different stakeholders. Psychologically, end-user control gives occupants a feeling of control over their space, which could them more comfortable and minimizes energy waste. There are tradeoffs between maximizing comfort and reducing energy consumption. Most of these sectors try to find a balance through optimization of different systems. Optimization is used in the design of building systems, but a simulation of different scenarios with occupant information can enable an improved understanding of the areas of improvement.

4.7. Summary

This chapter covered the study of other industries to identify how they provide value to end users during operation. The transportation sector is different from buildings but some similarities can be observed between the cabin environments in these industry sectors and indoor environments in buildings. Some lessons were drawn from these sectors for buildings. For the building industry,

more demonstration buildings are needed to experiment with different approaches, demonstrate the feasibility of different technological solutions using integrative approaches, and highlight how value can be delivered to end users. It also highlighted the need for more occupant value-sensitive operation in buildings.

Chapter 5

BUILDING MONITORING: AN INTERACTIVE APPROACH

5.1. Introduction

Understanding energy use in the context of the building operation is vital to identifying areas of improvement and gaining knowledge of occupant interaction with building systems.

Various approaches have been employed to predict energy consumption, but in the absence of adequate occupant-related data, it is challenging to establish the right inputs and obtain predictions that reflect occupant characteristics (behavior and preferences) (Abraham et al., 2017a). From the study of other industries, the importance of data collection was discussed for diagnostics and to improve performance. In the built environment, sensing technologies serve a variety of purposes.

They are used to improve the efficiency of building systems, for energy conservation, measurement, and verification, for fault detection and diagnosis, and to identify opportunities for energy saving (Becerik-Gerber et al., 2014). One of the applications of sensors in building energy projects is to monitor and track energy use, occupant presence, and indoor environmental conditions. This is done to improve a building’s performance by linking the sensed data with building controls.

Human behavior is difficult to model so to obtain better predictions and more accurate simulation; it is important to be able to track occupant behavior over a period. People have different indoor environmental needs (i.e., preferred temperature and lighting) and to achieve the comfort levels they desire, they might try to adapt the indoor environment to their needs which can waste energy. The impact of occupant behavior on energy consumption is difficult to quantify accurately

(Hong et al., 2016). A human-centered approach to improving building energy efficiency has been discussed emphasizing the importance of maintaining occupant values, health, and well-being through systems that adequately account for occupant-related characteristics (Abraham et al.,

2015). Some of the adaptive behaviors that can impact energy consumption are adjusting thermostats, adjusting lighting levels, and opening and closing windows while non-adaptive behavior includes the use of additional devices for space conditioning (Yan et al., 2015).

This chapter presents the sensing approaches to monitoring energy consumption by three main end-use categories [heating, ventilation, and air-conditioning (HVAC), lighting, and plug loads]. Occupant-related energy use behavior and changes in indoor environmental conditions are considered over a period through field measurements using a combination of sensors and surveys.

Outdoor weather data is collected separately from nearby weather stations. The implementation and results from the case studies of residential and commercial buildings, in Doha, Qatar, and

Pennsylvania, USA, using this interactive approach are presented in Chapter 6.

5.2. Building Energy Monitoring

Building energy monitoring involves the tracking of energy use in a facility using sensors to detect the parameters being measured, a data acquisition system for storage of sensed data, and an interface for visualizing or presenting the data (if required). The scope of the energy monitoring project will dictate the suitable data sensing system to be implemented. For instance, in buildings that need continuous tracking of energy use for measurement and verification, sensors can be deployed to monitor the whole building energy consumption and determine if the building is performing as intended. Approaches to monitoring building energy use include Non-Intrusive Load

Monitoring (NILM) and Intrusive Load Monitoring (ILM). In NILM, energy consumption is measured from the main meter and disaggregated using algorithms that read signals at specific sampling frequencies from devices that are connected while ILM uses additional sensors to sub- meter energy use depending on the level of granularity desired (Kazmi et al., 2014). Rafsanjani et al. (2015) reviewed various aspects of occupant-related energy use behavior in commercial buildings and emphasized the need for tools to track the energy use of individual occupants. Several

guidelines are available for the selection and installation of sensors for building energy monitoring

(Healy, 2010; Barley et al., 2005; Ahmad et al., 2016). From the comprehensive review of metering solutions conducted by Ahmad et al., (2016), the need for interoperability between different sensors was highlighted since manufacturers have different protocols for their sensors which make it difficult to combine data from different manufacturers directly. In large-scale deployments, considerable amounts of data are generated from sensors so having a good data management plan is key (Becerik-Gerber et al., 2014) while also ensuring a robust platform for collecting and analyzing the data is available.

ILM has been used to sub-meter power consumption in buildings. This approach provides more reliable information on energy use profile which can help to detect errors. ILM system architecture can be placed into three categories namely ILM1 (sub-meters), ILM2 (plug level) or

ILM3 (appliance level) (Ridi et al., 2014). They differ as the level of granularity/number of sensing points increases which also means more sensors will be required. Characterizing the signals for individual end-use categories and implementing these in areas with diverse equipment types might be challenging with NILM (Wang et al., 2012). Sub-metering, when the panelboard configuration allows it, has been used to monitor energy use for major end-use categories since it does not require as many sensors as appliance-level monitoring and it could be more accurate than disaggregating whole building energy use through NILM. Sub-metering measures a subset of energy usage for more granular billing or energy consumption analysis. It helps to monitor energy use more accurately and is beneficial in evaluating the performance of systems connected to it and to identify problems with the systems. During sub-metering, wireless technology can be used to store data on a server allowing it to be accessed via the Internet. Conventional data logging can also be used; however, collecting data through wireless technology and cloud-based systems provides immediate access to the results of several projects running simultaneously.

Different devices can be used for electricity sub-metering (NSTC, 2011). The three main devices for electrical sub-metering are:

 Feed-through meter: is used where the meter is connected to the receptacle and the plug

load. It is good for measuring loads at low power or standby modes.

 Current-transformer (CT) meter: is used for loads more than 400 Amps and it requires more

complicated installation.

 Electronic non-socket meter: is an electronic clamp-on CT probe meter and can be installed

quickly. They are more suitable for higher loads and are limited to alternating current (AC)

loads.

Some of the desirable qualities of sensors are the ease of installation and ease of integration into building monitoring and control systems (Healy, 2010). Sensor technology keeps improving and using information generated from sensors can potentially bring about significant energy savings. It is estimated that about 18% can be saved on HVAC energy consumption, 28% on plug loads and 33% on lighting energy consumption in office buildings using smart technologies and sensors (Perry, 2017). Sensors can transmit data through wired and wireless networks. These methods have different advantages over each other. For instance, wireless sensors are easy to install and provide immediate access to data and can be beneficial to detect problems when there are outages in the system. Wired sensors require cables that might need more effort to be installed.

They sometimes perform better than wireless sensors in handling large volumes of data, but the information is not as easily accessible as for wireless sensors. Sensors are a key component of building automation systems. Examples of sensors used in buildings are temperature, carbon monoxide (CO), carbon dioxide (CO2), relative humidity, smoke, occupancy, and light sensors.

They collect data on the building activities and this information can be used to appropriately control and manage the indoor environmental quality and monitor energy consumption in buildings.

The influence of building occupants on energy consumption should not be overlooked since studies have shown that, though difficult to accurately quantify, occupant behavior can significantly impact energy use (Hong & Lin, 2012; Yu et al., 2011). A sensor-based approach to capture human behavior can be costly and is limited in the kind of behaviors it can track. Yan et al.

(2015) identified methods of monitoring occupant behavior namely observational studies, surveys and interviews, and laboratory studies. Surveys provide a good avenue to capture behavior, some of which are influenced by the indoor environmental conditions (i.e., temperature, relative humidity, and CO2) levels. The data required for basic occupant behavior studies include weather data, indoor space data, energy use data and occupants’ data (Hong et al., 2016). A few studies cover aspects of occupant behavior but are not long enough to include seasonal changes in energy use. Excluding the human factor could mean a vital factor that impacts energy use in buildings is being overlooked.

5.3. Instrumentation Plan Development and Deployment

Before the development of the interactive system for energy and environmental monitoring, the goals of the energy monitoring were outlined which are to measure energy consumption, indoor environmental parameters, outdoor weather conditions, and collect occupant- related data at specified intervals for the selected case study buildings. The data was intended to provide insight on occupant-related energy use behavior and indoor environmental conditions. The three case study buildings used in this research (further described in Chapter 6) consist of an office space in a commercial building in Pennsylvania, a single-family home in Pennsylvania, USA, and an office space in a commercial building in Doha, Qatar. Pennsylvania and Doha have different climatic conditions, but the buildings were selected to understand occupant-related energy use in different contexts. It was important for the buildings in both locations to be somewhat similar to allow for comparative analysis. Some of the considerations for selection of the buildings were the

suitability for the study and the availability and willingness of the owners and occupants to participate in the study. The information collected for each of the case study buildings is presented below:

 Location and climate (outdoor temperature and humidity)

 Year of construction

 Building size

 Building system type

 Energy source

 Occupancy

A review of available monitoring strategies was conducted. Experts in the field were consulted for advice on the appropriate strategies to adopt. Since capturing human factors is an important part of the study, a combination of methods was chosen including the use of sensors and data loggers for field measurements and a Preference Monitoring Application (PMA) which was developed for tracking behavioral patterns and occupant satisfaction. The PMA was created on a survey development platform. It was designed for occupants to provide feedback on a daily basis or as frequently as they would like to report on the indoor environmental conditions.

The sub-metered end-use categories are the HVAC systems, lighting and plug loads. The indoor temperature, relative humidity, CO2, illuminance, and particle count (PM2.5) are also monitored. The experiments were conducted for about 12 months to monitor energy use and occupant behavior with seasonal changes. The weather data including outdoor air temperature and relative humidity were collected from nearby weather stations. Time steps of 15 minutes or 1 hour were used. Yan et al. (2015) suggested this interval might be suitable for occupant monitoring. A variety of sensors were deployed for this study to achieve the monitoring objectives. Since some of the buildings use a combination of energy sources, that is, electricity and gas, additional

considerations were made for monitoring the energy used by the non-electrical sources. The sensors were configured, calibrated and tested before installation. The accuracy of the sensors was also noted to calculate the measurement error. They were installed following guidelines provided in the literature (Barley et al., 2005; Wang et al., 2012). Due to budgetary constraints, it was important to find cost-effective solutions and make provision for maintenance and removal of the sensors at the end of the experiments.

5.3.1. Considerations for Data Sensing System

Various techniques have been developed to monitor buildings and, depending on the intended use of the system; there are a variety of configurations for the sensing system. A review of energy metering and environmental monitoring was completed (Ahmad et al., 2016) and the following criteria were determined to be important namely: accuracy, ease of deployment, communication protocol, granularity, cost, and availability. The considerations for the instrumentation and sensor deployment are presented below.

 Placement of sensors: is dependent on various factors such as the proximity to external factors

that can influence the measurements being captured or affect the sensitivity of the sensors to

the physical quantity being measured (ASHRAE, 2004). For instance, a room temperature

sensor should not be placed close to heating or cooling sources.

 Level of granularity of data: is dependent on the intended use of the data, more granular data

gives a greater level of detail but may require more storage and processing.

 Communication type: can also be used to assess and transmit the data to a repository using a

wired/wireless network.

 Parameters measured: A standalone sensor measures parameters separately (i.e., CO2 only)

while an integrated sensor can measure multiple parameters at the same time (i.e., temperature,

relative humidity, and CO2). An integrated sensor allows for faster installation and more 87

convenient data acquisition since fewer sensors are needed to collect data on a variety of

parameters.

 Data storage type: onboard memory or local storage using data loggers can be cheaper but more

time-consuming. There is an increased chance of missing data and errors may not be detected

on time. Cloud-based data is more easily accessible, and data can be tracked and monitored

remotely.

Among these considerations, the building type and configuration were some of the key factors that determined the selection of the instruments. The cost was also an important factor to ensure that a balance was achieved between the accuracy of the measurements and cost- effectiveness. Table 5-1 shows the different parameters being considered for the study.

Table 5-1: Parameters for the Interactive Sensing System

Parameter Unit Temperature °C Relative humidity % Indoor Carbon dioxide Ppm Measurements Light intensity Lux Particle count µ/m3 Temperature °C Outdoor Humidity % Measurements Solar irradiance W Wind speed m/s Plug loads kW Energy Lighting power kW Consumption HVAC power kW Gas Therms Window state - Use of portable heater/fan - Occupant Adjust lighting - Behavior Adjust thermostat Adjust shading devices - Adjust clothing -

The sensing system should have sensors, measuring equipment, a platform for data collection, a means of data transfer and storage and analysis. Details of the measuring instruments for each case study building will be presented in Chapter 6.

5.3.2. Indoor and Outdoor Environmental Conditions

Occupant activities, building operation, and outdoor weather conditions affect the IEQ and energy consumption in buildings (Kibert, 2013). Energy is consumed to maintain indoor environmental conditions in buildings with active building systems. Thermal comfort, lighting quality, indoor air quality (IAQ) and noise affect the indoor environmental conditions (Bluyssen,

2009). This research focuses on the parameters that affect energy consumption and occupant comfort so noise will not be considered. Thermal comfort is defined as the ‘condition of the mind that expresses satisfaction with the indoor environment’ and is influenced by the ambient temperature, relative humidity, radiant air temperature, airspeed, clothing and metabolic rate

(ASHRAE, 2004). A number of parameters determine lighting quality, but this study focuses on illuminance (how the human eye perceives light). IAQ is determined by the carbon dioxide (CO2) levels indoors and the particulates in the air. Indoor environmental quality has been introduced in detail in Chapter 3. Health, comfort, and productivity can be affected by the environmental conditions. Outdoor weather data including temperature and relative humidity, which influence

IEQ, is gathered from local weather stations.

5.3.3. Energy Use Measurements

Energy use measurements can be disaggregated into different levels from whole building monitoring to sub-metering. To measure sub-metered energy consumption for this study, ILM1 was employed for each end-use category (i.e., HVAC, lighting, and plug loads). Figure 5-1 below shows the levels of energy use in buildings. Although ILM is more expensive since it requires the

installation of additional measuring devices, it was more accurate for separating end-use categories for energy measurements. Low-cost devices measure instantaneous power and might require manual recording, mid-range devices include interval data and require manual download while advanced devices collect time series data and are integrated with building energy management systems (Ahmad et al., 2016).

Hot water Gas Space Heating Interior Lighting Exterior Whole Building Energy Space Heating HVAC Space Cooling

Electricity Miscellaneous Electrical Devices

Refrigerator

Plug Loads TV

Hot water Computers

Fans

Figure 5-1: Levels of Energy Use in Buildings

5.3.4. Occupant Feedback System Development

The PMA is one of the components of the interactive sensing system for this study

(Abraham et al., 2017b). It is a 3-5 - minute Web-based survey that collects time-stamped occupant responses. It contains 20 questions with the opportunity to provide open-ended comments. It was

created using Qualtrics survey development software and is accessible on smartphones (Figure 5-2) and computers (Qualtrics, 2015). The PMA was administered to occupants in the case study buildings. The monitored zone in each building where the occupants are located has instrumentation for energy use and indoor environmental parameter measurements. Occupants can respond as often as they want through their unique Web link. Reminders were sent periodically to the occupants to encourage continued participation and consistent response.

Figure 5-2: Screenshots of the PMA on a Smartphone

The questions were checked for clarity and understanding and were refined based on feedback from the Survey Research Center at the Pennsylvania State University, pilot testing with the research team, and initial feedback from the occupants. Questions on activities, clothing, and equipment being used were included to give more context (ASHRAE, 2004). Energy use data are collected at 15-minute intervals for the Qatar building and can be as low as 1-minute intervals for the U.S. building for the duration of the study (12 months). The difference in data collection interval is attributed to the sensor types. Weather data was collected for the two locations from local weather stations measuring historical observations for Qatar (Wunderground, 2017). Weather data was collected from locally installed sensors for the Philadelphia office building measuring actual

outdoor temperature and relative humidity, weather data for the residential building in State College was collected from local weather stations and the NOAA data. Occupant behavior, such as window opening is monitored using state loggers to monitor the change in state in addition to tracking this through occupant feedback.

In developing the questions, a variety of sources were used including the ASHRAE

Thermal Comfort Standard 55 (ASHRAE, 2004) and the Center for the Built Environment (CBE) survey at the University of California, Berkeley (CBE, 2015). Before completing the PMA, occupants completed a background questionnaire to elicit their values and overall satisfaction with the values. They provided background information on their age, gender, and other factors that could influence their energy use habits. The application contains questions on occupant perceptions of comfort (thermal, lighting and airflow) and actions they took to improve their comfort (adjusting thermostats, wearing additional layers of clothing, etc.). Responses to each question were numerically coded for analysis and each participant was assigned a unique ID. The parameters that were measured with the PMA along with the rating scales are presented in Table 5-2.

Table 5-2: Parameters Measured and Rating Scales

Parameter Quantified by

Thermal comfort 7-point scale- Temperature (hot-cold) humidity (too humid - too dry)

Thermal comfort satisfaction 6-point scale (very unsatisfied - very satisfied)

Lighting/visual comfort 7-point scale- Lighting (too dim - too bright)

Lighting level satisfaction 6-point scale (very unsatisfied - very satisfied)

Indoor air quality 7-point scale- Airflow (far too much - far too little)

Indoor air quality satisfaction 6-point scale (very unsatisfied - very satisfied)

Perceived health Impact on health (increased, decreased, no effect, do not know)

Personal productivity Impact on productivity (increased, decreased, no effect, do not know)

The data were analyzed using statistical techniques for the overall responses from each case study building. Each category of response from the occupants was collated excluding incomplete responses. Qualtrics was selected because of its data management capability and interoperability with other statistics tools and spreadsheets (Qualtrics, 2015). Analysis of the data was completed to determine relationships between occupant perception and the indoor environmental conditions. Profiles that typify energy use behavior and preferences of occupants over a period were developed.

5.4. Pilot Studies

Pilot studies are conducted before full implementation of quantitative or qualitative research to determine the feasibility of the study and to identify where changes need to be made

(Given, 2008). Modifications were made to the experimental setup based on observations from the pilot studies. The measuring instruments were set up, configured, and tested before installation.

The case study buildings have different configurations, so the sensing system was adapted to each building. During testing, the sensors were compared with more expensive high-end versions to determine the measurement error and accuracy of the instruments. Some the sampling frequencies were also adjusted for the sensors so that adequate comparisons could be made. The data collection process was tested with sample downloads which informed the creation of a database. The office case studies had some sensors installed and sample data were collected to verify the data and adjust the configuration as required. During pretesting, outliers and incorrect data points were identified.

Pilot testing offered a lot of benefits including modification of the survey based on feedback, reconfiguration of the sensors, improvement of the interaction with building owners and occupants, and the improvement of the data collection process. Simultaneously collecting data to track various aspects of the buildings over different seasons provides opportunities to observe changes in behavior with different outdoor weather conditions.

One of the challenges encountered in the instrumentation was with finding a uniform data sampling rate for sensors with predefined intervals that were not user-customizable. There were instances when communication was lost for the wireless sensors which resulted in missing data, so a maintenance plan was developed to regularly check the data, the power levels of the sensors, the communication status, and the onboard memory of the data loggers. The missing data were interpolated or omitted to minimize errors associated with filling in the data points. Data intervals as little as a minute give a finer level of detail and can better reflect changes in energy use. A trade- off is the time required to analyze the data, the increased equipment costs, and larger memory requirements. The data for the case study buildings was validated using spot checks with other monitoring solutions.

5.5. Summary

An interactive data sensing plan that was developed to capture energy use, track indoor environmental conditions, and monitor occupant-related-energy-use behavior and preferences over a period was discussed. A combination of sensing solutions was employed to achieve the monitoring objectives for this study in a cost-effective way. The collection of meaningful data can improve understanding of occupant preferences and behavior. Given the limitations of some of the sensing systems selected, it may be worthwhile to invest in more sophisticated tools with better accuracy and save the time required for collating and preprocessing the data, especially for a long- term monitoring project. One of the problems that might be encountered is the lack of interoperability between sensors from different manufacturers due to having different protocols.

Using a uniform protocol across different manufacturers will aid interoperability of different sensing solutions and enable easier implementation, organization, and interpretation of data. Case studies of buildings using an interactive monitoring approach are presented in Chapter 6.

Chapter 6

BUILDING ENERGY MONITORING CASE STUDIES

6.1. Introduction

Building energy consumption, indoor environmental parameters, and occupant behavior and preferences were monitored to gain an understanding of occupant behavior and the impact of the indoor environmental conditions on occupant values. Three operational case study buildings were selected for the empirical energy studies. The buildings selected for the case studies are described in this chapter along with the monitoring plan and instrument setup for each building. An overview of the interactive sensing plan is shown in Figure 6-1. Objective measurements using sensors, data loggers, and metering devices were taken. Subjective measurements using occupant feedback on the PMA were also collected. It was imperative to define occupant values and determine how they impact occupant satisfaction and energy consumption indoors.

Energy Use IEQ Outdoor Weather Occupant • Metering Devices and • IEQ Sensors • Weather Stations Preferences and Sensors Behavior • PMA and Sensors

Figure 6-1: Parameters for Interactive Sensing Plan for the Case Studies

The objective measurements were taken using different sensor types; this introduced some challenges since the sensors sometimes had different data structures and the data collection intervals had to be adjusted so that time series comparisons could be made. For each building, two types of occupant feedback were collected, one-time feedback giving background on occupant values using a value elicitation tool (VET) and continuous feedback on their preferences and perceptions using a preference monitoring application (PMA) (Figure 6-2). They were developed using the Qualtrics survey development platform (Qualtrics, 2015). 95

Figure 6-2: Occupant Feedback Collection Tools (VET and PMA)

The behavior considered in these case studies included the occupants’ use of windows and shading devices, adjusting thermostats, adjusting lighting levels, and the use of portable devices such as heaters and fans. From the data collected in the case study buildings, a comparative analysis was completed to identify the differences in energy use for both locations. A brief description of the case study buildings is presented in Table 6-1. The buildings comprise two offices, one in Doha,

Qatar and one in Pennsylvania, USA, and a residential building in Pennsylvania, USA. The buildings were selected based on the size, the potential for sub-metering, and the owner and occupants’ interest in participating. Two case studies were initially selected for each region to enable a comparison of residential and office settings, but one had to be left out due to unforeseen circumstances. Two main tools were used for the data preprocessing, analysis and visualization namely: Microsoft Excel and MATLAB. The surveys were available to all the occupants, so those that were interested in participating in the study were included. The three case studies were completed over different durations varying between 8 months and one year. Each case study is described in detail in the following sections. Subsets of the data collected enabled a cross

comparison with occupant feedback and indoor environmental conditions. The implications of the data collected and the limitations of the study are also discussed.

Table 6-1: Description of Case Study Buildings

Location Philadelphia, PA, Doha, Qatar State College, PA, USA USA Building Name PBTC Building 7 Web house Building Type Medium-sized Medium-sized Low-rise single-family commercial building institutional building home Year of 1927 (1983 2005 1965 (2013 retrofit) Construction converted) Building Size 930 m2 (10,000 ft2 of 680m2 (7,300 ft2 of 290 m2 (3,100 ft2) 272,000 ft2 total) 34,272 ft2 total) Number of Floors 6 (4th floor, west 2 (1st floor, west 2 floors plus basement zone office zone office (whole building monitored) monitored) monitored) Climate 4A mixed humid Subtropical desert/ 4A mixed humid climate low-latitude arid, hot climate climate Heating and AHUs and steam Chillers, VAV High-efficiency, mini- Cooling System boilers split heat pumps, electric wall heater(basement), and gas fireplace Energy Source Electricity and steam Electricity Electricity and gas Power Three-phase Three-phase Single phase Distribution No of Occupants 20-25 25-30 4

6.2. Case Study 1- U.S. Office Building

The U.S. case study office building, PBTC, is in Philadelphia, Pennsylvania and is a mixed- use commercial building comprising a café, a school, a warehouse, and offices with an ENERGY

STAR score of 88 (performs better than 88% of buildings with similar characteristics nationwide).

ENERGY STAR is an EPA program that certifies buildings based on its energy efficiency. PBTC is a six-story masonry building, a 10,000 ft2 office space on the fourth floor (west zone) was selected for this study (Figure 6-3). 97

Figure 6-3: U.S. Office Building Location and Building Elevation

Screenshots from the building management system (BMS) are presented in Figure 6-4. The building energy use and environmental monitoring data are downloaded from the BMS as an XML file. The building was already being monitored for heating and cooling energy use but additional sensors were included to sub-meter the lighting energy consumption and the plug loads for the monitored zone at the panelboard.

Figure 6-4: Screenshots of U.S. Office Data Collection Interface and BMS on Coresight

Isolating the steam usage for the area being monitored was not feasible, so the utility bills were used to estimate the gas contribution towards heating based on a square-foot estimate. Sensors

were already installed in different zones and rooms to monitor the temperature, relative humidity, and carbon dioxide, additional sensors were installed to measure the illuminance and particle concentration levels in some of the rooms. Occupants reported on their behavior and preferences indoors using the PMA. The layout of sensors deployed for the zone being monitored is presented below (Figure 6-5) while the full building instrumentation plan is presented in Appendix E.

Figure 6-5: Sensor Layout in U.S. Office (Modified from Coresight)

About half of the occupants in the offices participated in the study. Regarding local controls available to occupants, the lighting is automatically controlled and responds based on occupant presence. The windows are not operable and the thermostats cannot be manually adjusted.

U.S. Office Building Instrumentation Plan

Sensors were installed to monitor different indoor environmental parameters. Figure 6-6 shows a few of the sensors installed for environmental and energy monitoring. The parameters are measured directly through sensors or monitored indirectly through the PMA surveys provided to the occupants. The PMA allows occupants to report on their behavior and preferences indoors.

(a) (b) (c) Figure 6-6: Sample Sensors Installed in the Office Building (a) IEQ sensor, (b) Power Monitoring Solution, (c) Temperature and RH Sensor

The behavior being considered includes their use of windows, shading devices, adjusting thermostats, adjusting lighting levels, and the use of portable devices such as heaters and fans. The sensors that were installed in the office space are shown in Table 6-2.

Table 6-2: Instrumentation Plan Showing the Sensors Used in the U.S. Case Study Buildings

Measuring Measurement Parameter Unit Instrument Frequency* Temperature °C Automated Logic ZS 15 minutes Relative humidity % Pro sensors for 15 minutes Indoor temperature, RH, and Carbon dioxide Ppm 15 minutes Measurements1 CO2 Light intensity1 Lux 15 minutes Particle count1 µ/m3 1 hour Plug loads kW 15 minutes Energy Lighting power2 kW WattNode WNC-3Y- 15 minutes Consumption 208-BN HVAC power kW 15 minutes Gas3 Therms Utility bills 15 minutes Window state - Onset UX 90 open/close state Use of portable PMA and power - - heater/fan measurement Occupant Adjust lighting - PMA - Behavior Adjust thermostat - PMA - Adjust shading - PMA - devices Adjust clothing - PMA - *Sensing frequency for the office building was configured for every minute or hour 1IC Sentinel indoor environment sensors are installed in both buildings and can measure all the indoor parameters, 2Current sensors to sub-meter end-use categories in residential building 3Gas is measured in Therms from the utility bills but can be converted to kWh (1 Therm=29.3001 kWh) 100

6.3. Case Study 2- Qatar Office Building

An office space within the College of the North Atlantic Qatar (CNAQ) in Doha, Qatar was used for this case study. Professors and instructors occupy the selected zone (Figure 6-7) at the college. On average, the occupants spend about 20 hours a week in the office since most of their tasks involve teaching in other parts of the building. Additional information about the building is presented in Table 6-1. All the occupants are in private offices and can control the thermostats within a range of 8°C (18-26°C). The building has an automation system that controls the whole ventilation and air conditioning system, which is a single duct variable air volume (VAV) system.

Figure 6-7: Screenshot of the CNAQ BMS

Each room has a temperature and relative humidity sensor. Three air handling units serve the monitored zone and each one has an air quality sensor. Illuminance sensors were installed in 10 of the 22 offices in the zone. The selected rooms represent different areas of the office namely: the interior offices at the core, and a few offices at the perimeter of the building with access to daylight, and some with interior shading devices, and others without shading. There is also shading wall on the north exterior wall of the building (Figure 6-8).

101

Figure 6-8: Qatar Office Building Location and Building Elevation

Qatar Office Building Instrumentation Plan

The setpoint temperatures varied for each office and are usually between 18 and 26°C.

Occupants can manually turn the lights on and off. Illuminance sensors were placed close to the occupants’ working spaces on their desk. The sensors measure light intensity as seen by the human eye. Figure 6-9 shows the illuminance sensor placement in the zone being monitored. A summary of the instrumentation plan is presented in Table 6-3, full details of the instrumentation plan are included in the appendices.

Figure 6-9: Illuminance Sensor Layout in Qatar Office

102

Table 6-3: Instrumentation Plan Showing the Sensors Used in the Qatar Office Case Study

Measuring Measurement Parameter Unit Instrument Frequency Temperature °C 15 minutes From BMS1 Indoor Relative humidity % 15 minutes Measurements Air quality - 15 minutes LiCOR Illuminance Light intensity Lux 15 minutes sensor Plug loads kW Virtual meter 15 minutes Energy Lighting power kW Power meter 15 minutes Consumption BTU meter Cooling power l/s 15 minutes Ventilation kW From BMS1 15 minutes Window state - PMA - PMA and power Use of portable fan - - measurement Adjust lighting - PMA - Occupant Adjust thermostat - PMA and BMS1 - Behavior Adjust shading - PMA - devices Adjust clothing - PMA -

1BMS- Building Management System

The lighting and HVAC energy consumption are measured with power meters while the plug loads are measured virtually by deducting from the total building power consumption. The sensors were tested and calibrated before they were installed. Occupant satisfaction with the thermal

(temperature and humidity) conditions, lighting, and indoor air quality are monitored through the

PMA. The indoor conditions and energy use measurements were monitored with sensors and stored in the data acquisition system. The data was retrieved from the Vista workstation on-site every other week to check the quality of the data and ensure there are no technical problems. Remote access to the workstation was not granted due to security concerns. Information on outdoor weather conditions was collected from local weather stations, some of which are available online.

103

6.4. Case Study 3- U.S. Residential Building

The third case study is a two-story wood-framed residential building in State College,

Pennsylvania. The building is a detached single-family home with a basement. The original building drawings were unavailable, so a building plan was developed using on-site surveys. Like most U.S. buildings, the house has a centrally controlled programmable thermostat for space conditioning (U.S. EIA, 2013). The IEQ for the entire house was being monitored for the study except for the unconditioned garage. The aerial view and elevation of the house are presented below

(Figure 6-10).

Figure 6-10: Residential Building Aerial View and Rear Elevation

U.S. Residential Building Instrumentation Plan

The circuits were traced at the panel board to separate the lighting, HVAC and plug loads.

Circuits with mixed end-use categories were further disaggregated using additional sensors. Natural gas is used for water heating and the fireplace in the house. The fireplace is sometimes used during the winter season, along with the mini-split heat pumps (in the heating mode). It was important to be able to monitor the gas usage for space heating, so a diaphragm gas meter was installed to separate gas use by the fireplace. The living room is one of the most used rooms in the house and was identified as a space where a variety of behavioral adaptations can be observed such as opening windows and adjusting thermostats. To track window use, state loggers were used and illuminance sensors were also installed to detect the light levels in the space (Figure 6-11).

104

(a) Temperature, RH and CO2 (b) Temperature, RH and (c) IAQ Sensors Illuminance

(d) Gas Measurement (e) Power Monitoring (f) Window State Sensor Figure 6-11: Sensors and Measuring Devices for Residential Building

Additional information about the sensors is presented in Appendix C. A plug load management system was installed to measure power consumption at the circuits that are mixed so that plug loads could be separated from the lighting power.

6.5. Occupant Value Elicitation

Following the selection of the buildings, meetings were held with the occupants to brief them on the importance of the study and to solicit their participation. While occupant values focused on the seven mentioned, the VET served to collect a broader range of information relating to occupants perception, behavior, satisfaction and also some background information. They were sent a link to the VET to provide background information and respond to other questions. The aggregate results for a selection of parameters on the VET for occupants in the two office buildings are presented in Table 6-4. From the occupant’s responses, it was observed that about 92% of the

Qatar office occupants felt it was important for them to be able to control their thermal environment.

Out of those, 25% of them felt it was extremely important. In the U.S. office, about 77% of the 105

occupants felt it was important for them to be able to control the thermal conditions in their environment. They seemed not to have much control over their thermostats. In the U.S. office, about 65% of the occupants used a portable heater and 47% used a portable fan to adjust the indoor thermal conditions. In Qatar, about 50% adjusted the AC unit while 24% used a portable fan. About

53.3% of the U.S. occupants felt that energy cost savings were important to them while 33.3% of the Qatar occupants felt that it was important to them. 86.6% of the U.S. office occupants felt that environmental protection was important to them compared to 91.7% in Qatar.

From the analysis of the responses, we observe a high awareness of environmental protection, but the concern for energy cost savings was different which could be because the occupants are not directly responsible for paying utility bills. 73.3% of the U.S. office occupants responded that they would feel uncomfortable if they had no windows on the wall while 100% in

Qatar felt that they will feel uncomfortable. Most of them felt the absence of windows will decrease their productivity. The occupants prefer to work in an environment that provides acceptable IEQ for improved productivity and comfort. The full results obtained from the VET are included in

Appendix E.

106

Table 6-4: Occupant Values and Background Information on VET

U.S. Qatar Selected Questions Breakdown of Response Office Office (%) (%) How important is it for you to be able to control Not at all important your thermal environment? 0 0.0 Very unimportant 17.6 0.0 Somewhat unimportant 5.9 8.3 Somewhat important 41.2 8.3 Very important 17.6 58.3 Extremely important 17.6 25.0 How often do you regulate the thermostat to make Never the room warmer or cooler? 23.5 16.7 Rarely 11.8 33.3 Sometimes 0.0 8.3 Most of the time 11.8 0.0 Always 0.0 0.0 I cannot control the thermostat 52.9 41.7 How often do you use a portable heater/adjust the Never AC unit? 35.3 50.0 Rarely 11.8 25.0 Sometimes 35.3 25.0 Most of the time 5.9 0.0 Always 11.8 0.0 How often do you use a portable fan? Never 52.9 75.0 Rarely 11.8 8.3 Sometimes 29.4 16.7 Most of the time 5.9 0.0 Always 0.0 0.0 How important is energy cost savings to you? Not at all important 0.0 8.3 Very unimportant 26.7 0.0 Neither important nor unimportant 20.0 58.3 Very important 40.0 8.3 Extremely important 13.3 25.0 Not at all important How important is environmental protection to you? 0.0 0.0 Very unimportant 13.3 0.0 Neither important nor unimportant 0.0 8.3 Very important 53.3 50.0 Extremely important 33.3 41.7 Background Information Age Distribution 18-24 years 6.7 0.0 25-34 years 40.0 0.0 35-44 years 6.7 41.7 45-54 years 13.3 50.0 55-64 years 6.7 8.3 65-74 years 26.7 0.0 75 years and over 0.0 0.0 Gender Distribution Male 20.0 41.7 Female 80.0 58.3 107

6.6. Occupant Preference Monitoring

The following figures show the response rates on the Preference Monitoring Application

(PMA) as reported by the occupants in the three case study buildings from March 2016 until April

2017 (Figure 6-12, Figure 6-13, and Figure 6-14). The data was collected along with the empirical measurements for each building. The response rates were variable and the highest number of responses was collected from the Qatar office. There were more female participants than male participants from the offices.

90 80 70 60 50 40 30 20

Number Responses Number of 10 0

PBTC, USA Building 7, Qatar Home, USA

PBTC U.S.: 447 responses, Building 7 Doha: 641 responses, U.S. residential: 44 responses (from June 2016)

Figure 6-12: Response Rates on PMA over 12 Months

600 12 10 500 8 400 6 300 4 200

2 100 Number Number Peopleof

0 Number Responses of Male Female 0 Male Female Participants No of Responses US 3 10 US 23 430 Qatar 6 7 Qatar 106 535 (a) (b) Figure 6-13: PMA Breakdown in Office Buildings over 12 Months (a) By Number of Participants (b) By Number of Responses

108

250 250 200 200 150 150 100 100

50 50 Number of of Number Rsponses

Number of of ResponsesNumber 0 0

10014-F 10010-F 10015-F 10002-F 80016-F 10008-F 10013-F 10018-F

20001-F 20018-F 20014-F 20020-F 20010-F 20013-F 20008-F 20011-F 20015-F 20002-F

10004-M 10017-M 10012-M 10005-M 10001-M

20019-M 20005-M 20004-M Occupant ID Qatar Occupant ID USA

(a) (b) Figure 6-14: Number of Responses on PMA per Occupant over 12 Months (a) Qatar Occupants (b) U.S Occupants

6.7. Approaches to Data Analysis for Empirical Energy Studies

The approaches to data analysis for this study have been discussed in Chapter 2.6. The steps taken to analyze the data collected from the empirical energy studies are presented below.

 Data extraction - the data was downloaded from the data loggers for each sensor. Table 6-2

shows the data collected from each building.

 Preprocessing - involved checking for missing data and outliers using statistical approaches

including measures of central tendency (mean, median and mode) and measures of

variability (interquartile range- IQR, variance and standard deviation). Subsets of data were

checked, mostly using monthly data and individual occupant feedback data to determine

variability in the responses.

 Data fusion - included collecting data from different loggers and including the results in a

single spreadsheet for analysis. MATLAB was used to combine different data sources and

exported into an excel file. For instance, the occupant feedback is timestamped for when the

occupant responded. These were mapped to the indoor air temperature measurement at the

hour they reported. The indoor environment and energy use data are recorded every hour. 109

The formula used to determine measurements for the time of the occupant response is

presented below.

푇 + 푇 + 푇 퐴푣푒푟푎푔푒 푇푒푚푝푒푟푎푡푢푟푒 (푇 ) = 푖−1 푖 푖+1 푎푣푒 3

푊ℎ푒푟푒 푇푖 = 푝푎푟푎푚푒푡푒푟 푚푒푎푠푢푟푒푚푒푛푡 푓표푟 ℎ표푢푟 표푓 푟푒푠푝표푛푠푒 푇푖−1 = 푝푎푟푎푚푒푡푒푟 푚푒푎푠푢푟푒푚푒푛푡 푓표푟 ℎ표푢푟 푏푒푓표푟푒 푇푖+1 = 푝푎푟푎푚푒푡푒푟 푚푒푎푠푢푟푒푚푒푛푡 푓표푟 ℎ표푢푟 푎푓푡푒푟

The boxplots show the distribution of data for indoor temperature and occupant’s perception of comfort. The highlighted areas show the acceptable comfort range based on the occupant’s satisfaction levels. Occupant 1 provided 175 responses while occupant 2 provided 116 responses overall. Each response was categorized based on occupant’s perception.

The perception of indoor air temperature by occupant 1 and occupant 2 in the U.S. office building are shown in Figure 6-15. The coding for the responses are Hot (7), Warm (6), Slightly warm (5), Neutral (4), Slightly cool (3), Cool (2), and Cold (1).

27 27

26 26

) ) ℃ ℃ 25 25 24 24 23 23 22 22 21 21

20 20

Indoor Temperature IndoorTemperature ( IndoorTemperature ( 19 19 18 18 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Occupant Perception Occupant Perception

(a) (b) Figure 6-15: Indoor Temperature vs. Perception (a) Occupant 1 (b) Occupant 2

As expected, the perception of temperature showed that on average, as the temperature increased, the perception also increased accordingly. When the temperature was high, the occupants

110

perceived it was hot. The occupant’s satisfaction with thermal comfort was also examined (Figure

6-16). The coding for the responses are as follows, very unsatisfied (1), unsatisfied (2), moderately unsatisfied (3), moderately satisfied (4), satisfied (5) and very satisfied (6). The profiles for building occupants were selected from this analysis but it was only significant for occupants with high responses on the PMA. The range of temperatures at which they were satisfied or very satisfied was selected as the basis of their preferred comfortable summer temperatures.

27 27 )

) 26 26 ℃ ℃ 25 25 24 24 23 23 22 22 21 21

20 20 Indoor Temperature IndoorTemperature ( Indoor Temperature IndoorTemperature ( 19 19 18 18 1 2 3 4 5 6 1 2 3 4 5 6 Occupant Satisfaction Occupant Satisfaction

(a) (b)

Figure 6-16: Plot of Indoor Temperature vs. Satisfaction (a) Occupant 1 (b) Occupant 2

Further analysis of the data is presented in the following sections. It includes a cross-case analysis for the U.S. and Qatar office buildings and an analysis of the U.S. residential building results. Only a few of the parameters monitored were selected for this analysis since they were considered as the key parameters that impact occupant comfort. These parameters were selected based on the amount of available data.

6.8. Cross-case Analysis of the Office Buildings

The data collected from the U.S. and Qatar offices are presented and compared in this section. In the U.S., working days are Monday to Friday while working days in Qatar are from

111

Sunday to Thursday. Figure 6-17 shows a comparison of the yearly outdoor temperatures in the cities where the office case studies are located. A comparison of energy use, indoor environmental parameters and occupant feedback for June 2016 was completed. June was selected as the month for the comparisons since it was summer in both locations and the buildings were fully occupied at that time. There were 54 responses from 5 people in the Qatar office and 38 responses from 6 people in the U.S. office.

Figure 6-17: Maximum and Minimum Monthly Outdoor Temperatures- Doha and Philadelphia (Weatherspark, 2017)

6.8.1. Comparison of Occupant Feedback on PMA in in the Office Buildings

Figure 6-18 shows the distribution from the PMA reports for June 2016. For the rating scales, -1 to +1 represent an acceptable level while -3 and +3 are the extremes (too high or too low) for each parameter. The responses on their perception of each parameter almost followed a normal distribution. Most of the responses seem to be within the normal range except for the responses on the perception of lighting levels in Qatar and the perception of the temperature in Qatar. As a check for validity of the occupant’s responses, the variability of the responses using the variance, standard deviation and confidence interval were computed.

112

80 70 60 50 40 30 20

Percent Percent Responses of (%) 10 0 -3 -2 -1 0 1 2 3 Perception of IEQ Parameters Temperature Qatar Temperature US Humidity Qatar Humidity US

IAQ Qatar IAQ US Lighting Qatar Lighting US

Figure 6-18: Feedback on the Perception of the IEQ Parameters

The impact of the indoor environmental conditions on perceived health and personal productivity in both buildings for June are presented below (Figure 6-19). While the responses indicate that the indoor environmental conditions decreased the occupants’ perceived health in the

Qatar building, the U.S. occupants mostly reported that it did not affect their perceived health and personal productivity.

90 80 70 60 50 40 30 20

Percent of Responses (%) 10 0 Perceived health Qatar Perceived health US Personal productivity Personal productivity US Qatar

Increased Decreased No effect I do not know

Figure 6-19: Feedback on the Impact of the IEQ Parameters on Perceived Health and Personal Productivity

113

Regarding satisfaction with the IEQ, Figure 6-20 shows that based on the responses in June

2016, about half of the occupants were unsatisfied with thermal comfort in Qatar while about half were moderately unsatisfied or unsatisfied with the lighting levels in their offices. There was high satisfaction with lighting in the Qatar office, and with IAQ and thermal comfort in the U.S. office.

Lighting US

Lighting Qatar

IAQ US

IAQ Qatar

Thermal Comfort US

Thermal Comfort Qatar

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Very unsatisfied Unsatisfied Moderately unsatisfied Moderately satisfied Satisfied Very satisfied

Figure 6-20: Occupant Responses on Satisfaction with IEQ Parameters

6.8.2. Comparison of Power Consumption Patterns in the Office Buildings

Comparisons of the power consumption in June 2016 for both the Qatar and U.S. office buildings are presented in Figure 6-21. Energy use was broken down into cooling, plug and lighting loads.

Plug Load 8% Lighting Cooling Load Plug Load 15% Load 38% 43% Cooling Load Lighting 77% Load 19%

(a) (b) Figure 6-21: Energy Use Breakdown- June 2016 (a) Qatar Office (b) U.S. Office

114

Although the U.S. office building zone is larger than the Qatar office zone, the breakdown of energy consumption shows that a large percentage of energy was used for cooling in Qatar while plug loads accounted for the largest percentage of energy use in the U.S. office in June 2016. Most office buildings in the U.S. have a high space conditioning load [Figure 3-1(b)]. The plug load power consumption in the Qatar office for a typical week shows a steady consumption profile over the course of the week (Figure 6-22) except having reduced loads during part of the unoccupied hours of the day. The cooling loads did not decrease when the building was unoccupied during the weekend June 17th and 18th, 2016.

Figure 6-22: Weekly Power Consumption Profile Qatar Office- June 12-19, 2016

The power consumption for a typical workday (June 14th, 2016) is presented in Figure 6-23.

There was a slight increase in plug load power around 9:00. This could be as a result of people coming into their office and plugging in devices as they start work. Overall, HVAC (cooling) power was high between 7:00 and 20:00. Cooling power started to increase around 6:00 and then to reduce around 8:00 with a dip around 9:00 until it began to change slightly with changes in outdoor temperature. The changes in cooling power might be due to several factors including occupant behavior (thermostat adjustments), cooling operation mode and outdoor weather conditions.

115

Figure 6-23: Daily (Weekday) Power Consumption Profile Qatar Office- June 14, 2016

The plot showing the correlation between the average illuminance and the lighting power for June is presented below (Figure 6-24 (a)). The analysis of the relationship between the average illuminance and the lighting energy consumption for the ten offices over seven months showed a near perfect correlation but a significant correlation was observed for June 2016. This could indicate some contribution to their indoor lighting use from natural daylighting. The power consumption profile for different offices is presented in Figure 6-24 (b). It clearly shows some activity between 6:00AM and 8:00PM. There is a spike in illuminance and lighting power around

7:00PM in most offices which might be as a result of janitorial activities at the end of the workday.

5000 R² = 0.866 4000

3000

2000 Lighting Power (W)

1000 0 100 200 300 400 Average Illuminance (Lux)

Figure 6-24: Lighting Power (a) Average Illuminance Measurements in Qatar Office- June 2016, (b) Illuminance and Lighting Power vs. Time- June 14, 2016

116

The profiles for occupants with high responses for June in Qatar were analyzed to check for variability and the spread of the data Table 6-5. There is a large spread in the occupant responses for their perception of the temperature on average, they reported the temperature was within the acceptable range.

Table 6-5: Variability of Qatar Occupant Responses on Temperature Perception

Occupant Number of Variance Standard Mean Responses Deviation Occ1 22 0.754 0.868 3.136 Occ2 19 0.659 0.812 3.842

The breakdown of power consumption in a typical week in June for the U.S. office shows clear fluctuations based on occupancy and use of plug load devices. A variation is also observed with changes in outdoor air temperature. Cooling is turned off when the building is unoccupied as shown in Figure 6-25. Some occupants work on weekends which might be responsible for the plug load usage and a slight fluctuation in HVAC power for June 18th, 2016.

Figure 6-25: Breakdown of Power Consumption in a Typical Week in the U.S.- June 12-19, 2016

The power consumption for a weekday highlights the patterns in total plug, lighting and

HVAC loads related to the occupancy levels (Figure 6-26). Equipment in standby mode and other miscellaneous equipment account for most plug load power consumption during the unoccupied

117

hours. There is a slight dip in power consumption at the end of the workday and an increase for a short period which could be due to janitorial activities after work.

Figure 6-26: Daily (Weekday) Power Consumption Profile U.S. Office- June 14, 2016

6.8.3. Comparison of Average Outdoor Temperature with Power Consumption

The average daily outdoor temperature was plotted against the cooling power consumption in the Qatar office (Figure 6-27). The results showed no correlation between these two variables. It seemed like for June 2016; cooling was used regardless of the outdoor temperature which was hot

(37°C).

Figure 6-27: Hourly Average Outdoor Temperature vs. Cooling Power Demand Qatar- June 2016 118

For the U.S. office, the hourly average temperature was plotted with the HVAC power

(Figure 6-28). The data shows a weak correlation between these two variables. Daily demand seemed to vary based on the hourly fluctuations related to occupancy. Most times the cooling was turned off when the building was unoccupied. Both buildings seem to be internal load dominated and not envelope dominated.

R² = 0.2568 HVAC HVAC Power (kW) 5

0 5 10 15 20 25 30 35 40 45 Hourly Outdoor Air Temperature (°C)

Figure 6-28: Hourly Outdoor Temperature vs. Cooling Power Demand U.S.- June 2016

6.8.4. Comparison of Occupant Behavior in the U.S. and Qatar Office Buildings

Occupant behavior in both locations was compared and it was observed that more behavioral interventions were reported in the Qatar office than in the U.S. office building (Table

6-6). The Qatar office occupants have more control to adjust indoor conditions. They have access to the thermostats within a range of temperature and they can open and close the windows. The control of lighting is only on and off and has no dimmers. They did not report opening the windows during the experiments which could be due to the dust and other particles in the outdoor air. The

U.S. office occupants, on the other hand, have no control over the thermostats and cannot open windows. For this period, they reported using a portable heater only once, but this was more common in the overall responses. In both buildings, they opened the door to increase the airflow.

119

Table 6-6: Comparison of Occupant Behavior from PMA- June 2016

Action Related to Qar U.S. Action Qar U.S. Action Related Qar U.S. Thermal Comfort (%) (%) Related to (%) (%) to Lighting/ (%) (%) Airflow/IAQ Visual Comfort

Adjusted 9 0 Opened 1 1 Turned on room 23 18 thermostat to make window to lights the room warmer increase airflow

Adjusted 9 0 Closed window 0 0 Turned off room 3 0 thermostat to make to reduce lights the room cooler airflow

Put on more layers 9 2 Opened the 29 15 Turned on a 0 0 of clothing door to personal lighting increase device airflow Took off layers of 1 1 Closed door to 1 0 Turned off 0 0 clothing reduce airflow personal lighting device

Turned on portable 0 1 Opened shading 6 2 heater device to let more light in

Adjusted AC 2 0

I did not do 19 29 None of the 23 22 None of the 22 18 anything above above

6.9. Results from U.S. Residential Building

The U.S. residential building measurements for October 2016 are presented below. The experiments had not fully begun in June 2016, so a subset of the data was selected from a different period. A large variation was observed in overall power consumption in October 2016 (Figure

6-29). There was no clear pattern of energy consumption on a weekly basis. Total electricity consumption was mostly related to the variation in activities indoors and the use of different devices.

120

8.00

7.00

6.00

5.00

4.00

3.00

2.00 Power Power Consumption(kW)

1.00

0.00

Figure 6-29: U.S. Residential Building Electricity Consumption- October 2016

Figure 6-30 shows the measured indoor temperature, relative humidity, and carbon dioxide in the family room for one week in October (temperature is in Fahrenheit to allow for easier visualization of parameters). Figure 6-31 shows the measurement of the same parameters in the bedroom.

90 2200 2000 80 1800 1600 70 1400 60 1200 1000 50

800 CarbonDioxide (ppm)

Temperature Temperature (F)/RH (%) 600 40 400 30 200

Temp 2, °F RH 2, % CO2 2, ppm

Figure 6-30: Indoor Temperature, RH, and CO2 in Family Room for October 2-10, 2016

121

80 1400 75 1200 70 65 1000 60 55 800 50 600 45

40 CarbonDioxide (ppm) Temperature Temperature (F)/ RH (%) 400 35 30 200

Temp 3, °F RH 3, % CO2, ppm

Figure 6-31: Indoor Temperature, RH, and CO2 in the Bedroom for October 2-10, 2016

There were fluctuations of the parameters measured in the family room as observed in

Figure 6-30 which could be due to the high levels of activity and variable occupancy in that space.

There were more regular patterns in CO2 levels in the bedroom (Figure 6-31). A few responses were collected for the occupant feedback and they showed that the occupants were mostly satisfied with the indoor environmental conditions. The heat pumps are operated based on schedules, but other adjustments to the indoor environment such as opening or closing windows were monitored using sensors (state loggers).

6.10. Discussion of Results

Several observations have been made from the comparison of the U.S. and Qatar office building data. Occupants had different importance levels for their values such as differences in the importance of environmental protection and energy cost savings. Most of the Qatar office occupants felt that energy cost savings were neither important nor unimportant to them but in both buildings, they felt environmental protection was important. The difference in importance levels of

122

energy cost savings could be because the occupants are not directly responsible for paying utility bills in both office buildings also the cost of electricity is very low in Qatar. They also attached different levels of importance to their ability to control the indoor environmental conditions.

Although it was not directly proven, their importance levels of values collected from the VET might have influenced some of their behavior indoors.

For the highest responses on the PMA, there was some variability in the data and responses to changes in the indoor environmental conditions. From the responses on the PMA, occupants were mostly satisfied with their values indoors. They reported some behavioral modifications to improve their comfort indoors. There were mixed responses from both building occupants on the impact of the indoor environment on their perceived health and personal productivity. The Qatar occupants felt that the indoor environmental conditions decreased their personal productivity while the U.S. occupants felt it did not affect. The Qatar occupants were mostly dissatisfied with their thermal comfort for the period under investigation.

The IEQ parameters measured for the period presented was within the acceptable limits for

ASHRAE. Extremely hot temperatures in Qatar accounts for some of the observed differences in cooling power consumption for the period that was examined. The strong correlation between illuminance and lighting power could indicate the reliance on artificial lighting in the Qatar offices.

Also, there was a high use of plug loads observed in the U.S. office which can be partly due to the use of portable devices to compensate for indoor conditions as observed on-site visits during the experimentation period. The use of continuous cooling during unoccupied hours and the large cooling loads in the Qatar office can be further explored to identify opportunities to improve the energy performance of the building. Although the study involved only a portion of the building occupants and vital information might have been missed from occupants that are not providing feedback, the study provided some insight into the building energy consumption patterns and occupant-behavior and perceptions indoors. 123

6.11. Implications of Data Collected

There are several implications of the data obtained from the case studies conducted. People respond differently to changes in the indoor environmental conditions. These case studies have reinforced the notion that most occupants are active participants in buildings and they interact with building systems especially when they are uncomfortable. When people have no access to controls, they might use other means or change their location. It was observed that the actions that were taken by occupants sometimes impacted the energy consumption, i.e., when they used additional devices or opened the windows or doors to make the indoor environment more comfortable. At other times they changed the number of layers they were wearing which had no direct impact on energy consumption. The impact of the IEQ on perceived health and personal productivity was variable in both office buildings, but overall some of the occupants reported an impact on both values. Although it was observed that there were fewer complaints from the residential building occupants which could be due to the level of control they have over the indoor environmental conditions, there are still further insights that can be obtained about occupant behavior in residential buildings.

6.12. Summary

This chapter examined three case study buildings, one residential and one commercial building in Pennsylvania, USA and one commercial building in Doha, Qatar. An office space was monitored in both commercial buildings. Occupant values and other background information were collected using the VET. The detailed comparative analysis of the two office buildings revealed a difference in building operation modes in both locations. The Qatar office building consumed a lot more energy for space conditioning than the U.S., office building. Weekly energy use profiles also showed a steady pattern of cooling energy use in the Qatar office, even on weekends. They were always cooling the building even when it was unoccupied which might be as a result of the hot

124

outdoor temperatures. A variety of responses to changes indoors were observed over the course of the study some of which resulted in increased energy consumption. This study provided an opportunity to better understand occupant values, preferences, and behavior indoors and identify areas of intervention for improved integration of occupant values with building systems. In Chapter

7, an approach was explored working toward improved integration of end-user values with building systems.

125

Chapter 7

A VALUE-SENSITIVE AGENT-BASED MODELING APPROACH

7.1. Introduction

In the initial review of agent-based modeling and simulation in Chapter 3, the applications of agent-based approaches for modeling human behavior and preferences was presented. In this chapter, ABM was explored to simulate occupant values and behavior in buildings. Human perception of indoor environmental conditions is variable and people have different preferences for the indoor environmental quality (IEQ) parameters. Computational tools and approaches have been used to simulate human behavior in buildings. This chapter explores an approach that represents different building components as agents through agent-based modeling (ABM). The applications of ABM in different fields including building construction and operation are discussed. This approach has been beneficial for the simulation of human and building systems behavior in different scenarios with the introduction of intelligent agents that can handle more complex scenarios (Wooldridge, 2009). This chapter is concerned with simulating occupant behavior and interaction with building systems during the operations phase rather than details of the building envelope and building system components. Occupant values, such as thermal comfort and lighting/visual comfort are considered. A variety of scenarios were developed and tested using the

ABM architecture. The impact of interventions on occupants through training was also investigated in addition to revealing the potential of ABM in improving how occupant values can be accounted for and integrated with building systems.

126

7.2. Definition of Key Terms

Agents: An agent is an entity that can act autonomously. The term was introduced in the 1950s by

John McCarthy and Oliver G. Selfridge (Kay, 1984). Agents are autonomous and can act alone, interacting with each other and interacting with their environment (Bonabeau, 2002; Macal &

North, 2007). They can respond based on their behavior or based on their interaction with other agents.

Agent-based Modeling: It involves the virtual representation of agents/entities in their environment. The interactions of the agents with each other and with their environment are simulated as they work towards achieving the goals of the system (Schank, 2010).

Multi-agent Systems: They can act autonomously and achieve their individual goals (Wooldridge,

2009). Multi-agent systems (MAS) are not centrally controlled (Ren et al., 2005). MAS involve multiple agents interacting with each other and with the environment. The agents are assigned different characteristics/attributes and respond based on the rules of the system.

7.3. Applications of Agent-based Modeling in Different Industries

As mentioned earlier, ABM has been widely used to model and predict human behavior in different industries and to simulate systems and organizations. ABM applications are continuously evolving. The applications of ABM in buildings and other industries are presented in the following sections.

7.3.1. Safety and Egress Design Applications

Human presence, movement, and behavior can be modeled in a virtual environment using

ABM tools. Simulating occupant location and movement for fire safety and egress with visualization enables improved design decisions to be made. Applications that employ ABM include egress analysis for fire safety design in buildings (Sagun et al., 2013). Egress design models

127

can be behavioral, movement, or partial behavior models (Kuligowski et al., 2010). Behavioral models incorporate occupants’ actions; movement models move occupants within the building; and partial behavior models are used to evaluate occupant movement and behavior (Kuligowski et al.,

2010). In simulating crowd movement in emergency situations, multi-agent systems were employed using the building model geometry and a crowd simulation engine. Through this simulation, different human social behaviors in emergency situations were explored (Law et al.,

2005). Agents are also used in distributed sensing which includes monitoring for safety and security systems (Arciszewski et al., 2005; Leedy & Ormrod, 2005).

7.3.2. Construction Industry Applications

Sawhney et al., (2003) described two potential applications of ABM in construction. The first one is for site safety and creating a safe climate for construction workers. Here, the workers are represented as agents representing different aspects of safety climate at the workplace and their response to different safety strategies can be analyzed. Another example is in residential building construction where a lot of construction cycle time is wasted due to the outdated methods that are used to process information (Sawhney et al., 2003). MAS and ABM have been used in other aspects of the construction industry. Ren and Anumba (2005) developed a multi-agent system architecture for construction claims negotiation (MASCOT). MASCOT improved the efficiency of the claims and improved the rationality of the construction claims negotiation process. It has also been used for the simulation of construction waste generation and management (Ding & Wu, 2017).

7.3.3. Building Energy Analysis

Some of the applications of ABM for energy-related studies have been presented in Chapter

3. Other applications of ABM are for building occupants to improve the performance and usability of the buildings (Andrews et al., 2011) and to model heating demand in residential buildings

128

(Chingcuanco & Miller, 2012). They are also used to evaluate the impact of occupants and occupant behavior on energy consumption in commercial buildings (Azar & Menassa, 2011; Zhang et al.,

2011; Putra et al., 2017). There are used for offshore wind energy analysis (Mast et al., 2007), for testing the dissemination of new energy efficiency technologies (Bastani et al., 2016), and for modeling building occupancy (Liao et al., 2012). ABM and MAS have been used to model control system logic for building operations (Jazizadeh et al., 2014; Lee & Malkawi, 2014).

7.3.4. Other Applications of Agent-based Modeling

Apart from the benefits of ABM in the architecture, engineering, and construction (AEC) industry, it has also been used for supply chain management, logistics, stock market dynamics, diffusion of information, and new technology adoption. Some of these application areas have been summarized in the literature (Bonabeau, 2002; Macal & North, 2007).

ABM has wide applications and holds potential for advanced implementation in buildings for modeling building occupants and their interaction with building systems. It can be further explored for occupant-value integration with building systems in different scenarios. A selection of application scenarios can be tested within the agent-based model to determine the effects of a variety of decisions and changes on the phenomenon under investigation.

7.4. Buildings and Occupant-related Factors

The building stock comprises different types of facilities and diverse designs and configurations. In egress safety and emergency design, the models consider human physical characteristics, environmental characteristics and they began to address psychological and sociological characteristics (Pan, 2006). These buildings provide varying levels of control to the occupants which could cause them to take certain actions that impact energy use. For instance, in some buildings, occupants can adjust the thermostats to make the space warmer or cooler. In

129

situations where they have no control, they may use portable electrical devices such as heaters and fans to improve their comfort. In both instances, energy use is impacted. The building function can also influence occupant behavior. Several external factors influence building occupants such as the outdoor weather conditions, economic status, energy conservation habits, and culture.

Hong et al. (2016) classified energy-related occupant behavior into two categories namely adaptive and non-adaptive behaviors. In adaptive behavior, occupants could adapt the environment to meet their needs or adapt themselves while in non-adaptive behavior, people use additional equipment like portable devices to improve their comfort. The industry is working towards a more uniform approach to representing occupant behavior in buildings by developing a framework for modeling building occupant behavior (Hong et al., 2016). The most commonly observed occupant behaviors, from the case study buildings in Chapter 6, are listed in Table 7-1 and have been discussed elsewhere (Abraham et al., 2017b). This reiterates the fact that people actively interact with building systems.

Table 7-1: Occupants’ Response to Uncomfortable Indoor Environmental Conditions- Adapted from Hong et al. (2016) and Abraham et al. (2017b) Condition Possible Adaptive Actions Possible Non-adaptive Actions Too hot/cold Adjust temperature: Open doors/ Report to someone, i.e., facilities windows, adjust thermostats, turn manager, take no action heating or cooling devices on/off, use portable heating/cooling devices, change layers of clothing Too bright/ too dim Adjust lighting: Turn lights on/off, Report to someone, i.e., facilities use portable lighting devices, adjust, manager, take no action open, close shading device Poor air quality Adjust ventilation: Increase Report to someone, i.e., facilities ventilation by opening doors/ manager, take no action windows, use mechanical ventilation

Multi-agent systems can be used to simulate a variety of behaviors and scenarios by assigning different rules and defining the interactions of multiple agents in the system. Occupant values can be represented in an energy simulation model based on occupancy schedules or based on individual preferences. Schedules reflect the occupancy levels, but preferences that are extracted

130

from the occupants based on their profiles can provide a more accurate representation of individual occupants.

In Table 7-2, a theoretical representation of occupant behavior in an agent-based model using occupant preferences is presented. Occupant behavior data can be collected through surveys, observation or learning from the occupant’s behavior under different environmental conditions.

These can be represented in the model implicitly using rules, using artificial intelligence techniques, or based on probabilistic methods (Kuligowski et al., 2010).

Table 7-2: Possible Representation of Occupant Behavior in ABM- Adapted from Lee & Malkawi (2014) Behavior Considered Parameter ABM Representation Use of windows and Airflow/Temperature Percent open or closed doors Use of blinds Radiant air temperature/ Percent open or closed Illuminance Use of portable electrical Equipment power consumption Plugged/unplugged equipment Adjusting thermostats HVAC power Inputs of preferred temperature set points Use of lighting Lighting power Occupant’s preferred illuminance levels Occupancy levels Schedule/use of lights Occupant presence, based on schedules from occupancy patterns

A combination of different occupant behavior can be assigned to individual agents. The cost or utility function determines the relative value of different behavior assigned to agents and it determines the satisfaction of the occupant (Arciszewski et al. 2005). Agents can be examined based on their interaction with the environment, their interaction with one another and, where their behavior evolves, based on what they learn from another agent. Machine learning methods could also be combined with these to reflect the behavior of an agent over some time under different indoor environmental contexts. In other words, the reliability of the ABM can be improved by using

131

actual empirical data. The agent can continuously learn from its environment and adapt to changes in the environment.

The capabilities of ABM can be extended by coupling with an energy simulation tool which can be adapted to accommodate different occupant-energy-related behavior and validated through case studies using actual buildings. A few factors to take into consideration for agent-based model development are defining the objectives of the model, assigning agent characteristics, prescribing rules, interactions, and the outputs.

7.5. Overview of Occupant Behavior Modeling

When considering energy consumption, many factors can be attributed to occupants in buildings. They include their indoor environmental preferences, perceptions of IEQ parameters, and energy use behavior. As earlier discussed, human behavior is difficult to predict, so a few occupant behavior models have been developed in different fields as discussed by Andrews et al.

(2011). They include economic models, i.e., neoclassical economic model which uses a simple model of human agency. In psychology, several theories have been proposed to explain human behavior namely, a theories-norm activation theory, the theory of planned behavior (TPB) also value-belief norm (VBN). Computer science uses artificial intelligence and seeks to improve the human decision-making process. An example is the belief-desire-intention (BDI) model which has also been used for modeling human behavior in buildings (Andrews et al., 2013; Hong et al., 2016;

Putra et al., 2017). BDI is also used in ABM to represent human reasoning and behavior. Regarding occupant behavior in buildings, people adapt in different ways to improve their comfort. A variety of factors affect occupant values such as their background, culture, education and financial status.

Capturing the factors that affect the perceptions of thermal comfort, visual comfort, and indoor air quality can be difficult to quantify, but these modeling approaches attempt to model human reasoning and decision-making process.

132

Other approaches that are used in marketing to model how people influence each other to change or adopt new technologies or behavior using dynamic models include bounded confidence

(Hegselmann & Krause, 2002), relative agreement (Deffuant et al., 2002), and Bass diffusion

(Mahajan et al., 1990).

7.6. Rapid Prototyping of Agent-based Model

Building occupant values have been defined, and the need to improve how occupant values are integrated with building systems has been discussed. Occupant-related energy use behavior can impact energy consumption. An agent-based modeling and simulation approach was explored to address the integration of occupant values with building systems. For the development of the model for building occupants, first, the modeling objectives were defined which was to identify an approach to depict individual occupant values in buildings, assign occupant behavior in response to discomfort, define agent interactions with controls and with each other and observe the impact on energy consumption. Second, an appropriate modeling platform was selected based on a few criteria, namely, the functionality, the programming language, ease of use, user interface, modeling output, and visualization capabilities, etc. Third, the implementation of the model and the assignment of agent characteristics and interactions within the modeling platform were completed.

Figure 7-1 presents the methodology and steps taken in developing the value-sensitive ABM. At the knowledge acquisition phase, the requirements for the model development are identified. This phase includes defining the roles of the agents, the rules of the agent environment, the interactions as well as selecting the sources of input data. The analysis stage involves analyzing the agent function, defining the hierarchy of the agents and creating the interaction protocols. The last stage is for implementation and testing. The requirements are updated based on feedback obtained from this phase.

133

Requirements Capture (Knowledge Acquisition Phase) • Roles of the agent • Rules • Data sources/databases • Interaction of the agents

ABM Analysis (Agent Process Map Development/State Chart) • Analyze agent function Validate and • Define order/heirarchy update model • Design and create interaction protocols

ABM Implementation and Testing • Update and modify requirements based on feedback

Figure 7-1: ABM Methodology for Value-sensitive Analysis

Rapid prototyping is used in software development to mock up a software program in the early stages of development for testing and evaluation before full development of the system. From the comparison of ABM tools in Table 3-3, Anylogic was selected as a suitable tool for development based on its user-friendly platform, its visualization capabilities, and the ability to couple it with other programs (Anylogic, 2016). Anylogic has also been used by other researchers in this field to achieve different modeling objectives such as to model occupants’ energy use characteristics (Azar & Menassa, 2011), model office electricity consumption (Zhang et al., 2011), simulate diffusion/adoption of energy-saving measures (Bastani et al., 2016), model occupant- building-appliance interaction for the analysis of energy waste (Carmenate et al., 2016) and, simulate energy-related occupant behavior (Chen et al., 2017). These studies have investigated different dimensions of building occupants and energy consumption as summarized in Table 7-3.

They demonstrated the extent of ABM application in buildings using Anylogic. The incorporation

134

of multiple occupant values with building systems is an area that is still yet to be explored using

ABM.

Table 7-3: Selected ABM Studies of Occupant-related Building Energy Use with Anylogic

Author Model Occupant Building Location Findings and year Type Behavior Type Zhang et al., Used Automated vs. Office Nottingham, Tested different (2011) empirical end-user UK elements of energy data, no well- control of consumption in office established lighting. buildings including occupant Proportions of organizational energy models at that energy policies/regulations, time consumed by energy management lights and technologies, electrical computers appliances and equipment and human behavior using three experiments Azar and Developed User types Office Virtual Developed a model in Menassa, from HEC, MEC, model eQuest and used ABM (2012) simulation LEC and for occupant with eQuest dynamic interactions and change occupancy in occupant behavior and energy use Carmenate Used process Energy literate Single Hypothetical Estimated lighting, et al., (2016) modeling and illiterate floor scenario plugs, and HVAC loads categories in office and explored the different spaces interactions between with or without occupants and the natural buildings daylighting Bastani et Simulation Based on Office Houston, Simulated the diffusion al., (2016) model with feedback TX, USA of energy saving eQuest interaction- policies and the impact wasteful, on energy consumption standard and and emissions using austerity Bass diffusion theory Chen et al., Co- Used occupant Office Miami, FL, Used 4 different agents- (2017) simulation behavior USA occupants, lights, with functional HVAC, the window in EnergyPlus, mockup unit the office spaces. The used (ObFMU) model needs to be occupancy validated. May not be simulator for feasible for long-term occupant predictions based on types their application

135

7.6.1. ABM Development Objectives

From Chapters 4 to 6, the importance of enhancing occupant values was discussed and emphasized through the study of other industry sectors and the empirical energy studies. Some of the findings indicate the importance of end-user controls for indoor environments. The ABM approach seeks to incorporate the findings from these studies to improve end-user comfort in buildings. The main objectives of the approach are presented below:

 To explore occupant interaction with building systems based on their values and

preferences as they improve their comfort,

 To simulate occupant interactions and their impact on energy consumption.

Saving energy and providing comfort are sometimes perceived as conflicting objectives and may involve some trade-offs since improved satisfaction may result in increased energy consumption.

7.6.2. Agent-based Modeling Architecture for Building Operations

The model architecture for building operations modified from Chen et al. (2017) is presented in Figure 7-2. The visualization layer is the top level in the model architecture; it serves as the user interface. At the other end is the sensing layer, the building data can be incorporated into the system using sensor data and other data sources. The sensing layer considers occupant behavior reported through the surveys. Following the sensing layer is the agent layer which includes the characteristics of the agents. The main agent being considered is the occupant, other agents that can be considered are lights, cooling systems, and windows. The configuration layer includes the information about the occupant, the building systems, and the space layout.

136

Visualization •User Interface Layer •2D/3D Visualization

•Occupant Information Configuration •Appliance Information Layer •Spatial Configuration

•Occupants •Lights Agent Layer •HVAC •Windows

•Energy Use Data Sensing Layer •Indoor Environmental Data •Occupant Data

Figure 7-2: Agent-based Model Architecture (Adapted from Chen et al. (2017))

7.6.3. System Design Requirements

The design requirements for the ABM approach comprise the software, the programming languages, the occupant and systems information, and the sensor information. Various development environments such as Repast, NetLogo, and Simulink are widely used, but Anylogic was selected since it provides good visualization and allows for rapid prototyping and development of a variety of scenarios. It can also model a phenomenon to different abstraction levels based on user preferences. Java is a high-level, object-oriented programming language used in Anylogic. One of its advantages is its cross-functionality and ability to perform well on different platforms (IBM,

2017). The building geometry was obtained from an Autodesk Revit model and was imported as an image into the ABM platform. The human agents are represented within the model as avatars in

3D form, but visualization of the agent state is better portrayed using the 2D agent representation

137

icons. The agent properties for the occupants were also assigned within the program. Screenshots of the model interface are presented in Figure 7-3 and Figure 7-4.

Figure 7-3: Interface of the Prototype System

Figure 7-4: View of Building Floor Layout and Plots showing Occupants Interaction in One Day

138

The model state charts for the three scenarios are presented below (Figure 7-7). For scenarios 1 and 2, the occupant arrives, and their office is uncomfortable. The occupant responds differently depending on if they are active or passive users. Scenario 1 considers occupants that have no control while scenario 2 considers occupants with control. The same state chart is used, but the control conditions are adjusted for each scenario. In scenario 3, the behavioral influence is observed as active users become passive and are more energy and cost conscious. Exposure involves training and educational programs aimed at helping them improve their energy use habits.

When the rate of exposure decreases, the passive user may begin to change their behavior over time and become more active.

7.6.4. Model Assumptions

The model was developed for a specific building geometry, and the building material was assumed to remain unchanged in the model. The energy use intensity was determined from literature and the case studies presented in Chapter 6. The variability of the occupants was adjusted to reflect and represent different extremes of energy consumption and is represented as the occupant types/profiles. The space configurations were designed for one floor in an office building with private offices occupied by one person. For the occupant behavior categories, the initial assumptions include the fact that a conservative occupant/passive user is energy conscious, the medium energy user is an average user, and the high energy consumer/active user does not pay attention to their energy use with regards to cost and environmental protection.

7.6.5. Operation and Testing

The model was developed on Anylogic and allows for different parameters to be entered and simulated to determine the impact on comfort and energy consumption. It was tested with a selection of behaviors and using different what-if scenarios to determine how the model processes

139

a variety of inputs under different conditions. The steps involved in the operation of the model and changing occupant properties are further described in the following sections.

7.7. Agent-based Modeling Application Scenarios in a Building

A variety of scenarios were explored to demonstrate the versatility of the model. They involve a single interaction with building systems and a collection of occupants in private office spaces looking at the influence on energy use behavior. The building systems that directly affect the indoor environmental conditions include the HVAC systems and lighting. As observed in

Chapter 6, residential and office buildings are operated differently. Residential buildings especially single-family homes sometimes have decentralized systems while office buildings tend to have centralized systems. The scenarios focus on an office building. For each scenario, the building geometry is maintained, but occupant characteristics change. The occupant characteristics are assigned from a database that contains different features. Each person is assumed to occupy a private office. There are 16 occupants altogether in the private offices. Two scenarios of occupant behavior are observed: for those that have control over the indoor conditions such as the ability to adjust thermostats and light levels and for those that do not have control (Figure 7-5).

Do nothing

Occupants have no control Minor adjustment

Major adjustment Scenarios- multiple occupants in private offices Do nothing

Occupants have control Minor adjustment

Major adjustment

Figure 7-5: Scenarios of Occupants Interaction with Building Systems

140

Subcategories of occupants include those that are passive and may do nothing to adjust their comfort, or their actions to improve their comfort does not impact energy consumption (i.e., wearing additional layers of clothing. This could be because they are more energy conscious, the medium users that may make minor adjustments, and the active users that go to any length to make themselves comfortable, they make major adjustments with the building systems and are less energy conscious. To further clarify the differences in behavior, the occupants were subdivided into two types- passive and active users eliminating the medium user. The active users take actions that have the minimum impact on energy consumption while the active users pursue their comfort at any cost (Table 7-4). For thermostat controls, minor adjustment assumes a slight change (<±1°C) in the set point temperature while a major adjustment will be outside that range.

Table 7-4: Occupant Types and Possible Actions Based on Level of Control

Values Passive Users Active Users

Occupants without Thermal Comfort Adjust layers of Use portable device- control over clothing/ Do nothing fan/heater microclimatic Lighting Comfort Do nothing/change Use portable conditions location/ Adjust lighting/additional task shading device lighting Occupants with Thermal Comfort Do nothing/minor Extreme adjustment and control over adjustment to use of portable microclimatic temperature, turn off fan/heater, use natural conditions HVAC and use natural ventilation in addition to ventilation devices indoors Lighting Comfort Do nothing, minor Extreme adjustment or adjustment to lights and use portable lighting/ shading device, or additional task lighting change location

7.7.1. Occupant in a Private Office without Control over Building Systems

In this simple scenario, each occupant was modeled in a private office. The occupant’s interaction with the lighting system, HVAC system, and plug load use was explored. This scenario considered the different categories of occupants simultaneously in private offices. 16 occupants were considered, and the occupants were assumed to be evenly distributed between active and 141

passive users. The building is being simulated for the summer and the occupants have automated control for the air-conditioning, light switches and windows. This scenario considers occupant behavior when they arrive at the room and make some changes to improve their comfort or take time to adjust to the indoor conditions. The steps include the following, the occupant enters the room, the lights go on automatically and cooling is already being supplied based on the building operation schedule. Depending on their preferences and other thermal comfort conditions, they may want to adjust the conditions, but they have no control. The occupant, based on if he/she is an active or passive user, may respond in the different ways. A passive user may do nothing while an active user may use additional portable devices such as a portable fan, desk/standing lamp, and a portable heater.

The initially assumed weighting factors are presented in Table 7-5. The weighting distribution for the two occupant types is based on the observed efforts from the case studies and assumptions about the impact of their behavior on their discomfort, cost, and the environment.

Occupant distribution can be varied between the two types. The weighting factors are varied based on whether the occupant has control over the microclimatic conditions.

Table 7-5: Occupant Distributions and Initial Weighting Factors based on Occupant Utility

Occupant Number of Environmental Effort Cost Discomfort Type Occupants Impact Passive 8 0.23 0.17 0.2 0.4 User Active 8 0.15 0.30 0.40 0.15 User

7.7.2. Occupant in a Private Office with Control over Building Systems

The effect of providing occupants with control over building systems was explored in this scenario. The occupant could open the windows and adjust the thermostats as they require. The passive user may keep the IEQ parameter levels to the minimum to conserve energy. They are also more likely also to turn off devices that consume energy when the office is unoccupied. The active 142

user may misuse the controls and waste energy through their activities; they may also leave devices on when they are not in the office thus leading to energy waste.

7.7.3. Modeling Effect of Change of Behavior on Different Occupants

The effect of change of behavior is also analyzed, assuming all the occupants become more energy conservative. Considering how they are likely to respond to changes in the indoor environmental conditions when they have control and when they do not have control. The difference between these two scenarios is compared to the multi-occupant spaces. Although in the event of an intervention, not 100% of the occupants will change/adjust their behavior, it is assumed that the occupants are highly incentivized to make a change, and they all respond to the incentives provided. The three model scenarios that were proposed are presented here (Table 7-6). Initial occupant profiles were developed from a variety of sources including the experimental data from our case studies and the CBECS database. The initial profiles are included in the appendices. The proposed simulation framework is presented in Figure 7-6. The framework was considered for scenarios 1 and 2 for the exploration of the availability of local controls to occupants to improve the indoor environmental conditions. The database includes occupant information and parameters that can be assigned to the occupants. Energy consumption rates are estimated from historical data available in sources such as CBECS and the building performance database (BPD).

Table 7-6: Model Scenarios

Scenario Duration Simulation No of Occupants Definition Interval Scenario 1 No local 1 working day 1 minute 16 Control Scenario 2 Local control 1 working day 1 minute 16

Scenario 3 Active-Passive 120 days 1 day 16

143

Start

Time step t+1

Database of Occupant occupant arrives profiles

Yes Uncomfortable? Adjust settings Take action Yes

Update Occupant Information

Database of Calculate and plot energy impact on energy consumption consumption rates

Repeat simulation?

End

Figure 7-6: Proposed Simulation Framework

7.8. Preliminary Simulation Results

144

have no control while scenario 2 considers occupants with control. The same state chart is used, but the control conditions are adjusted for each scenario. In scenario 3, the behavioral influence is observed as active users become passive and are more energy and cost conscious. Exposure involves training and educational programs aimed at helping them improve their energy use habits.

When the rate of exposure decreases, the passive user may begin to change their behavior over time and become more active.

Scenarios 1 and 2 Scenario 3 Figure 7-7: State Charts for Model Scenarios

Occupant characteristics from the developed profile are imported into the model database in Anylogic. Figure 7-8 shows the characteristics of one occupant; assigned characteristics are represented as parameters within the model. The statechart also highlights changes in the model.

The simulation is an exploratory study which will need to be further developed incorporating a variety of testing scenarios and multiple simulation runs.

145

Figure 7-8: Showing Parameters Assigned to an Occupant within the Model

In scenario 3, the effectiveness of interventions to improve energy use behavior are also explored. Interventions can be in the form of education, training or events planned to increase awareness and induce a change in the occupants. From Figure 7-9, the interactions between the two user types are modeled. Assuming all occupants are initially active users and they begin to change into passive users. The y-axis shows the number of occupants (0-16) while the x-axis shows the number of days (0-120). With extensive education and training, about 15 of the occupants become passive users after 70 days and the energy consumption reduces because of the change in behavior.

Figure 7-9: Demonstrating Effect of Intervention- High Effectiveness 146

With reduced effectiveness in the interventions, fewer people change their behavior. It takes longer for occupants to become passive users (20 days). After 85 days, ten occupants change from being active users to passive users (Figure 7-10), and they remain consistent with continued exposure to education and training but have reduced interaction with other occupants.

Figure 7-10: Demonstrating the Effect of Intervention- Moderate Effectiveness

The effect of having low occupant education and training is presented in Figure 7-11. There is a low rate of change from the beginning; occupants start changing to passive users after 20 days.

With some effort on the intervention and the training, the maximum number of occupants that respond do so after 50 days. Then a few people start dropping out until the numbers of passive users decrease to 1 person. There is a need for continuous engagement and involvement of occupants as energy efficiency measures are being implemented. In buildings where occupants have control over the systems, it is important to ensure they understand how to use the system and save energy and costs. Although individual beliefs about energy consumption can be deeply entrenched and may not be easily changed, people may be more receptive to persistent interventions through appropriate education and communication channels.

147

Figure 7-11: Demonstrating the Effect of Intervention- Low Effectiveness

7.9. Evaluation of the Occupant-value-sensitive ABM Approach

The preliminary ABM approach was evaluated through one-on-one meetings. Seven experts participated in the evaluation. The approach was based on some assumptions and rather than accounting for occupants as groups (occupancy), individual occupants were considered. The scope of the approach is based on office buildings focusing on thermal and visual comfort. It was important to include an occupant feedback layer where occupants provide regular feedback to facilities managers on the indoor conditions. The potential benefits of this approach include:

 Improved integration of occupant values with building systems by accounting for

individual preferences

 Enhanced understanding of the impact of providing control for individual occupants

 Exploration of the frequency of interventions (programs geared toward improving energy

use behavior)

148

The scenarios for testing were presented to the participants to solicit their feedback.

Recommendations for improved occupant value-sensitive operation were provided as a part of the study. The evaluation objectives, the process, and the results are presented below.

7.9.1. Evaluation Objectives

An approach to integrating occupant values with building systems to improve occupant comfort and improve building performance was proposed in this study. The following are the objectives of the evaluation:

 To demonstrate the operation of the occupant-value sensitive system and occupant

feedback dashboard

 To evaluate the appropriateness of the approach for occupant preference and behavior

modeling

 To obtain feedback for refinement of the approach and how improved integration can be

achieved

It was important to determine the applicability of the approach for occupant behavior modeling including determining if the testing scenarios are appropriate and soliciting additional suggestions on how the system can be improved including recommendations on how the approach might be applied in a real office building scenario. The considerations for the approach to be used during building operation and suggestions for the design phase were also identified. Seven professionals provided feedback on the proposed approach. The evaluation process is described below.

7.9.2. Evaluation Process

Experts in building operations and management provided feedback on the prototype approach. The seven evaluators comprised three facilities engineers, two commissioning engineers,

149

one building automation systems analyst, and one building operations engineer. The evaluators were selected based on their experience with operating buildings and their use of building operation and control systems. The average years of experience were 23.4 years. One-on-one meetings allowed for the clarification of questions and provided an avenue to collect adequate information from the respondents.

A questionnaire was developed to collect information in an organized way from the evaluators. The questionnaire is presented in Appendix G. The questionnaire included closed-ended questions, open-ended questions, and opportunities to provide suggestions. Questions on the usefulness of the approach, the testing scenarios, and the potential for improved integration were asked followed by questions on the features of the ABM approach, the importance of providing notification to occupants, and the inclusion of an occupant feedback dashboard. Then questions about the potential benefits suggested improvements, and the practical considerations for the approach were asked. The respondents also had the opportunity to provide additional comments and recommendations.

The steps taken for the evaluation were to first introduce the topic, the background work including the empirical studies and the surveys, and a summary of its relevance. It was also important to explain agent-based modeling and the potential for improved integration using this approach. With most of the evaluators, a preliminary discussion followed where they shared their experiences with occupant comfort, occupant behavior, and building energy consumption. The approach to integration was presented including the application scenarios. The questionnaire was handed out for feedback followed by a closing discussion. The steps taken in the evaluation are presented in Figure 7-12.

150

1. Introduction of 2. Presentation on the 3. Potential application research topic and approach to integration scenarios background

4. Hand out 5. Discussion and questionnaire for additional feedback feedback

Figure 7-12: Evaluation Steps

7.9.3. Evaluation Results and Discussion

The meetings with evaluators yielded the following results. On the question about the usefulness of the approach to modeling occupants’ thermal and visual comfort, six of the evaluators felt it was very useful or extremely useful in modeling thermal and visual comfort (Figure 7-13).

One evaluator felt it is extremely useful in building design but moderately useful during building operation.

How useful is the approach in modeling occupant values (thermal and visual comfort) in buildings? 5 4 3 2 1

No Responses of 0 Extremely useful Very useful Moderately useful Slightly useful Not useful at all

Figure 7-13: Usefulness of the ABM Approach

Other contributions were that using this approach; there will be many opportunities to lower or raise temperatures and find faulty sensors since occupant feedback is included as part of the system. They felt that occupant feedback can serve as a way to determine if comfort is achieved.

They mentioned that people sometimes have a poor perception of their comfort. One person felt 151

the occupants will be able to provide feedback to the facilities operator ‘anonymously.’ The occupant side was recognized as an essential aspect of building operations that are needed in existing systems.

Three scenarios previously described were presented to the respondents. The scenarios were based on the level of control available to the occupants and the impact of an educational/training intervention on the occupants and the frequency of such interventions. Six of the respondents felt that the testing scenarios are very realistic or extremely realistic (Figure 7-14).

It was suggested that the occupants gain a decent understanding of how the system works.

How realistic are the testing scenarios? 6 5 4 3 2 1

No Responses of 0 Extremely realistic Very realistic Moderately realisticSlightly realistic Not realistic at all

Figure 7-14: Proposed Scenarios for Testing the Approach

The potential of the system to integrate occupant values with building systems was discussed. Six responses were collected for this question. The majority felt that the approach can integrate occupant values with building systems very well (Figure 7-15) but most of them mentioned HVAC system limitations and limitations based on the configuration of the system.

How well do you feel this approach can integrate individual occupant values with building systems operation? 5 4 3 2 1

No Responses of 0 Extremely well Very well Moderately well Slightly well Not well at all

Figure 7-15: Potential for Improved Integration of Occupant Values and Building Systems

152

The need for motivated facilities managers to use such systems was mentioned. Another important factor that was discussed is the cost to implement the system which might be high due to the possible need for a system that can adequately separate individual offices and provide different comfort needs.

Their rating of the performance of the ABM approach in relation to different features is presented in Figure 7-16. This question was also posed as the features they feel is important in a fully developed system. All the respondents think that the approach can be very effective or extremely effective regarding its ease of use, visualization capabilities, the usefulness of feedback, and meaningfulness of results. One respondent rated it as moderately effective regarding the usefulness of feedback. Another respondent mentioned that 2D visualization is sufficient and color coding to alert the facilities manager or operator showing the indoor conditions and the occupant perception can allow them better address and manage the indoor conditions.

How do you rate the performance of the agent-based modeling approach in relation to the following features? 5 4 3 2 1

No Responses of 0 Extremely Very effective Moderately Slightly effective Not effective at all effective effective Ease of use Visualization capabilities Usefulness of feedback Meaningfulness of results

Figure 7-16: Rating of Performance of the ABM Approach

A question on the importance of occupant feedback and providing notifications was asked.

They all felt it is extremely important or very important to notify the facilities manager of changes to the indoor conditions (Figure 7-17). Five of the seven felt it is very important or extremely important to notify the building owner and the building occupant since the facilities manager will be able to intervene in the system when there is a problem. The building owner may also need to

153

be involved in the cost aspects and ensure occupants are not complaining since they are providing a service and want to provide a conducive working environment for the occupants.

How do you rate the importance of notifying the following people of changes to the indoor environmental conditions? 5 4 3 2 1

No Responses of 0 Extremely Very important Moderately Slightly important Not important at all important important Building owner Facilities manager Building occupant Other occupants

Figure 7-17: Importance of Providing Notification to Different Parties

An occupant feedback dashboard was presented to the evaluators to provide an avenue for occupant interaction with the building system. Some of the suggested features of the dashboard were discussed with them, they all felt it was extremely important for the dashboard to be easy to use, while most of them felt it is extremely important or very important provide good visualization for the occupant and facilities manager (Figure 7-18).

How important is it for the occupant feedback dashboard to have the following features? 8 7 6 5 4 3 2

No Responses of 1 0 Extremely Very important Moderately Slightly important Not important at all important important Ease of use Visualization capabilities for occupant Visualization capabilities for facilities manager Notification of facilities manager or building operator Availability of occupant feedback to facilities manager

Figure 7-18: Importance of the Features of an Occupant Feedback Dashboard 154

Six of the seven respondents felt it was extremely important or very important for the facilities manager to be notified of changes through the feedback dashboard. Five of them felt it was extremely important or very important for the facilities manager to be sent the feedback.

Looking into the features that are important for an occupant dashboard, the respondents had mixed opinions about providing information to occupants. They felt that occupants sometimes do not fully understand the ‘numbers’ and may misinterpret them thus triggering more complaints. Others felt it was important to allow the occupants know what is going on in the space.

Three open-ended questions were asked to allow for more open feedback. The first one was about the potential benefits of the approach to improving comfort and reducing energy consumption. The following responses were collected on that question. They felt that getting additional feedback from occupants can be helpful, but one of the respondents felt that occupant feedback might not always be realistic. The benefits of educating occupants were also discussed.

One respondent put it this way-

‘The more educated they are about the systems, the better.’

Another respondent said,

‘Energy benefits could be huge. Real-time occupant feedback could make happier customers.’

They recognized the potential energy savings that can be achieved through improved communication between the end user and the building operator which can also reduce service calls.

They also mentioned the productivity benefits and improved effectiveness of workers. The potential of reducing auxiliary heating/cooling that is less efficient was also one of the comments from an evaluator. It was suggested that occupants should not be provided with too much information. One respondent felt that it may be difficult to achieve both energy savings and occupant comfort.

On the question about the recommended improvements for the approach, one respondent felt that the results can be presented in more meaningful terms such as the quantity of emissions, 155

the number of trees cut down, something the occupant may be able to relate to. It was suggested that occupants could schedule their occupied/unoccupied times through the feedback application.

The use of a mobile phone app to achieve this integration was discussed. The inclusion of other parameters for the ABM framework such as window shades, lighting level, air velocity sensing, and ambient noise was suggested.

On the question about the practical considerations for the approach to be used in an actual office building, the difficulty of measuring one person’s impact through submetering was mentioned. It was suggested that we start with the zoning of the system since some of these systems have limitations on what can be adjusted during operation. The majority of respondents discussed the need for occupant education. Other comments were as follows:

‘An individual may not have a lot of impact, but the education can impact the system on another level.’

‘Providing information is expensive and can cause issues. Carefully consider what information is provided.’

It was mentioned that the system should be easy to use for the occupant and the facilities manager and inexpensive to install. The limitations of temperature and humidity adjustments were highlighted. The need for testing in an actual building, allowing a limited range of control for occupants, and the balance of cost versus the benefit was mentioned. The benefits of IEQ monitoring and using the data collected from different system types to identify patterns and inform the operation of the system were discussed.

7.9.4. Recommendations from Evaluation

Overall, the evaluators acknowledged the impacts of occupant behavior on energy consumption and expressed the need for this area to be addressed. The following key points were recurring themes during the evaluation discussions. They revolved around, first, the need for

156

occupant education on how the systems work and the impacts of their behavior. Second, the need to reassess controls in buildings to consider the level of control that might be appropriate for occupants, and third the need for improved communication between occupants, and the building operators so that the right interventions can be made to improve indoor conditions. One evaluator suggested that occupants are taught how to use buildings. Improved knowledge of the system and allowing more end-user control can provide energy savings since they are more aware of the impact of their actions on the system and other occupants. One suggestion about the occupant dashboard is to allow for occupant notification when they can take specific actions to improve their comfort, i.e. when they can open windows or make adjustments. The dashboard can be provided as a long- term operational effort to ensure that continuous communication between the occupant and the building operator is available.

Providing one set point temperature might not be the best, they acknowledged the need to vary the indoor conditions for some occupants. The use of portable devices may cost more money because of the imbalance to indoor thermal conditions. The sensors read the wrong temperature and send a signal to the controls to provide more heating or cooling thus counteracting their use of the device in some ways. They also mentioned problems that are not quickly recognized during design. For instance, in large glass windows and the radiant effects from the façade. For occupant- side control, they mentioned the use of faceless thermostats that adjust the temperature relative to its current position rather than showing actual numbers. This goes back to ensuring occupants have an understanding of how the system operates and what the numbers mean. The importance of monitoring relative humidity was also discussed. They mentioned the need for flexibility in design to allow for future changes in configuration. Most of the discussion centered around thermal comfort since this was the cause of significant energy consumption and has a significant effect on the occupants. A few suggestions were made for lighting such as allowing for dimming to adjust light intensity and daylight harvesting in more office spaces. One respondent suggested scheduling 157

occupant calendars with the feedback system so that unoccupied hours are recorded and accounted for by the system.

7.10. Potential Benefits for Value-sensitive Occupant Behavior Modeling

The proposed agent-based modeling approach has the potential to improve the understanding of occupant interaction with building systems and its impact on comfort and energy consumption. Focusing on an operational office building context, the interaction of an agent with other agents in the environment regarding their energy consumption patterns could be potentially beneficial to facilities managers and building owners to help them understand how a building operates and identify the operation mode that provides the optimum comfort and energy savings.

The approach can be extended in the future to include additional scenarios and allow for different building configurations and agent types to be modeled.

For a building owner, it can be helpful for assessing the impact of automating some of the building systems and determine how this affects the utility bills. They may also want to experiment with the occupant types. Although this approach is more suited to the operations phase of a building, it might also be beneficial to a building designer to assess the effects of having different building configurations to accommodate a variety of occupant types.

7.11. Summary

To summarize, this chapter included an exploratory study of ABM for improved occupant value integration in buildings. The steps involved in the creation of this model include a review of existing studies that have used agent-based modeling for energy conservation or management in buildings. A selection of the appropriate tools was discussed, followed by the identification of studies that used the selected tool. Agent attributes were defined and assigned, the interactions between agents were also defined, including how they can inform the design of buildings for

158

improved occupant comfort and reduced energy consumption. Preliminary work considering occupants and the level of controls available to them indoors also considering the effect of educational programs to increase awareness of energy use habits was explored through possible scenarios. The approach can be further developed into a fully functional tool that can be used by building owners, facilities managers and designers to determine how energy consumption can be decreased while improving occupant satisfaction in buildings. It can be of benefit to facilities managers for space management and to determine how occupants can be oriented in a building.

The evaluation with building operators and facilities engineers demonstrated that the approach is useful and has the potential to improve occupant comfort and minimize energy waste in office spaces. Four central themes were emphasized, improved communication, occupant education, enhanced controls, and affordable cost to implement.

The main limitations of this approach are the simplification of these behavioral patterns to enable the incorporation of a variety of occupant-related factors (values, preferences, and behavior).

This model is a high-level approach to estimate impacts of occupants on energy consumption and how their preferences and needs can be better accounted for in buildings without solely relying on traditional simulation models. A detailed development of the model can be beneficial for improved occupant value integration with building systems. The research findings and contributions are summarized in Chapter 8.

159

Chapter 8

CONCLUSIONS AND RECOMMENDATIONS

This chapter reiterates the scope of this research and summarizes the contributions including the significance of the study, the limitations, and recommendations for future work. The main findings from the study explored occupant values in buildings and illustrated the need to integrate occupant values and preferences with building systems (HVAC and lighting) to improve the comfort of occupants while minimizing energy consumption.

8.1. Overview of Research

The indoor environmental conditions can impact occupant values and preferences. Comfort is subjective, and people are unique. Several factors should be taken into consideration to achieve integration of occupant values with building systems. The research objectives presented in Chapter

1 have been addressed through the research tasks and are presented in the following sections. This study considered how building systems could better incorporate occupant values and preferences in conditioned environments through the controls. Occupant values were first identified followed by a literature review of other related themes such as occupant behavior, building control systems, energy simulation, a review of end-user values in automobile, aerospace and shipbuilding industries, and an exploratory study using an agent-based model. A summary of how the research objectives were achieved is presented followed by the contribution to knowledge, the research limitations, recommendations for future work, and the concluding remarks. The recommendations were developed as part of this study and are not intended to be best practice but serve as suggestions that need to be tested in each building context and modified based on the specific local needs

(7Group & Reed, 2009; Mang et al., 2016).

160

8.2. Summary of How Objectives were Achieved

The overall aim of this study was to explore the potential for the improved integration of occupant values and preferences with building systems while seeking to improve occupant comfort and reduce energy consumption. In the following paragraphs, the research objectives are presented including a description of how the research objectives were achieved.

Objective 1: Establish key occupant values and preferences and explore the relationship between these values and energy consumption in buildings.

The first objective included a literature review of the factors people consider to be important to them in buildings. Occupant values were defined and elicited from the occupants in the case study buildings. This objective was covered as part of Chapter 3 and 6. The relationship between these values, indoor environmental conditions, and energy consumption was explored. The responses of occupants in the U.S. and Qatar were compared.

Objective 2: Investigate approaches to integrating end-user values and preferences in conditioned environments in other industries (such as automotive, aircraft, cruise ships, etc.) and develop recommendations for buildings.

In this objective, a review of the different industries was completed to understand their approach to end-user satisfaction. Six case studies were conducted using a combination of interviews and questionnaires and discovering the enablers and barriers to integration in buildings. The automobile industry seemed to have more end-user focused approaches. They provide information to end users and use the data collected to improve performance continuously. Some of the findings from the case studies can be beneficial to buildings. This study also further revealed the need for improved value sensitive approaches for building operations considering the occupants. This objective was addressed in Chapter 4.

Objective 3: Undertake comparative analyses of occupant values and preferences in different indoor environmental conditions based on empirical studies in commercial and residential 161

buildings in Qatar (a hot desert climate) and Pennsylvania, northeastern USA (a humid continental climate with four distinct seasons).

As earlier stated, indoor environmental conditions should be improved for building occupants’ comfort and well-being. Three case study buildings were selected for this task and explored in detail. An interactive sensing system was developed and tailored to each building since some of these buildings already had part of the instrumentation and only required additional sensors.

Comparative analysis of energy use and IEQ of the two office buildings in both locations were completed for the cooling season. The empirical energy studies revealed that the office space in

Qatar consumed significantly more energy and had a steady operation mode throughout the week while the U.S. office case study minimized energy consumption, especially during weekends. The instrumentation was presented in Chapters 5 while the case studies were presented in Chapter 6.

Objective 4: Create end-user profiles/typologies for occupants based on their values and preferences which can be used for the simulation of what-if scenarios using agent-based modeling

(ABM) related to the identified typologies.

For this objective, end-user profiles were created from the data collected in the case studies and augmented with data from the literature. An exploratory study of the simulation approach was completed using agent-based modeling of different scenarios for occupants. The potential for integrating end-user values with building systems was assessed considering the impact on energy consumption. This objective was addressed as part of Chapter 6 and fully addressed in Chapter 7.

An evaluation of the preliminary ABM approach was completed as part of this objective. Seven professionals in building operations and controls served as evaluators and provided initial feedback and suggestions on other considerations and on how the approach can be enhanced.

162

8.3. Contributions to Knowledge

This research contributes to research and industry by proposing a value-sensitive approach and recommendations for improved occupant comfort. The contributions of the study are presented below.

An exploratory study of the transportation sector: This study was conducted to reveal and identify lessons that can be learned from automobile, cruise ship and aircraft cabins in relation to end-user comfort and energy efficiency. The transportation sector has seen a higher improvement in energy efficiency than the building sector. In Chapter 4, this study extracted lessons learned from automobile, aerospace, and shipbuilding industries to improve the way end-user values are accounted for in buildings. Some of the recommendations are the use of data to drive operation while including the end-user perspective and the need for individualized profiles.

In-depth analysis of occupant-related energy use and IEQ in buildings: Occupant preference and behavior were monitored in buildings in the USA and Qatar (from field studies). The need for the study has been highlighted following a description of the effect of energy consumption by the building sector in the USA and Qatar. Other researchers have also confirmed the impact of end- user behavior on energy consumption, and some of this behavior is to improve comfort. Although there are many studies into occupant comfort, occupant behavior, and energy use, most studies look at fragments of this not using a holistic approach. This study aims to provide insight into research on occupant values and preferences in residential and office buildings to identify occupant values and behavior in different scenarios. One of the main contributions of this task is occupant-related data on continuous monitoring of indoor environmental conditions and energy consumption. There are very few studies on residential and office building energy monitoring and occupant comfort in

Qatar. Data on interactively measuring building energy consumption in a hot desert climate can help inform how buildings are designed and constructed in Qatar thereby reducing energy demand in buildings. 163

Approach to explore value-sensitive building operation: An exploration of a value-sensitive approach for building occupants based on occupant profiles rather than only relying on occupancy using an agent-based simulation model was conducted. ABM had been used in different fields to model human behavior and had also been used to model occupant behavior in buildings. This study proposes an approach where individual occupant profiles are used to simulate the impact on energy consumption. This approach, when fully developed, can be beneficial for integrating occupant preferences indoors by collocating occupants based on their preferences. The fully developed approach may help in reducing energy waste and can be useful during retrofits in existing buildings to ensure the systems are flexible enough to provide occupant preferred settings indoors, for tuning and possibly for the design of new office spaces.

Recommendations for improved building end-user control: Furthermore, recommendations for more end-user friendly intelligent building controls can be developed. It could result in an integrative approach to building operations where the occupant values are better addressed by building systems, and continuous improvement is ensured as the building interacts and exchanges information with the occupant.

8.4. Implications for the Industry

The implications of this study to industry and future applications are presented below.

Improved representation of occupant and building-related factors: One of the main considerations is the need for continuous monitoring of the building indoor environmental conditions, energy consumption and occupant-related factors and behavior and using ontologies to better represent building and occupant-related data (Mahdavi et al., 2018). This can aid improved integration of occupant values with building control systems and the development of a uniform protocol that can be used in the industry.

164

Ensure usability of installed building systems: This includes ensuring building controls are usable by the operators through regular training, testing, and maintenance. Educating occupants on the use of these systems might also be beneficial to curbing energy wasteful behaviors. Also, continuous commissioning to verify that the systems perform as intended.

Create and continuously update a database for the building: The information from the database can be used for benchmarking other buildings and serve as a repository for historical data about the building performance and operation. The data should also be used to tune and for fault detection and diagnosis in the system. Sub-metered data allows you track different systems and better identify the areas of improvement.

Install efficient building systems: The energy performance of buildings can be improved while enhancing occupant comfort and building performance. The building systems should be efficient to minimize losses associated with low-performing systems. Continuous commissioning should be conducted to improve the reliability and resilience of building systems.

Understand and account for the context of the building and the occupants: Context-aware designs should be adopted considering the uniqueness of the end users, the location, and the building type. It is also important to consider the flexibility that might be required for the future operation of the building to include changes in occupancy type and building operation mode.

8.5. Research Limitations

In addition to the main contributions of this study, a few limitations have been recognized and are presented below. The study of human behavior can be quite challenging due to the complex and uncertain nature of people. The study tracked human behavior using objective (i.e., sensors) and subjective (i.e., surveys) measures.

Limited occupant participation and feedback: The surveys rely on occupant participation which was not always available so the amount of data collected was limited. This limitation was addressed

165

through observations of other behavior, maintaining regular contact to encourage participation, and providing incentives. The responses were cross-validated through observation and by verbally confirming the occupants’ perceptions and satisfaction.

Few case study buildings: Selections for the case studies in Chapter 6 and the occupants involved convenience samples. Also, practical deployment of sensors and instruments for this study was quite involved and required a lot of time and effort. The number of buildings used for the case studies is also limited so the results are not generalizable to the entire office or residential building population but can provide some insight into the patterns that might emerge from observations of occupant values, preferences, and behavior.

Further study of other industry sectors: The key considerations for the determination of end- user values in aircraft, cruise ship, and automobiles were obtained from the practitioners. The industry practitioners that were knowledgeable in environmental control systems were contacted to participate. The number of respondents for this study were quite small, but they gave some insight into the study and the lessons that can be drawn for buildings. Another constraint is that most of these industries are do not share information freely due to propriety concerns and the competitive nature of the industry.

Few scenarios and testing for the ABM approach: The ABM approach can be enhanced to serve as a more robust platform for simulating end-user values in buildings. Anylogic aids visualization and the simulation of different occupant scenarios. The approach currently explores a limited number of scenarios. It also only focuses on a hypothetical building using a selected number of occupants assigned some behavior to explore the approach which can be further tested in the future using actual building case studies and occupant data for validation.

166

8.6. Recommendations for Future Work

This study presented an approach to gathering occupant-related energy use data using an interactive monitoring method. The relationships between different occupant values were identified and the case studies helped in developing individual occupant profiles for a selection of values. Not all the parameters that were measured in the empirical energy studies (from Chapter 6) were included in the results, but the data will be helpful to study a variety of energy use and occupant- related parameters in buildings.

Integration with control system logic: This study can be extended to develop comprehensive end- user profiles in a format that can be integrated with building control systems logic so that they can better account for end-user values. This can be further explored through the ABM simulation approach coupling with other simulation tools for improved representation of occupant values and to complement the capabilities and features of the ABM tool. An early intervention during the design phase is important for new buildings.

Further validation and testing of the ABM approach: Further research should be conducted to develop and validate the ABM approach enhancing the integration of the occupant values and expanding the capabilities to include different building types. The approach can be enhanced by coupling with other simulation tools to explore the interactions of different occupant values and validation with empirical measurements. There is a need for validation of the ABM approach, different combinations of occupant behavior should be addressed, and a more holistic approach to occupant behavior modeling using intelligent agents is necessary to simultaneously improve occupant satisfaction and reduce energy consumption in the case studies. These can also be integrated with learning algorithms to predict occupant behavior and changes in preferences over a period. The measurements can be compared with a simulation model developed in an energy modeling software and empirical data from the case studies to validate the scenarios. The usability and appropriateness of the approach can also be validated with professionals in the industry. 167

In-depth study of other industries: A more in-depth study of the different industries with more respondents focusing on specific building systems is beneficial and can also reveal additional lessons that can be learned for buildings. This could lead to the development of guidelines for the design of buildings systems and building controls during the design phase.

A more holistic approach to addressing occupant-related factors in buildings: It is essential to identify how occupant-related information can be collected in buildings and how this information can be used to improve building operation on a continuous basis. This study can be extended to include different building types and configurations. The data collected can also be made available to occupants real-time which can enhance their behavior and increase awareness of their impact on energy consumption.

8.7. Concluding Remarks

The potential of integrating occupant values and preferences to improve satisfaction and reduce energy consumption was addressed in this study. Occupant values were discussed, and the need for this integration with building systems was presented along with the benefits of integration.

An approach for the integration of occupant values was proposed in this study along with findings from the study of other industries and case study buildings. The need to reconsider taking control from occupants and making allowance for some end-user control coupled with intelligent building controls was discussed.

From the study of the transportation sector, it was noted that one of the drivers for enhancing end-user values is the need to stay ahead of the competition and the increased requirements from regulatory bodies to keep improving efficiency and reducing emissions while meeting the needs of the end users. Quality control is also an important factor, the need for continuous improvement is emphasized, and the products undergo rigorous testing for safety, comfort, and usability. Regarding the indoor environmental conditions, automobiles are beginning

168

to use personalized profiles; airplanes have individual controls. An efficient approach for indoor conditioning either through highly intelligent dynamic controls or providing some level of individual control to meet the needs of occupants is beneficial.

As stated earlier, interactively measuring energy use, indoor environmental conditions and occupant satisfaction and preferences through the feedback survey is an integral part of this study to provide a means to track occupant behavior and determine occupant satisfaction indoors. This study provided valuable data that highlighted occupant values in buildings and were used for the development of profiles for the occupants, building types and climates being considered. The recommendations from the evaluation of the proposed approach highlighted the need for improved communication, occupant education, improved controls and affordable implementation cost.

Databases for benchmarking buildings should be provided to allow for comparison of building energy performance and to identify opportunities for improvement. The need for maintaining occupant values and preferences in buildings cannot be overemphasized. Buildings are made for the occupants. The indoor environment should not negatively affect occupant values and conditions should be conducive for human habitation.

169

REFERENCES

1. 7Group, & Reed, B. G. (2009). The Integrative Design Guide to Green Building:

Redefining the Practice of Sustainability. Hoboken: John Wiley & Sons.

2. Abraham, Y. S., Anumba, C. J., & Asadi, S. (2017a). Data Sensing Approaches to

Monitoring Building Energy Use and Occupant Behavior. In K-Y. Lin, N. El-Gohary, &

P. Tang (Ed.). International Workshop on Computing in Civil Engineering. Seattle:

American Society of Civil Engineers (ASCE).

3. Abraham, Y. S., Zhao, Z., Anumba, C. J., & Asadi, S. (2017b). Implementation of a

Preference Monitoring Application for Office Building Occupants. In F. Bosche, I.

Brilakis, & R. Sacks (Ed.), Proceedings of the Joint Conference on Computing in

Construction (JC3). 1, pp. 793-801. Heraklion: ITC Digital Library.

4. Abraham, Y., Amasyali, K., El-Gohary, N., & Anumba, C. (2015). The Need for Human-

Centered and Value-Sensitive Improvement of Building Energy Efficiency. The 7th

International Conference of SuDBE (pp. 1-8). Reading: SuDBE.

5. Accenture. (2010). Understanding Consumer Preferences in Energy Efficiency: Accenture

End-consumer Observatory on Electricity Management 2010. Accenture. Retrieved from

https://www.accenture.com/t20160105T033228__w__/us-en/_acnmedia/Accenture/next-

gen/insight-unlocking-value-of-digital-consumer/PDF/Accenture-Understanding-

Consumer-Preferences-Energy-Efficiency-10-0229-Mar-11.pdf [Accessed Dec. 10, 2014].

6. Adikari, S., McDonald, C., & Campbell, J. (2011). A Design Science Framework for

Designing and Assessing User Experience. In J. A. Jacko (Ed.), 14th International

Conference, HCI International. Orlando: Springer.

7. Ahmad, M. W., Mourshed, M., Mundow, D., Sisinni, M., & Rezgui, Y. (2016). Building

Energy Metering and Environmental Monitoring – A State-of-the-Art Review and

170

Directions for Future Research. Energy and Buildings, 85-102.

doi:10.1016/j.enbuild.2016.03.059

8. Airbus. (2017). Cabin and Comfort. Airbus. Retrieved from AIRBUS Leading Aircraft

Manufacturer: http://www.airbus.com/aircraft/passenger-aircraft/cabin-

comfort.html#passengers [Accessed Nov. 6, 2017].

9. Akamatsu, M., Green, P., & Bengler, K. (2013). Automotive Technology and Human

Factors Research: Past, Present, and Future. International Journal of Vehicular

Technology, 1-28.

10. Alzheimer Europe. (2009). Types of research: The Four Main Approaches. Retrieved from

Alzheimer Europe: http://www.alzheimer-europe.org/Research/Understanding-dementia-

research/Types-of-research/The-four-main-approaches [Accessed Aug. 25, 2017].

11. Amasyali, K., & El-Gohary, N. M. (2016). Energy-Related Values and Satisfaction Levels

of Residential and Office Building Occupants. Building and Environment.

12. Andrews, C. J., Putra, C. H., & Brennan, C. (2013). Simulation Modeling of Occupant

Behavior in Commercial Buildings. Center for Green Building at Rutgers University for

the Energy Efficient Buildings Hub, Philadelphia, PA.

13. Andrews, C. J., Yi, D., Krogmann, U., Senick, J. A., & Wener, R. E. (2011). Designing

Buildings for Real Occupants: An Agent-Based Approach. IEEE Transactions on Systems,

Man, and Cybernetics- Part A: Systems and Humans, 41(6), 1077-1091.

14. Anylogic. (2016). Agent Based Modeling. Retrieved from Anylogic: Multimethod

Simulation Software: http://www.anylogic.com/agent-based-modeling [Accessed Jan. 25,

2017]

15. AnyLogic. (2017). AnyLogic. Retrieved from https://www.anylogic.com/ [Accessed Sep.

5, 2017]

16. Architecture 2030. (2014). Roadmap to Zero Emissions. New Mexico. 171

17. Architecture 2030. (2015). Why the Building Sector? Retrieved from Architecture 2030:

http://architecture2030.org/buildings_problem_why/the-solution/ [Accessed Feb. 5, 2017]

18. Arciszewski, T., Skolicki, Z., & De Jong, K. (2005). Intelligent Agents: Fundamentals. In

C. J. Anumba, O. O. Ugwu, & Z. Ren (Eds.), Agents and Multi-agent Systems in

Construction (pp. 6-30). Oxon: Taylor and Francis.

19. Argonne National Laboratory. (2017). Repast Documentation. Retrieved from Repast:

https://repast.github.io/docs.html [Accessed Sep. 3, 2017]

20. ASHRAE. (2004). Standard 55: Thermal Environmental Conditions for Human

Occupancy. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning

Engineers.

21. ASHRAE. (2007). ASHRAE Handbook Applications. Atlanta: American Society of

Heating, Refrigerating and Air-Conditioning Engineers.

22. ASHRAE. (2010). Standard 62.1: Ventilation for acceptable indoor air quality. Atlanta:

American Society of Heating, Refrigeration and Air-Conditioning Engineers Inc.

23. ASHRAE. (2013). Ventilation for Acceptable Indoor Air Quality: Standard 62.1. Atlanta:

American Society of Heating and Refrigeration Engineers.

24. ASHRAE. (2013b). Air Quality within Commercial Aircraft: Standard 161. Atlanta:

American Society of Heating and Refrigeration Engineers.

25. Autodesk. (2017). Sustainability Workshop. Retrieved from

https://sustainabilityworkshop.autodesk.com/buildings/lighting-and-daylighting-design

[Accessed Sep. 15, 2017]

26. Azar, E. (2014). A Comprehensive Framework to Assess, Model, and Enhance the Human

Role in Conserving Energy in Commercial Buildings. Ph.D. Dissertation, The University

of Wisconsin- Madison, Madison: ProQuest Dissertations and Theses.

172

27. Azar, E., & Menassa, C. (2011). An Agent-Based Approach to Model the Effect of

Occupants’ Energy Use Characteristics in Commercial Buildings. ASCE International

Workshop on Computing in Civil Engineering (pp. 536-543). Miami: American Society of

Civil Engineers (ASCE).

28. Azar, E., & Menassa, C. C. (2012). A Comprehensive Analysis of the Impact of Occupancy

Parameters in Energy Simulation in Office Buildings. Energy and Buildings, 55, 841-853.

29. Ball, M., & Callaghan, V. (2012). Managing Control, Convenience, and Autonomy: A

Study of Agent Autonomy in Intelligent Environments. Journal of Ambient Intelligence

and Smart Environments, 1-38.

30. Barley, D., Deru, M., Pless, S., & Torcellini, P. (2005). Procedure for Measuring and

Reporting Commercial Building Energy Performance. National Renewable Energy

Laboratory. Golden: National Renewable Energy Laboratory.

31. Bastani, M. S., Asadi, S., & Anumba, C. J. (2016). Application of Bass Diffusion Theory

to Simulate the Impact of Feedback and Word of Mouth on Occupants’ Behavior in

Commercial Buildings: An Agent-Based Approach. Journal of Architectural Engineering,

1-13.

32. Becerik-Gerber, B., Siddiqui, M. K., Brilakis, I., El-Anwar, O., El-Gohary, N., Mahfouz,

T., Jog, G. M., Li, S., Kandil, A. A. (2014). Civil Engineering Grand Challenges:

Opportunities for Data Sensing, Information Analysis and Knowledge Discovery. Journal

of Computing in Civil Engineering, 1-13. doi:10.1061/(ASCE)CP.1943-5487.0000290

33. Bluyssen, P. M. (2006). The Indoor Environment Handbook. Oxon: Earthscan.

34. Bluyssen, P. M. (2009). The Indoor Environment Handbook: How to Make Buildings

Healthy and Comfortable. London: Earthscan.

35. Bluyssen, P. M. (2010). Towards new methods and ways to create healthy and comfortable

buildings. Building and Environment, 808-818. 173

36. BMW. (2015). BMW Insights. Retrieved from BMW Technology Guide:

http://www.bmw.com/com/en/insights/technology/technology_guide/articles/driver_profi

le.html?source=index&article=driver_profile

37. Bonabeau, E. (2002). Agent-based Modeling: Methods and Techniques for Simulating

Human Systems. Proceedings of the National Academy of Sciences of the United States of

America, 99(3), 7280-7287.

38. Bonte, M., Thellier, F., & Lartigue, B. (2014). Impact of occupant's actions on energy

building performance and thermal sensation. Energy and Buildings, 76, 219-227.

39. Booth, A., Greene, M., & Tai, H. (2010). U.S. smart grid value at stake: The $130 billion

question. McKinsey Report on Smart Grid.

40. Bordass, B., Bromley, K., & Leaman, A. (1993). User and Occupant Controls in Office

Buildings. BRE Building Design, Technology and Occupant Well-being in Temperate

Climates. Brussels: Building Research Establishment.

41. Boztepe, S. (2007). User Value: Competing Theories and Models. International Journal of

Design, 1(2), 55-63.

42. Bryant, S., & Wrigley, C. (2015). Driving Toward User-Centered Design in Automotive

Design. The Design Management Institute, 74-84.

43. Callaghan, V. (2013). Intelligent Environments. In D. J. Clements-Croome (Ed.),

Intelligent Buildings (2nd ed., pp. 71-87). Westminster, London: ICE Publishing.

44. Cao, Y., Wang, T., & Song, X. (2015). An Energy-aware, Agent-based Maintenance-

scheduling Framework to Improve Occupant Satisfaction. Automation in Construction, 60,

49-57.

45. Carmenate, T., Inyim, P., Pachekar, N., Chauhan, G., Bobadilla, L., Batouli, M., &

Mostafavi, A. (2016). Modeling Occupant-Building-Appliance Interaction for Energy

Waste Analysis. In O. Chong, K. Parrish, P. Tang, D. Grau, & J. Chang (Ed.), International 174

Conference on Sustainable Design, Engineering and Construction. 145, pp. 42-49. Tempe:

Elsevier.

46. Carmenate, T., Rahman, M. M., Leante, D., Bobadilla, L., & Mostafavi, A. (2015).

Modeling and Analyzing Occupant Behaviors in Building Energy Analysis Using an

Information Space Approach. IEEE International Conference on Automation Science and

Engineering (pp. 425-431). Gothenburg: IEEE.

47. CBE. (2015). Center for the Built Environment- Occupant Indoor Environmental Quality

(IEQ) Survey. Retrieved from http://www.cbe.berkeley.edu/research/survey.htm

[Accessed Oct. 18, 2015]

48. CBE. (2014). Advanced Human Thermal Comfort Model. Retrieved from Center for the

Built Environment: http://www.cbe.berkeley.edu/research/briefs-thermmodel.htm

49. Chen, Y., Liang, X., Hong, T., & Luo, X. (2017). Simulation and Visualization of Energy-

related Occupant Behavior in Office Buildings. Building Simulation, 1-14.

50. Chingcuanco, F., & Miller, E. J. (2012). A Microsimulation Model of Urban Energy Use:

Modeling Residential Space Heating Demand in ILUTE. Computers, Environment and

Urban Systems, 36, 186-194.

51. CIBSE. (2009). Building control systems: CIBSE guide H. London: The Chartered

Institution of Building Services Engineers

52. CIOB. (2017). Value in Building Design and Construction. Retrieved from Designing

Buildings Wiki:

www.designingbuildings.co.uk/wiki/Value_in_building_design_and_construction

[Accessed Apr. 20, 2017]

53. Clarke, J. A. (2001). Energy Simulation in Building Design (2nd Ed.). Oxford:

Butterworth-Heinemann.

175

54. Clements-Croome, D. J. (2013). Environmental Health and Well-being in Buildings. In D.

J. Clements-Croome (Ed.), Intelligent Buildings (pp. 43-59). Westminster, London: ICE

Publishing.

55. Clements-Croome, D., & Li, B. (2000). Productivity and indoor environment. Proceedings

of Healthy Buildings. Finland.

56. Clevenger, C. M., & Haymaker, J. (2006). The Impact of the Building Occupant on Energy

Modeling Simulations. Joint International Conference on Computing and Decision

Making in Civil and Building Engineering (pp. 1-10). Montreal: Citeseer.

57. Cole, R. J., Robinson, J., Brown, Z., & O'Shea, M. (2010). Re-contextualizing the Notion

of Comfort. In Comfort in a Lower Carbon Society (pp. 19-32). New York: Taylor and

Francis.

58. Cole, W. (2004). Meet the leader of a team charged with determining how to give 7E7

passengers the best airplane ride. Boeing Frontiers, 3(8). Retrieved from

http://www.boeing.com/news/frontiers/archive/2004/december/ts_sf06.html [Accessed

Aug. 18, 2016]

59. Crawford, R., Bartak, E. L., Stephan, A., & Jensen, C. (2016). Evaluating the life cycle

energy benefits of energy efficiency regulations for buildings. Renewable and Sustainable

Energy Reviews, 63, 435-451.

60. Creswell, J. W. (2003). Research Design: Qualitative, Quantitative, Mixed Methods

Appraoches. Thousand Oaks: Sage Publications.

61. Creswell, J. W. (2013). Qualitative Inquiry and Research Design: Choosing among Five

Approaches (3rd ed.). Los Angeles: SAGE Publications.

62. Creswell, J. W., & Miller, D. L. (2000). Determining Validity in Qualitative Inquiry.

Theory into Practice, 39(3), 124-130. doi:10.1207/s15430421tip3903_2

176

63. Cui, W., Cao, G., Park, J. H., Ouyang, Q., & Zhu, Y. (2013). Influence of indoor air

temperature on thermal comfort, motivation and performance. Building and Environment,

68, 114-122.

64. Dallinga, R. P., & Bos, J. E. (2010). Cruise Ship Seakeeping and Passenger Comfort.

HPAS, 1-7. Retrieved from Maritime Research Institute Netherlands:

http://www.marin.nl/web/Paper-download-page.htm?lf=94536&sf=94534 [Accessed Oct.

2, 2016]

65. de Dear, R., & Brager, G. S. (1998). Towards an Adaptive Model of Thermal Comfort and

Preference. ASHRAE Transactions, 104(1), 145-167.

66. de Dear, R., & Brager, G. S. (2001). The Adaptive Model of Thermal Comfort and Energy

Conservation in the Built Environment. International Journal of Biometeorology, 45(2),

100-108.

67. Deffuant, G., Amblard, F., Weisbuch, G., & Faure, T. (2002). How can Extremism Prevail?

A Study Based on the Relative Agreement Interaction Model. Journal of Artificial Societies

and Social Simulation, 5(4).

68. Ding, Z., & Wu, J. (2017). ABM Based Simulation Research on Construction Waste

Management. In Y. Wu, S. Zheng, J. Luo, W. Wang, Z. Mo, & L. Shan (Ed.), Proceedings

of the 20th International Symposium on Advancement of Construction Management and

Real Estate (pp. 465-476). Singapore: Springer.

69. E3. (2012). General Plug Loads Data Collection and Analysis. Australia: Commonwealth

of Australia.

70. Ebinger, C., Huttman, N., Avasarala, G., Redbois, D., & Bailey, C. (2010). Energy

efficiency. Energy Security Initiative. Doha.

71. Eley, C. (2000). Energy Monitoring. In AIA, The Architect’s Handbook of Professional

Practice. John Wiley & Sons. 177

72. Emes, M. R., Smith, A., & Marjanovic-Halburd, L. (2012). Systems for Construction:

Lessons for the Construction Industry from Experiences in Spacecraft Systems

Engineering. Intelligent Buildings Journal, 4(2), 67-88.

doi:http://dx.doi.org/10.1080/17508975.2012.680428

73. Enerdata. (2015). Total Energy Consumption. Retrieved from Global Energy Statistical

Yearbook: https://yearbook.enerdata.net/ [Accessed May. 4, 2016]

74. Federspiel, C. C. (1998). Statistical Analysis of Unsolicited Thermal Sensation Complaints

in Commercial Buildings. ASHRAE Transactions, 104(1), 912-923.

75. Fellows, R., & Liu, A. (2008). Research Methods for Construction (3rd ed.). West Sussex:

John Wiley & Sons.

76. Fink, A. (2013). How to Conduct Surveys: A Step-by-Step Guide (6th ed.). Los Angeles:

SAGE Publications.

77. Fisk, W. J. (2000). Health and Productivity Gains from Better Indoor Environments and

their Relationship with Building Energy Efficiency. Annual Review of Energy and the

Environment, 25, 537-566.

78. Fisk, W. J., & Seppänen, O. A. (2007). Providing Better Indoor Environment Quality

Brings Economic Benefits. Proceedings of the Clima 2007 Wellbeing Indoors Conference.

79. Freire, R. Z., Oliveira, G. H. & Mendes, N., 2008. Predictive Controllers for Thermal

Comfort Optimization and Energy Savings. ABCM Symposium Series in Mechatronics,

Volume 3, pp. 839-848.

80. Frontczak, M., & Wargocki, P. (2011). Literature survey on how different factors influence

human comfort in indoor environments. Building and Environment, 46, 922-937.

81. Gaetani, I., Hoes, P.-J., & Hensen, J. L. (2016). Occupant Behavior in Building Energy

Simulation: Towards a Fit-for-Purpose Modeling Strategy. Energy and Buildings, 188-204.

178

82. Given, L. M. (2008). The SAGE Encyclopedia of Qualitative Research Methods. Thousand

Oaks: SAGE Publications. doi:10.4135/9781412963909

83. Gligor, V. (2004). Luminous Environment and Productivity at Workplaces. Espoo:

Helsinki University of Technology.

84. GM. (2015). Design & Technology: Emerging Technology. Retrieved from General

Motors: http://www.gm.com/vision/design_technology.html#/Emerging_Technology

[Accessed Nov. 12, 2016]

85. Grin, R. (2015, February). Passenger Ship Comfort. Maritime Reporter & Engineering

News, p. 20.

86. Grondzik, W. T., & Kwok, A. G. (2015). Mechanical and Electrical Equipment for

Buildings. New Jersey: John Wiley & Sons.

87. Grueber, M., Studt, T., Horowitz, M., Tripp, S., Gillespie, A., Rosenberg, E., McCoy,

Pamela., Kuchta, C. (2013). 2014 Global R&D Funding Forecast. U.S.: Battelle and R&D

Mag.

88. Harrison, H., Birks, M., Franklin, R., & Mills, J. (2017). Case Study Research: Foundations

and Methodical Orientations. Forum: Qualitative Social Research, 18(1).

doi:http://dx.doi.org/10.17169/fqs-18.1.2655

89. Harteveldt, H. H. (2016). The Future of Airline Distribution. Atmosphere Research Group.

International Air Transport Association.

90. Hazelrigg, G. A. (1998). A Framework for Decision-based Engineering Design.

Transactions-American Society of Mechanical Engineers Journal of Mechanical Design,

120, 653-658.

91. Healy, W. N. (2010). Measurement Challenges in Achieving Energy Monitoring Systems

in Buildings. National Institute of Standards and Technology. National Institute of

Standards and Technology. 179

92. Hegselmann, R., & Krause, U. (2002). Opinion Dynamics and Bounded Confidence

Models, Analysis, and Simulation. Journal of Artificial Societies and Social Simulation,

5(3), 1-33.

93. Hemsath, T. L. & Bandhosseini, K. A., 2018. Energy Modeling in Architectural Design.

New York: Routledge.

94. Hetu, J., & Plante, S. (2008). Vehicle On-Board Diagnostics Added Values. SAE Technical

Paper, 1-8.

95. Hoes, P., Hensen, J. L., Loomans, M. G., de Vries, B., & Bourgeois, D. (2009). User

Behavior in Whole Building Energy Simulation. Energy and Buildings, 41(3), 295-302.

96. Holland, J. H. (1995). Hidden order. How adaptation builds complexity. Reading:

Addison-Wesley Publishing Company.

97. Hong, T., & Lin, H.-W. (2012). Occupant Behavior: Impact on Energy Use of Private

Offices. International Building Performance Simulation Association. Shanghai: Asim.

98. Hong, T., Chang, W.-K., & Lin, H.-W. (2013). A Fresh Look at Weather Impact on Peak

Electricity Demand and Energy Use of Buildings Using 30-Year Actual Weather Data.

Journal of Applied Energy, 1-31.

99. Hong, T., Chou, S. K., & Bong, T. Y. (2000). Building Simulation: An Overview of

Developments and Information Sources. Building and Environment, 35, 347-361.

100. Hong, T., D'Oca, S., Taylor-Lange, S. C., Turner, W. J., Chen, Y., & Corgnati, S. P.

(2015b). An Ontology to Represent Energy-related Occupant Behavior in Buildings. Part

2: Implementation of the DNAS Framework using and XML schema. Journal of Building

and Environment, 196-205.

101. Hong, T., D'Oca, S., Turner, W. J., & Taylor-Lange, S. C. (2015a). An Ontology to

Represent Energy-related Occupant Behavior in Buildings Part 1: Introduction to the

DNAs Framework. Journal of Buildings and Environment, 764-777. 180

102. Hong, T., Taylor-Lange, S. C., D'Oca, S., Yan, D., & Corginati, S. P. (2016). Advances in

Research and Applications of Energy-Related Occupant Behavior in Buildings. Energy

and Buildings, 694-702. doi:10.1016/j.enbuild.2015.11.052

103. Humphreys, M., Nicol, F., & Roaf, S. (2016). Adaptive Thermal Comfort: Foundation and

Analysis. Oxon: Routledge.

104. IBM. (2017). Advantages of Java. Retrieved November 4, 2017, from IBM Knowledge

Center:

https://www.ibm.com/support/knowledgecenter/en/ssw_aix_72/com.ibm.aix.performance

/advantages_java.htm

105. IEA. (2013). Transition to sustainable buildings- strategies and opportunities to 2050.

France: International Energy Agency.

106. IEA. (2015). International Energy Agency Statistics. Retrieved from International Energy

Agency: http://www.iea.org/statistics/ [Accessed Dec. 12, 2016]

107. ISIAQ and CIB. (2004). Performance criteria of buildings for health and comfort. Helsinki:

International Society for indoor air quality and environment and international council for

research and innovation in building and construction Task group 42.

108. Jacko, J. A. (2012). Human-Computer Interaction Handbook: Fundamentals, Evolving

Technologies, and Emerging Applications (3rd ed.). Baton Rouge: CRC Press.

109. Jazizadeh, F., Ghahramani, A., Becerik-Gerber, B., Kichkaylo, T., & Orosz, M. (2014).

Human-Building Interaction Framework for Personalized Thermal Comfort-Driven

Systems in Office Buildings. Journal of Computing in Civil Engineering, 28(1), 2-16.

110. Kamara, J. M., Anumba, C. J., & Evbuomwan, N. F. (2002). Capturing Client

Requirements in Construction Projects. London: Thomas Telford.

111. Kara, D., & Baxendale, P. (2012). An Agent-Based Building Energy Consumption Model.

IEEE PES Innovative Smart Grid Technologies (pp. 1-5). Tianjin: IEEE. 181

112. Kavulya, G., & Becerik-Gerber, B. (2012). Understanding the Influence of Occupant

Behavior on Energy Consumption Patterns in Commercial Buildings. ASCE Computing in

Civil Engineering, 569-576.

113. Kay, A. (1984). Computer Software. Scientific American, 251(3), 53-59.

114. Kazmi, A. H., O'Grady, M. J., Delaney, D. T., Ruzzelli, A. G., & O'Hare, G. M. (2014). A

Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems.

ACM Transactions on Sensor Networks, 10(4), 1-43.

115. Khazaii, J. (2016, February). Agent-Based Modeling. ASHRAE Journal, pp. 62-64.

116. Kibert, C. J. (2013). Sustainable Construction. New Jersey: John Wiley & Sons, Inc.

117. Kim, S., & Woo, J.-H. (2011). Analysis of the Differences in Energy Simulation Results

between Building Information Modeling (BIM) based Simulation Method and the Detailed

Simulation Method. Simulation Conference. Phoenix: IEEE.

118. Klein, L., Kwak, J., Jazizadeh, F., Becerik-Gerber, B., Varakantham, P., & Tambe, M.

(2012). Coordinating Occupant Behavior for Building Energy and Comfort Management

using Multi-agent Systems. Automation in Construction.

119. Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M., Switzer, P., . . .

Engelmann, W. H. (2001). National Human Activity Pattern Survey: A Resource for

Assessing Exposure to Environmental Pollutants. Journal of Exposure Analysis and

Environmental Epidemiology, 11(3), 231-252.

120. Kolokotsa, D. et al., 2001. Advanced Fuzzy Logic Controllers Design and Evaluation for

Buildings' Occupants Thermal-Visual Comfort and Indoor Air Quality Satisfaction. Energy

and Buildings, Volume 33, pp. 531-543.

121. Kosow, H., & Gaßner, R. (2008). Methods of Future and Scenario Analysis: Overview,

Assessment, and Selection Criteria. Bonn, Germany: Deutsches Institut für

Entwicklungspolitik gGmbH. 182

122. Kothari, C. R. (2004). Research Methodology: Methods and Techniques (2nd ed.). New

Delhi: New Age International Limited.

123. Kravari, K., & Bassiliades, N. (2015). A Survey of Agent Platforms. Journal of Artificial

Societies and Social Simulation, 18(1), 11.

124. Kuligowski, E. D., Peacock, R. D., & Hoskins, B. L. (2010). A Review of Building

Evacuation Models (2nd Ed.). National Institute Of Standards and Technology.

125. LaMarche, J., Cheney, K., Christian, S., & Roth, K. (2012). Home Energy Management

Products and Trends. ACEEE Summer Study on Energy Efficiency in Buildings. California:

Fraunhofer.

126. Law, K., Latombe, J.-C., & Dauber, K. (2005). Computational Modeling of Non-adaptive

Crowd Behaviors for Egress Analysis: 2004-2005 CIFE Seed Project Report. Stanford:

Center for Integrated Facility Engineering. Retrieved August 10, 2016

127. Lee, S.-C., Poon, C.-S., Li, X.-D., & Luk, F. (1999). Investigation of Cabin Air Quality in

Commercial Aircrafts. Indoor Air, 9, 180-187.

128. Lee, Y. S., & Malkawi, A. M. (2014). Simulating Multiple Occupant Behaviors in

Buildings: An Agent-based Modeling Approach. Energy and Building, 69, 407–416.

129. Leedy, P. D., & Ormrod, J. E. (2005). Practical Research: Planning and Design. (8th ed.).

Upper Saddle River: Prentice Hall.

130. Levermore, G. J. (2000). Building Energy Management Systems: An Application to

Heating and Control. London: Spon.

131. Liao, C., Lin, Y., & Barooah, P. (2012). Agent-based and Graphical Modelling of Building

Occupancy. Journal of Building Performance Simulation, 5-25.

132. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry (1st ed.). Newbury Park: SAGE

Publications.

183

133. Lloyds Register. (2008). The Human Element: An Introduction. The Lloyds Register

Group.

134. Lofland, J. (1995). Analyzing Social Settings: A Guide to Qualitative Observation and

Analysis (3rd ed.). Belmont: Wadsworth.

135. Lucon O., D. Ürge-Vorsatz, A. Zain Ahmed, H. Akbari, P. Bertoldi, L.F. Cabeza, N. Eyre,

A. Gadgil, L.D.D. Harvey, Y. Jiang, E. Liphoto, S. Mirasgedis, S. Murakami, J. Parikh, C.

Pyke, and M.V. Vilariño. (2014). Buildings. In: Climate Change 2014: Mitigation of

Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y.

Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier,

B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx

(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY,

USA.

136. Macal, C. M., & North, M. J. (2007). Agent-based Modeling and Simulation: Desktop

ABMS. Winter Simulation Conference (pp. 95-106). IEEE.

137. Mahajan, V., Muller, E., & Bass, F. M. (1990). New Product Diffusion Models in

Marketing: A Review and Directions for Research. Journal of Marketing, 54, 1-26.

138. Mahdavi, A., Taheri, M., Schuss, M., Tahmasebi, F., & Glawischnig, S. (2018). Structured

Building Data Management: Ontologies, Queries, and Platforms. In A. Wagner, W.

O’Brien, & B. Dong (Eds.), Exploring Occupant Behavior in Buildings (pp. 261-286).

Cham: Springer.

139. Maile, T., Fischer, M., & Bazjanac, V. (2007). Building Energy Performance Simulation

Tools- a Life-Cycle and Interoperable Perspective. Stanford: Center for Integrated Facility

Engineering (CIFE).

184

140. Mang, P., Haggard, B., & Regenesis. (2016). Regenerative Development and Design: A

Framework for Evolving Sustainability. Hoboken: John Wiley & Sons.

141. Marshall, C., & Rossman, G. B. (2016). Designing Qualitative Research (6th Ed.).

Thousand Oaks: SAGE.

142. Masoso, O. T., & Grobler, L. J. (2010). The Dark Side of Occupant's Behavior on Building

Energy Use. Energy and Buildings, 42, 173-177.

143. Mast, E. H., van Kuik, G. A., & van Bussel, G. J. (2007). Agent-Based Modelling for

Scenario Development of Offshore Wind Energy. The Netherlands: Delft University of

Technology.

144. Mathison, S. (2005). Encyclopedia of Evaluation. Thousand Oaks: SAGE Publications.

doi:10.4135/9781412950558

145. Maxwell, J. A. (2012). Qualitative Research Design: An Interactive Approach (3rd Ed.).

Los Angeles: SAGE Publishers.

146. McNabb, D. E. (2008). Research Methods in Public Administration and Nonprofit

Management: Quantitative and Qualitative Approaches (2nd Ed.). M.E. Sharpe.

147. Melosi, M. V. (2010). The Automobile and the Environment in American History.

Retrieved July 1, 2017, from

http://www.autolife.umd.umich.edu/Environment/E_Overview/E_Overview4.htm

148. Merriam, S. B. (1998). Qualitative Research and Case Study Applications in Education

(2nd ed.). San Francisco: Jossey-Bass Publishers.

149. Murtagh, N., Nati, M., Headley, W. R., Gatersleben, B., Gluhak, A., Imran, M. A., &

Uzzell, D. (2013). Individual Energy Use and Feedback in an Office Setting: A Field Trial.

Energy Policy, 62, 717-728.

150. Myrsalieva, N., & Barghouth, A. (2015) Arab Future Energy Index (AFEX): Energy

Efficiency, Egypt: Regional Center for Renewable Energy and Energy Efficiency 185

(RCREEE). Retrieved from:

http://www.rcreee.org/sites/default/files/afex_ee_2015_engish_web_0.pdf

151. NAED. (2011). The Building Energy Management Market, Smart Grid Focus.

152. National Research Council. (2002). The Airliner Cabin Environment and the Health of

Passengers and Crew. Washington: National Academies Press (U.S.). Retrieved from

NCBI: http://www.ncbi.nlm.nih.gov/books/NBK207472/

153. Nguyen, A. T., & Aiello, M. (2013). Energy Intelligent Buildings Based on User Activity:

A Survey. Energy and Buildings, 56, 244-257.

154. NSTC. (2011). Submetering of Building Energy and Water Usage: Analysis and

Recommendations of the Subcommittee on Buildings Technology Research and

Development. Washington, DC: National Science and Technology Council (NSTC).

155. Oancea, C., & Caluianu, S. (2013). Designing Intelligent Buildings for People's Well-being

using an Artificial Intelligence Approach. In D. J. Clements-Croome (Ed.), Intelligent

Buildings: Design, Management and Operation (pp. 89-105). Westminster, London: ICE

Publishing.

156. OECD/IEA. (2014). World Energy Outlook. France: International Energy Agency.

157. Onyango, J., & Ciaran, R. (2014). Impact of User Behavior in Office Building on Energy

Reduction Strategies. Innovative Energy Policy, 3(112), 1-8.

158. Ortiz, M. A., Kurvers, S. R., & Bluyssen, P. M. (2017). A Review of Comfort, Health, and

Energy Use: Understanding Daily Energy Use and Wellbeing for the Development of a

New Approach to Study Comfort. Energy and Buildings, 152, 323-335.

159. Pan, X. (2006). Computational Modeling of Human and Social Behaviors for Emergency

Egress Analysis. Stanford University: Ph.D. Thesis- Center for Integrated Facility

Engineering.

186

160. Passino, K. (2001). Intelligent Control: An Overview of Techniques. In T. Samad,

Perspectives in Control: New Concepts and Applications (pp. 1-34). New Jersey: IEEE

Press.

161. Perez-Lombard, L., Ortiz, J., & Pout, C. (2008). A Review on Buildings Energy

Consumption Information. Energy and Buildings, 40, 394-398.

162. Perry, C., 2017. Smart Buildings: A Deeper Dive into Market Segments, Washington DC:

American Council for an Energy-Efficient Economy (ACEEE).

163. Putra, H. C., Andrews, C. J., & Senick, J. A. (2017). An Agent-based Model of Building

Occupant Behavior during Load Shedding. Building Simulation, 10(6), 845-859.

doi:https://doi.org/10.1007/s12273-017-0384-x

164. QEERI. (2015). Energy Security: Energy Efficiency. Retrieved from Qatar Environment &

Energy Research Institute: http://www.qeeri.org.qa/en/grand-challenges/energy-

security/energy-efficiency [Accessed Dec. 12, 2016]

165. Qualtrics. (2015). Qualtrics Survey Software. Provo, Utah, USA. Retrieved from

http://www.qualtrics.com [Accessed Dec 12, 2016]

166. Rafsanjani, H. N., Ahn, C. R., & Alahmad, M. (2015). A Review of Approaches for

Sensing, Understanding, and Improving Occupancy-related Energy-use Behaviors in

Commercial Buildings. Energies, 8(10), 10996-11029.

167. Reliability HotWire. (2008, November). Interaction between the House of Quality (within

Design for Six Sigma) and the FMEA (within Design for Reliability). Reliability HotWire

(93).

168. Ren, Z., & Anumba, C. J. (2005). MASCOT: A Multi-agent System for Construction

Claims Negotiation. In C. J. Anumba, O. O. Ugwu, & Z. Ren (Eds.), Agents and Multi-

agent Systems in Construction (pp. 162-185). Oxon: Taylor and Francis.

187

169. Ren, Z., Anumba, C. J., & Ugwu, O. O. (2005). Multi-agent Systems in Construction. In

C. J. Anumba, O. O. Ugwu, & Z. Ren (Eds.), Agents & Multi-agent Systems in

Construction (pp. 31-86). London: Taylor & Francis.

170. Ridi, A., Gisler, C., & Hennebert, J. (2014). A Survey on Intrusive Load Monitoring for

Appliance Recognition. 22nd International Conference on Pattern Recognition (pp. 3702-

3707). Stockholm: IEEE.

171. Rosenbaum, M. (2003). Understanding the Energy Modeling Process: Simulation Literacy

101. Pittsburgh: BuildingGreen Inc.

172. Sagun, A., Anumba, C. J., & Bouchlaghem, D. (2013). Designing Buildings to Cope with

Emergencies: Findings from Case Studies on Exit Preferences. Buildings, 3, 442-461.

173. Sanvido, V. E., & Norton, K. J. (1994). Integrated Design-Process Model. Journal of

Management in Engineering, 5, 55-62.

174. Sawhney, A., Bashford, H., Walsh, K., & Mulky, A. R. (2003). Agent-Based Modeling

and Simulation in Construction. Proceedings of the 2003 Winter Simulation Conference

(pp. 1541-1547). New Orleans: IEEE.

175. Schank, J. (2010). Agent-based Models: Methodology and Philosophy. Retrieved from

http://www.agent-based-models.com/blog/2010/03/30/agent-based-modeling/ [Accessed

Aug. 3, 2017]

176. Seligman, C., Darley, J. M., & Becker, L. J. (1977). Behavioral Approaches to Residential

Energy Conservation. Center for Environmental Studies, 1(3), 325-337.

177. Shaikh, P. H., Nor, N. B., Nallagownden, P., Elamvazuthi, I., & Ibrahim, T. (2014). A

Review on Optimized Control Systems for Building Energy and Comfort Management of

Smart Sustainable Buildings. Renewable and Sustainable Energy Reviews, 34, 409-429.

178. Simchak, T., & Ungar, L. (2011). Realizing the Energy Efficiency Potential of Smart Grid:

An Alliance White Paper. Alliance. 188

179. Statistics Authority. (2010). Census of Population and Housing and Establishments. The

state of Qatar. Retrieved from Census 2010:

https://www.mdps.gov.qa/en/statistics/Statistical%20Releases/General/Census/Populatio

n_Households_Establishment_QSA_Census_AE_2010_1.pdf. [Accessed Sep. 10, 2016]

180. Steg, L. (2008). Promoting Household Energy Conservation. Energy Policy, 36, 4449-

4453.

181. Strong, D., & Burrows, V. (2016). A Whole-system Approach to High-performance Green

Buildings (1st ed.). Norwood, MA: Artech House Publishers.

182. Sullivan, G. P., Hunt, W. D., Pugh, R., Sandusky, W. F., Koehler, T. M., & Boyd, B. K.

(2011). Metering Best Practices. U.S. Department of Energy.

183. Sun, R. (2008). Introduction to Computational Cognitive Modeling. In R. Sun (Ed.), The

Cambridge Handbook of Educational Psychology (pp. 3-20). Cambridge University Press.

184. Taber, K. S. (2014). Methodological Issues in Science Education Research: A Perspective

from the Philosophy of Science. In M. R. Matthews (Ed.), International Handbook of

Research in History, Philosophy and Science Teaching (Vol. 3, pp. 1839-1893).

Netherlands: Springer.

185. Taylor, S. J., DeVault, M., & Bogdan, R. (2015). Introduction to Qualitative Research

Methods (4th ed.). Hoboken: Wiley.

186. Treado, S., & Delgoshaei, P. (2010). Agent-Based Approaches for Adaptive Building

HVAC System Control. International High Building Performance Conference (pp. 1-8).

Purdue e-Pubs.

187. Trochim, W. M. (2006). Research Methods. Retrieved from The Research Methods

Knowledge Base, 2nd Edition: http://www.socialresearchmethods.net/kb/hypothes.php

[Accessed Jul. 17, 2017]

189

188. U.S. DOE. (2011). Building Energy Software Tools Directory. Retrieved from U.S.

Department of Energy- Energy Efficiency and Renewable Energy:

http://apps1.eere.energy.gov/buildings/tools_directory/ [Accessed May. 10, 2014]

189. U.S. EIA. (2013). Residential Energy Consumption Survey (RECS) Data. Retrieved from

U.S. Energy Information Administration:

https://www.eia.gov/consumption/residential/data/2009/ [Accessed Jan. 9, 2018]

190. U.S. EIA. (2014). Annual Energy Outlook. Retrieved from U.S. Energy Information

Administration: http://www.eia.gov/forecasts/aeo, 2014-05-07/ [Accessed Sep. 16, 2017]

191. U.S. EIA. (2015). U.S. Energy Demand slows except for Industrial, Commercial Sectors.

Retrieved from U.S. Energy Information Administration:

https://www.eia.gov/todayinenergy/detail.cfm?id=21012 [Accessed Feb. 3, 2017]

192. U.S. EIA. (2016). Commercial Buildings Energy Consumption Survey (CBECS) Data.

Retrieved from U.S. Energy Information Administration:

https://www.eia.gov/consumption/commercial/data/2012/ [Accessed Sep. 16, 2017]

193. U.S. EIA. (2017). Use of Energy in the United States. Retrieved from U.S. Energy

Information Administration:

https://www.eia.gov/energyexplained/index.cfm?page=us_energy_use [Accessed May

10, 2017]

194. U.S. EPA. (2015). Overview of Greenhouse Gases. Retrieved from the United States

Environmental Protection Agency:

http://www.epa.gov/climatechange/ghgemissions/gases.html [Accessed Dec. 30, 2015]

195. U.S. EPA. (2016). Particulate Matter (PM) Basics. Retrieved from the United States

Environmental Protection Agency: https://www.epa.gov/pm-pollution/particulate-matter-

pm-basics [Accessed Nov. 1, 2017]

190

196. U.S. EPA. (2017). History of Reducing Air Pollution from Transportation in the United

States (U.S.). Retrieved from the United States Environmental Protection Agency:

https://www.epa.gov/air-pollution-transportation/accomplishments-and-success-air-

pollution-transportation [Accessed May 27, 2017]

197. Veitch, J. A., & Newsham, G. R. (1998). Determinants of lighting quality I: State of the

Science. Journal of the Illuminating Engineering Society, 27(1), 92-106.

198. Vink, P., & Brauer, K. (2011). Aircraft Interior Comfort and Design. New York: CRC

Press.

199. Virote, J., & Neves-Silva, R. (2012). Stochastic Models for Building Energy Prediction

Based on Occupant Behavior Assessment. Energy and Buildings, 53, 183-193.

200. Wang, S., Yan, C., & Xiao, F. (2012). Quantitative Energy Performance Assessment

Methods for Existing Buildings. Energy and Buildings, 55, 873-888.

doi:10.1016/j.enbuild.2012.08.037

201. Wang, Z., Dounis, I, A., Wang, L., & Yang, R. (2011). An Information Fusion Based

Multi-Agent Control System for Indoor Energy and Comfort Management in Smart and

Green Buildings. Power and Energy Society General Meeting (pp. 1-8). San Diego: IEEE.

202. Weather Analytics. (2015). What are TMY & AMY files? Weather Analytics.

203. Weatherspark. (2017). Comparison of the Average Weather in Doha and Philadelphia.

Retrieved from Weather Spark:

https://weatherspark.com/compare/y/105083~22721/Comparison-of-the-Average-

Weather-in-Doha-and-Philadelphia [Accessed Sep. 16, 2017]

204. Weather Underground (2017). The Weather Company. Retrieved from:

https://www.wunderground.com/history/airport/OTBD/2016/6/30/DailyHistory.html?req

_city=&req_state=&req_statename=&reqdb.zip=&reqdb.magic=&reqdb.wmo=

[Accessed Sep. 15, 2017] 191

205. Welle, B. (2009). Energy Simulation and BIM. CEE214 - Modeling Product, Processes,

and Organizations in the AEC Industry. California: Stanford.

206. Wilensky, U. (1999). NetLogo. Evanston, Illinois: Center for Connected Learning and

Computer-Based Modeling, Northwestern University. Retrieved from

http://ccl.northwestern.edu/netlogo/ [Accessed Oct 12, 2017]

207. Williamson, T., Soebarto, V., & Radford, A. (2010). Comfort and Energy Use in Five

Australian Award-winning Houses: Regulated, Measured and Perceived. Building

Research and Information, 38(5), 509-529.

208. Wogan, D., Pradhan, S. & Albardi, S., (2017). GCC Energy System Overview – 2017,

Report. KAPSARC.

209. Wooldridge, M. (2009). An Introduction to Multiagent Systems (2nd Ed.). UK: John Wiley

& Sons.

210. World Bank Group. (2018). The World Development Indicators. [Online]

Available at:

http://databank.worldbank.org/data/reports.aspx?source=2&series=EG.USE.PCAP.KG.O

E&country= [Accessed 2 November 2017].

211. Yan, D., O'Brien, W., Hong, T., Feng, X., Gunay, B. H., Tahmasebi, F., & Mahdavi, A.

(2015). Occupant Behavior Modeling for Building Performance Simulation: Current State

and Future Challenges. Energy and Buildings, 264-278. doi:10.1016/j.enbuild.2015.08.032

212. Yang, K., & El-Haik, B. S. (2009). Design for Six Sigma: A Roadmap for Product

Development (2nd ed.). USA: McGraw-Hill Companies.

213. Yang, R., & Wang, L. (2013). Development of Multi-agent System for Building Energy

and Comfort Management Based on Occupant Behaviors. Energy and Buildings, 56, 1-7.

214. Yin, R. K. (2012). Applications of Case Study Research (3rd ed.). Los Angeles: Sage

Publishers. 192

215. Yu, Z., Fung, B. C., Haghighat, F., Yoshino, H., & Morofsky, E. (2011). A Systematic

Procedure to Study the Influence of Occupant Behavior on Building Energy Consumption.

Energy and Buildings, 1409-1417. doi:10.1016/j.enbuild.2011.02.002

216. Zhang, T., Siebers, P.-O., & Aickelin, U. (2011). Modelling Electricity Consumption in

Office Buildings: An Agent-Based Approach. Energy and Buildings, 43(10), 2882-2892.

193

APPENDICES

Appendix A- Questionnaire for Other Industries

ACCOUNTING FOR END-USER VALUES AND PREFERENCES IN THE AUTOMOBILE

INDUSTRY

Values: thermal comfort, lighting and airflow (ventilation)

Preferences: temperature preference, lighting preference etc.

End users: automobile users/occupants

Background Information

Name:

Organization:

Role within the organization:

Phone:

Main Questions (focusing on conditioned environments- cabin comfort)

1. To what extent are end-user values and preferences integrated into the design of heating, ventilation,

cooling and lighting in automobile cabins?

☐ Never ☐ Rarely ☐ Sometimes ☐ Usually ☐ Always

2. How are end-user values and preferences (with regard to temperature, airflow/ventilation, lighting,

relative humidity, etc.) in conditioned spaces (such as automobile cabins) captured?

a) Is there a formal methodology to account for end-user values in automobile cabins? Yes/No

b) If so, what is the formal methodology? (e.g. Six sigma)

c) Is this methodology supported by a software tool? Yes/No

d) What software tool is used?

3. How are these values and preferences integrated into the design of automobile?

194

4. What are the key considerations in the design, control and operation of indoor environments in

automobile for human comfort (in relation to temperature, lighting and airflow)?

5. What level of indoor environment control or flexibility is available to people in automobile cabins

with regard to the following?

Full A lot Some A little None

Temperature ☐ ☐ ☐ ☐ ☐

Airflow ☐ ☐ ☐ ☐ ☐

Lighting ☐ ☐ ☐ ☐ ☐

6. To what extent are occupants able to adjust indoor environments by (e.g. by opening windows,

adjusting the thermostat, dimming lights, or varying airflow) to improve their comfort?

7. In what way does energy efficiency influence the conditioning of automobile cabins for occupant

comfort?

8. Does maximizing occupant comfort conflict with minimizing energy consumption? Yes/No

If yes, how do you strike a balance?

9. What standards do you rely on that address end-user values/preferences in the design of HVAC

systems in automobile cabins?

a) Are you able to provide access to any documents for the design of automobile cabins? Yes/No

10. How do you measure the extent to which occupant values and preferences have been satisfied (e.g.

satisfaction surveys)?

195

Appendix B- Survey Questions

VET Questionnaire

VET Office- U.S. 6 How important is it for you to be able to control your thermal environment? 1 Background Survey  Not at all important (1) Thank you for agreeing to take part in this survey.  Very unimportant (2) The purpose of this study is to obtain feedback on  Somewhat unimportant (3) occupant comfort in conditioned spaces and identify  Somewhat important (4) other factors that affect comfort in office  Very important (5) environments.  Extremely important (6)

Part 1 7 Are you within 15 feet of an exterior wall? 2 Thermal Questionnaire: How comfortable are you  Yes (2) with the temperature in your office presently?  No (1)  Very comfortable (5)  Comfortable (4) 8 Are you within 15 feet of a window?  Neutral (3)  Yes (2)  Uncomfortable (2)  No (1)  Very uncomfortable (1) 9 What is your office layout? 3 How do you feel about the temperature in your  Open plan office (1) office presently? I feel it is  Cubicle with partitions (2)  Hot (7)  Private office (3)  Warm (6)  Shared office (4)  Slightly warm (5)  Other (88) ______ Neutral (4)  Slightly cool (3) 10 What kind of chair is in your office?  Cool (2)  Net/metal/wooden chair (1)  Cold (1)  Standard office chair (2)  Executive chair (3) 4 On the basis of your personal preference, would  Other (88) ______you consider the room temperature acceptable or unacceptable? 11 If you experience thermal discomfort (temperature  Acceptable (2) and humidity) in your work space, which of the  Unacceptable (1) following best describes when it occurs?  Mornings (1) 5 How do you feel about the airflow now? I feel it is  Afternoons (2)  Far too little (1)  Weekends (3)  Too little (2)  Holidays (4)  About right (3)  Monday mornings (5)  Too much (4)  Always (6)  Far too much (5)  Other (88) ______

12 If you experience thermal discomfort (temperature and humidity) in your work space at the

196

moment, which of the following best describes 16 What was your activity in the last hour? (Check it? (Select up to three) all that apply)  Too much air movement (1)  Reclining (1)  Too little air movement (2)  Seated quietly (e.g. writing) (2)  Incoming sunlight heats up space (3)  Light activity sitting (e.g. filing seated) (3)  Heat from office equipment (4)  Standing relaxed (4)  Drafty windows (5)  Light activity standing (e.g. walking about) (5)  My work space is hotter than other areas (6)  Medium activity standing (e.g. cleaning) (6)  My work space is colder than other areas (7)  High activity (e.g. heavy lifting) (7)  Walls and floors are hot (8)  Other (88) ______ Walls and floors are cold (9)  Thermostat is inaccessible or controlled by 17 What was your most recent activity? others (10)  Reclining (1)  Other (88) ______ Seated quietly (e.g. writing) (2)  Light activity sitting (e.g. filing seated) (3) 13 What kind of clothing are you wearing (top)?  Standing relaxed (4)  Sleeveless top (1)  Light activity standing (e.g. walking about) (5)  Sleeveless underwear top (2)  Medium activity standing (e.g. cleaning) (6)  Longsleeve underwear top (3)  High activity (e.g. heavy lifting) (7)  Short sleeve shirt/tshirt/blouse (4)  Other (88) ______ Long-sleeve shirt/t-shirt/blouse (5)  Sweater (6) Part 2  Short dress (7) 18 Personal Microclimatic Control: How often do  Long dress (8) you open and close your windows?  Suit jacket (9)  Never (1)  Thick jacket (10)  Rarely (2)  None (0)  Sometimes (3)  Other (88) ______ Most of the time (4)  Always (5) 14 What kind of clothing are you wearing (bottom)?  I don't have access to a window (0)  Shorts/short skirt (1)  Long skirt (2) 19 How often do you open and close the window  Long underwear bottoms (3) shading device (e.g. window blinds)?  Thin trousers (4)  Never (1)  Thick trousers (5)  Rarely (2)  Sweatpants (6)  Sometimes (3)  None (0)  Most of the time (4)  Other (88) ______ Always (5)  I do not have a shading device (0) 15 What kind of footwear do you have on?  Sandals/shoes (1) 20 How often do you leave your office door open to  Slippers (2) improve your comfort?  Boots (3)  Never (1)  None (0)  Rarely (2)  Other (88) ______ Sometimes (3)  Most of the time (4)  Always (5)  I do not have a personal door (0)

197

21 How often do you leave the main door open to 27 How comfortable are you with noise levels in your improve your comfort? office?  Never (1)  Very comfortable (5)  Rarely (2)  Comfortable (4)  Sometimes (3)  Neutral (3)  Most of the time (4)  Uncomfortable (2)  Always (5)  Very uncomfortable (1)

22 How often do you regulate the thermostat to make 28 Do you feel the noise increases or decreases your the room warmer or cooler? productivity at work?  Never (1)  Increases productivity (2)  Rarely (2)  Decreases productivity (1)  Sometimes (3)  No effect (0)  Most of the time (4)  Always (5) 29 Do you feel the current indoor environmental  I cannot control the thermostat (0) condition at work affects your perceived health?  Yes (2) 23 How often do you use a portable heater?  No (1)  Never (1)  I don't know (0)  Rarely (2)  Sometimes (3) 30 How comfortable are you with lighting levels in  Most of the time (4) your office?  Always (5)  Very comfortable (5)  Comfortable (4) 24 How often do you use a portable fan?  Neutral (3)  Never (1)  Uncomfortable (2)  Rarely (2)  Very uncomfortable (1)  Sometimes (3)  Most of the time (4) 31 During the daytime, what kind of lighting do you  Always (5) prefer to use?  Natural light (1) 25 How comfortable are you with the air quality in  Artificial light (2) your office?  Very comfortable (5) 32 Does the use of natural light increase or decrease  Comfortable (4) your productivity at work?  Neutral (3)  Increases productivity (2)  Uncomfortable (2)  Decreases productivity (1)  Very uncomfortable (1)  No effect (0)

26 Do you feel the indoor air quality increases or 33 Does the use of artificial light increase or decrease decreases your productivity at work? your productivity at work?  Increases productivity (2)  Increases productivity (2)  Decreases productivity (1)  Decreases productivity (1)  No effect (0)  No effect (0)

198

34 How would you feel if there were no windows on Part 3 the wall? 38 Background Information: What is your age?  Very comfortable (5)  18-24 years (1)  Comfortable (4)  25-34 years (2)  Indifferent (3)  35-44 years (3)  Uncomfortable (2)  45-54 years (4)  Very uncomfortable (1)  55-64 years (5)  65-74 years (6) 35 Will the absence of windows increase or decrease  75 years and over (7) your productivity at work?  Increase productivity (2) 39 What is your gender?  Decrease productivity (1)  Male (1)  No effect (0)  Female (2)

36 How important is energy cost savings to you? 40 Please enter your email, and office room number  Not at all important (1) Email (3)  Very unimportant (2) Office Room Number (4)  Neither important nor unimportant (3) Average number of hours a week spent in the office  Very important (4) (5)  Extremely important (5) 41 Would you like a copy of your response sent to 37 How important is environmental protection to you by email? you?  Yes (1)  Not at all important (1)  No (2)  Very unimportant (2) If No Is Selected, Then Skip To End of Survey  Neither important nor unimportant (3)  Very important (4)  Extremely important (5)

199

PMA Questionnaire

PMA Office- U.S. 5 Humidity: Which of the following did you do to improve your comfort? 1 Hello, today is Wednesday, November 2nd  Turned on portable dehumidifier because the room was too humid (1) 2 Temperature: I feel it is  Adjusted portable dehumidifier (2)  Hot (7)  Turned on portable humidifier because the room  Warm (6) was too dry (3)  Slightly warm (5)  Adjusted portable humidifier (4)  Neutral (4)  None of the above (0)  Slightly cool (3)  Cool (2) 6 Thermal comfort (overall evaluation of temperature  Cold (1) and humidity). What is your current satisfaction level with thermal comfort in your office? 3 Temperature: Which of the following actions did  Very unsatisfied (1) you take (or do you intend to take) to improve your  Unsatisfied (2) comfort?  Moderately unsatisfied (3)  Adjusted thermostat to make room warmer (1)  Moderately satisfied (4)  Adjusted thermostat to make room cooler (2)  Satisfied (5)  Put on more layers of clothing (3)  Very satisfied (6)  Took off layers of clothing (4)  Turned on portable heater (5) 7 Airflow: I feel it is  Adjusted portable heater (6)  Far too much (7)  Turned off portable heater (7)  Too much (6)  Turned on portable fan (8)  A bit too much (5)  Adjusted portable fan (9)  About right (4)  Turned off portable fan (10)  A bit too little (3)  Complained to the facility manager (11)  Too little (2)  I did not do anything (0)  Far too little (1)  Other (88) ______8 Airflow: Which of the following did you do to 4 Humidity: I feel it is improve your comfort?  Too humid (7)  Opened window to increase airflow (1)  Humid (6)  Closed window to reduce airflow (2)  Slightly humid (5)  Opened door to increase airflow (3)  About right (4)  Closed door to reduce airflow (4)  Slightly dry (3)  None of the above (0)  Dry (2)  Too dry (1) 9 Indoor air quality: What is your current satisfaction level with air quality in your office?  Very unsatisfied (1)  Unsatisfied (2)  Moderately unsatisfied (3)  Moderately satisfied (4)  Satisfied (5)  Very satisfied (6)

200

10 Indoor lighting I feel it is 15 How do you feel your personal productivity is  Too dim (1) increased or decreased by the current indoor  Dim (2) environment conditions at work?  Slightly dim (3)  Increased (2)  Neutral (4)  Decreased (1)  Slightly bright (5)  No effect (0)  Bright (6)  I do not know (88)  Too bright (7) 16 What is your current activity? 11 Indoor lighting: What is your current satisfaction  Reclining (1) level with lighting in your office?  Seated quietly (e.g. reading or writing) (2)  Very unsatisfied (1)  Light activity sitting (e.g. filing seated) (3)  Unsatisfied (2)  Standing relaxed (4)  Moderately unsatisfied (3)  Light activity standing (e.g. walking about) (5)  Moderately satisfied (4)  Medium activity standing (e.g. cleaning) (6)  Satisfied (5)  High activity (e.g. heavy lifting) (7)  Very satisfied (6)  Other (88) ______

12 Indoor lighting: Which of the following did you 17 What kind of clothing are you wearing (top)? do to improve your visual comfort?  Sleeveless top (1)  Turned on room lights (1)  Long-sleeve underwear top (2)  Turned off room lights (2)  Short-sleeve shirt/t-shirt/blouse (3)  Turned on personal lighting device (3)  long-sleeve shirt/t-shirt/blouse (4)  Turned off personal lighting device (4)  Sweater (5)  Opened shading device to let more light in (5)  Short dress (6)  Closed shading device to block out the sunlight  Long dress (7) (6)  Suit jacket (8)  Complained to the facility manager (7)  Thick jacket (9)  None of the above (0)  Other (88) ______

13 Did you do anything else to improve your 18 What kind of clothing are you wearing (bottom)? comfort? If yes, what did you do?  Shorts/short skirt (1)  Long skirt (2)  Thin trousers (3) 14 How do you feel your perceived health is  Thick trousers (4) increased or decreased by the current indoor  Sweatpants (5) environment conditions at work?  Other (88) ______ Increased (2)  Decreased (1) 19 What kind of footwear do you have on?  No effect (0)  Sandals/shoes (1)  I do not know (88)  Slippers (2)  Boots (3)  None (0)  Other (88) ______

201

20 What electronic appliance are you using presently?  Computer (1)  Printer/copier (2)  Portable heater (3)  Portable fan (4)  Portable humidifier/dehumidifier (5)  Personal light (6)  Other (88) ______

21 Would you like to provide additional feedback or comments?  Yes (1)  No (2)

Answer If Would you like to provide more feedback or comments? Yes Is Selected 22 Enter feedback or comments

23 Would you like your response report sent to you by email?  Yes (1)  No (2) If No Is Selected, Then Skip To End of Survey

Answer If Would you like your response report sent to you by email? Yes Is Selected 24 Enter your email address

202

Appendix C- Sensor Specifications

Sensors and equipment for energy monitoring

SPECIFICATIONS SENSOR PARAMETER UNIT RANGE ACCURACY IC Sentinel Temperature °C ±5% Relative humidity % ±10% Carbon dioxide ppm 0-10,000 Airborne particulates µm 0-3,000,000 Counting P/CF efficiency 50% ±20%, 100% ±10% Illuminance Lux - - HOBO UX90 Window state 1/0 - - Speck Airborne particulates 0.5-3µ HOBO Temperature °C 0° to 50° ±0.21°C from 0° MX1102 to 50°C Relative humidity % 1% to 70% ±2% from 20% to 80% Carbon dioxide ppm 0 to 5,000 ppm ±50 ppm HOBO U12 Temperature °C -20 to 70 ±0.35 Relative humidity % 5 to 95 ±2.5 LI-210R Illuminance Lux - - HOBO UX120 Data logger - - - E50B2 Power Power/energy varies 90-600VAC ANSI 12.20 & Energy 0.5% accuracy Meter Sensor HOBO 4- Power logger - - - Channel Pulse Data Logger T-ACT-0750- AC current Amps 5-250 ±0.75% from 1% 020- Split core to 120 % rated sensor primary current HOBO CTV AC current Amps 5-50 ±4.5% Bert 110M Plug load power Up to 15 Amps 5% up to 15 Amps

203

Appendix D- Instrumentation Plans

Instrumentation Plan for Qatar Office Building

204

Instrumentation Plan for U.S. Buildings

Measuring Instrument Measurement Parameter Unit Residential Office Frequency*

Temperature °C 15 minutes

1 Automated Logic Relative HOBO temperature, ZS Pro sensors % 15 minutes humidity RH and CO2 sensor for temperature, RH, and CO2 Carbon dioxide Ppm 15 minutes Light intensity1 Lux Licor light sensor 15 minutes Particle count1 µ/m3 Speck 1 hour

Indoor Measurements Indoor Plug loads kW 15 minutes E50B2 power and WattNode WNC- Lighting power kW 15 minutes energy meter3 3Y-208-BN HVAC power kW 15 minutes Utility bills and Gas2 Therms Utility bills 15 minutes gas meter

Energy Consumption Window state - Onset UX 90 - open/close state Use of portable - PMA and power measurement - heater/fan Adjust lighting - PMA -

Adjust PMA - thermostat Adjust shading - PMA - devices Adjust clothing - PMA -

Occupant Behavior Occupant *Sensing frequency for the office building was configured for every minute or hour 1IC Sentinel indoor environment sensors are installed in both buildings and can measure all the indoor parameters 2Gas is measured in Therms from the utility bills but can be converted to kWh (1 Therm=29.3001 kWh) 3Current sensors to sub-meter end-use categories

205

Instrumentation Plan for U.S. Residential Building

Variable to be Frequency of Unit Instrument measured measurements

Degrees F 15 minutes Temperature Wireless sensors

Relative Humidity % 15 minutes

Carbon Dioxide ppm Wired sensors 15 minutes

Lighting Intensity Lux/footcandles Licor Light Sensor 15 minutes Receptacle Power/ WattNode WNC-3Y-208-BN at panel Plug Loads kWh #2 Split core CT 15 minutes WattNode WNC-3Y-208-BN at panel Lighting Power kWh #3 Split core CT 15 minutes Current sensors wired to existing AHU- AHU-1 kW 1 Automated Logic control panels 15 minutes Current sensors wired to existing AHU- AHU-2 kW 2 Automated Logic control panels 15 minutes

Variable Circuit Service System Name No Area Qty Instrument Virtual Meter West heat West zone- pump two rooms + Power Direct (WHP) 2,4 family room 1 meter/TED* measurement East zone- living room, East heat masters B/R Direct pump (EHP) 24,26 suite 1 Power meter/TED measurement Basement Power meter Electric Heating Wall heater Onset Mini CTVs Direct and Cooling (BWH) 13,15 Basement 1 at the panelboard measurement Multiple Measure lighting Interior (Mixed at circuits, Onset Lighting only= Lighting (LGT) circuits) Interior 6 Mini CTVs LGT – PLC Plug Load1 at Plug load at Circuits mixed end use Power plug Direct with Lighting (PLC) Multiple Various TBD meters measurement Plug1 and Interior and PLG= Total – Miscellaneous exterior (lighting + WHP Loads (PLG) Multiple Entire house - Virtual +EHP+BWH)

Total Electric Direct Energy Use Total Entire house 1 Power meter/TED measurement Entire house Monthly Gas except for Meter Gas the readings/pulse Gas energy use- Gas Heating fireplace N/A basement - Meter hot water use Monthly Gas Meter readings/pulse Hot Water Heater N/A Entire house 1 Meter 206

List of equipment installed- State College

S/N Equipment Qty 1 IC Sentinel sensors 4 2 Wifi Kits 4 3 Telaire sensors 3 4 Licor light sensor 1 5 Data loggers (4 channel) 5 6 Data loggers (temperature and Relative humidity) 6 7 Speck sensor 1 8 CT Coils (Onset) 11 9 State logger 8 10 Bert plugs 8 11 CT coils (250A) 2 12 Power meter 1 13 Power logger 1

Instrumentation Plan US Residential Building (First Floor)

207

Data Collection Plan State College

S/N Data Collection Sensor Measures Location Unit Data Data Coll Device Interval Frequency 1 HOBO data CO2-1 T, RH, Basement F, %, 15 mins 2 weeks logger (TRH1) + CO2 ppm CO2 TRH2 + CO2 CO2-2 T, RH, Family room F, %, “ “ CO2 ppm TRH3 + CO2 CO2-3 T, RH, Master’s BR F, %, “ “ CO2 ppm TRH4 + LI-1 T, RH, LI Near the office F, %, “ “ Illuminance by the main Lux entrance TRH5 T, RH Girls BR F, % “ “ TRH6 T, RH Boys BR F, % “ “ 2 AQM1 (Speck) Particles Basement Ppm 1 minute Cloud 3 ST1 Open/close Dining area Event 2 Weeks state ST2 “ Family room “ “ ST3 “ Living “ “ room/office ST4 “ Living room “ “ ST5 “ Masters BR “ “ ST6 “ Masters bath “ “ 4 C-01 CTV-1 Current Bsmt wall Amp 15 mins 2 Weeks heater* CTV-2 Bsmt wall Amp “ “ heater* CTV-3 Basement lights Amp “ “ +plugs 5 C-02 CTV-4 Unfinished Amp “ “ basement CTV-5 Family room Amp “ “ lights+outside lights CTV-6 Front sitting Amp “ “ room light+plug 6 C-03 CTV-7 Kitchen/foyer Amp “ “ lights CTV-8 All bedroom Amp “ “ lights CTV-9 Back bedroom Amp “ “ 7 C-04 CTV- East heat pump Amp “ “ 10 CTV- West heat pump Amp “ “ 11

208

8 My IC Sentinel 1246 T, RH, Basement F, %, 30 Cloud CO2, IL, ppm, min/1min Particles Lux, ppm 10 Bertbrain B1 Power Bsmt Wh 1 hr 2 Weeks Dehumidifier B2 Bsmt Lamp 1 hr “ B3 Bsmt HW heater 1 hr “ B4 Bsmt By the TV 1 hr “ B5 Masters BR 1 hr “ B6 Masters BR by 1 hr “ window 11 Power logger MPM Power Total power kWh 15 mins 2 Weeks consumption 12 Gas (fireplace) Monthly

209

Appendix E- Background VET Results

Occupant Values and Background information on VET

Overall Analysis U.S. U.S. Qatar Qatar Breakdown of Selected Questions Office Office Office Office response (No) (%) (No) (%) How important is it for you to be able to Not at all control your thermal environment? important 0 0 0 0.0 Very unimportant 3 17.6 0 0.0 Somewhat unimportant 1 5.9 1 8.3 Somewhat important 7 41.2 1 8.3 Very important 3 17.6 7 58.3 Extremely important 3 17.6 3 25.0 How often do you open and close your Never windows? 9 52.9 2 16.7 Rarely 1 5.9 5 41.7 Sometimes 1 5.9 3 25.0 Most of the time 0 0.0 0 0.0 Always 0 0.0 0 0.0 I don't have access to a window 6 35.3 2 16.7 How often do you open and close the window shading device (e.g. window Never blinds)? 5 29.4 0 0.0 Rarely 4 23.5 3 25.0 Sometimes 0 0.0 2 16.7 Most of the time 1 5.9 1 8.3 Always 1 5.9 1 8.3 I don't have a shading device 6 35.3 5 41.7 How often do you leave your office door Never open to improve your comfort? 1 5.9 0 0.0 Rarely 1 5.9 4 33.3 Sometimes 3 17.6 5 41.7 Most of the time 5 29.4 3 25.0 Always 6 35.3 0 0.0 I don't have a personal door 1 5.9 0 0.0 How often do you leave the main door open Never to improve your comfort? 7 41.2 9 81.8 Rarely 1 5.9 1 9.1 Sometimes 3 17.6 0 0.0 210

Most of the time 4 23.5 1 9.1 Always 1 5.9 0 0.0 How often do you regulate the thermostat to Never make the room warmer or cooler? 4 23.5 2 16.7 Rarely 2 11.8 4 33.3 Sometimes 0 0.0 1 8.3 Most of the time 2 11.8 0 0.0 Always 0 0.0 0 0.0 I cannot control the thermostat 9 52.9 5 41.7 How often do you use a portable heater Never (U.S.)/adjust the AC unit (Q)? 6 35.3 6 50.0 Rarely 2 11.8 3 25.0 Sometimes 6 35.3 3 25.0 Most of the time 1 5.9 0 0.0 Always 2 11.8 0 0.0 How often do you use a portable fan? Never 9 52.9 9 75.0 Rarely 2 11.8 1 8.3 Sometimes 5 29.4 2 16.7 Most of the time 1 5.9 0 0.0 Always 0 0.0 0 0.0 How comfortable are you with the air quality Very in your office? uncomfortable 0 0.0 0 0.0 Uncomfortable 1 5.9 2 16.7 Neutral 8 47.1 6 50.0 Comfortable 6 35.3 3 25.0 Very comfortable 0 0.0 1 8.3 Do you feel the indoor air quality increases Decreases or decreases your productivity at work? productivity 2 14.3 3 25.0 Increases productivity 3 21.4 3 21.4 No effect 9 64.3 6 42.9 How comfortable are you with noise levels Very in your office? uncomfortable 0 0.0 0 0.0 Uncomfortable 2 13.3 1 8.3 Neutral 2 13.3 6 50.0 Comfortable 9 60.0 4 33.3 Very comfortable 2 13.3 1 8.3 Do you feel the noise increases or decreases Decreases your productivity at work? productivity 4 28.6 9 75.0 Increases productivity 1 7.1 0 0.0 No effect 9 64.3 3 25.0

211

Do you feel the current indoor environmental condition at work affects your perceived No health? 6 40.0 4 33.3 Yes 5 33.3 1 8.3 I don't know 4 26.7 7 58.3 How comfortable are you with lighting Very levels in your office? uncomfortable 1 6.7 1 8.3 Uncomfortable 3 20.0 3 25.0 Neutral 1 6.7 2 16.7 Comfortable 10 66.7 5 41.7 Very comfortable 0 0.0 1 8.3 During the daytime, what kind of lighting do Natural light you prefer to use? 9 60.0 8 66.7 Artificial light 6 40.0 4 33.3 Does the use of natural light increase or Decreases decrease your productivity at work? productivity 2 13.3 1 8.3 Increases productivity 9 60.0 9 75.0 No effect 4 26.7 2 16.7 Does the use of artificial light increase or Decreases decrease your productivity at work? productivity 3 20.0 6 50.0 Increases productivity 3 20.0 1 8.3 No effect 9 60.0 5 41.7 How would you feel if there were no Very windows on the wall? uncomfortable 8 53.3 8 66.7 Uncomfortable 3 20.0 4 33.3 Neutral 3 20.0 0 0.0 Comfortable 1 6.7 0 0.0 Very comfortable 0 0.0 0 0.0 Will the absence of windows increase or Decreases decrease your productivity at work? productivity 10 66.7 11 91.7 Increases productivity 1 6.7 1 8.3 No effect 4 26.7 0 0.0 How important is energy cost savings to Not at all you? important 0 0.0 1 8.3 Very unimportant 4 26.7 0 0.0 Neither important nor unimportant 3 20.0 7 58.3 Very important 6 40.0 1 8.3 Extremely important 2 13.3 3 25.0 How important is environmental protection Not at all to you? important 0 0.0 0 0.0 Very unimportant 2 13.3 0 0.0 212

Neither important nor unimportant 0 0.0 1 8.3 Very important 8 53.3 6 50.0 Extremely important 5 33.3 5 41.7 Background Information Age Distribution 18-24 years 1 6.7 0 0.0 25-34 years 6 40.0 0 0.0 35-44 years 1 6.7 5 41.7 45-54 years 2 13.3 6 50.0 55-64 years 1 6.7 1 8.3 65-74 years 4 26.7 0 0.0 75 years and over 0 0.0 0 0.0 12 Gender Distribution Male 4 20.0 5 41.7 Female 13 80.0 7 58.3

213

Appendix F- Occupant Profile Information

Occupant Profile Information and Distribution- Adapted from Case Studies, Murtagh et al. (2013), and Azar (2014) OccNo OccType OccGender OccAge OccEUI OccVariability PrefTemp PrefLux 1 Active Female 23 9 0.6 23 500 2 Active Female 60 10 0.6 24 500 3 Active Male 55 11 0.6 22 500 4 Active Female 35 9 0.6 23 500 5 Active Female 58 8 0.6 24 500 6 Active Female 49 10 0.6 24 500 7 Active Male 55 9 0.6 21 500 8 Active Female 41 9 0.6 24 500 9 Passive Female 28 4 0.2 25 250 10 Passive Female 25 4 0.2 26 250 11 Passive Male 28 5 0.2 22 250 12 Passive Male 35 4 0.2 21 250 13 Passive Male 25 3 0.2 25 250 14 Passive Male 58 5 0.2 26 250 15 Passive Female 24 3 0.2 22 250 16 Passive Female 48 4 0.2 21 250 *Initially, all occupant are assumed to be active, a percentage changed due to an educational intervention.

214

Appendix G- Evaluation Questionnaire

Evaluation Questions

Thank you for your time and for your feedback. There are 10 questions in this evaluation and you can provide additional comments at the end. Background information  Position/Role ______ Years of experience ______

Questions 1. How useful is the approach in modeling occupant values (thermal and visual comfort) in buildings? a. Extremely useful b. Very useful c. Moderately useful d. Slightly useful e. Not useful at all Please provide reasons here ______

2. How realistic are the testing scenarios? a. Extremely realistic b. Very realistic c. Moderately realistic d. Slightly realistic e. Not realistic at all Please provide reasons and other suggestions here ______

3. How well do you feel this approach can integrate individual occupant values with building systems operation? a. Extremely well b. Very well c. Moderately well d. Slightly well e. Not well at all Please provide reasons here ______

4. How do you rate the performance of the agent-based modeling approach in relation to the following features?

215

Feature Extremely Very Moderately Slightly Not N/A effective effective effective effective effective at all Ease of use

Visualization capabilities Usefulness of feedback Meaningfulness of results

5. How do you rate the importance of notifying the following people of changes to the indoor environmental conditions? Role Extremely Very Moderately Slightly Not N/A important important important important important at all Building owner Facilities manager Building occupant Other occupants

6. How important is it for the occupant feedback dashboard to have the following features? Feature Extremely Very Moderately Slightly Not N/A important important important important important at all Ease of use Visualization capabilities for occupant Visualization capabilities for facilities manager Notification of facilities manager or building operator Availability of occupant feedback to facilities manager 216

7. What are the potential benefits of this approach to your organization for improving occupant comfort and reducing energy consumption indoors?

8. What improvements do you recommend for the agent-based modeling approach?

9. What practical considerations are necessary for this approach to be used in actual office buildings?

10. Please provide additional comments, feedback, and recommendations about this approach

Optional Question Will you like to be contacted for additional feedback? Yes/No If yes, please provide the following information Name: ______Email: ______Phone: ______

217

Appendix H- IRB Exemption

218

VITA

Yewande S. Abraham

EDUCATION The Pennsylvania State University, University Park, Pennsylvania, USA May 2018 Ph.D. Architectural Engineering, Construction Option Advisors- Prof. Chimay J. Anumba & Dr. Somayeh Asadi Cardiff University, Wales, United Kingdom July 2010 M.Eng., B.Eng., First Class Honors, Integrated Bachelors and Masters in Civil Engineering

SELECTED PROFESSIONAL AND RESEARCH EXPERIENCE The Pennsylvania State University, Department of Architectural Engineering 2014- 2017 Graduate Research Assistant Penn State at The Navy Yard, Philadelphia, PA 2017 Energy Innovation Leadership Experience Intern- Global Sustainability Practice Qatar Environment and Energy Research Institute, Qatar Foundation, Qatar 2016 Consultant Kinsley Construction, Penn State HFS Warehouse Expansion, University Park, PA 2015 Construction Management Intern Office of Physical Plant, The Pennsylvania State University, University Park, PA 2014 Engineering Services Intern Brunel Engineering and Consulting, Abuja, Nigeria 2012 Civil Engineer

SELECTED PUBLICATIONS  Abraham, Yewande S., Anumba, Chimay J. and Asadi, Somayeh. (2018). “Exploring Agent-Based Modeling Approaches for Human-Centered Energy Consumption Prediction.” American Society of Civil Engineers (ASCE), Baton Rouge, Louisiana  Abraham, Yewande S., Zhao, Zhidan, Anumba, Chimay J., Asadi, Somayeh. (2017). “Implementation of a Preference Monitoring Application for Office Building Occupants.” LC3, Crete, Greece  Abraham, Yewande S., Anumba, Chimay J. and Asadi, Somayeh (2017). “Data Sensing Approaches to Monitoring Building Energy Use and Occupant Behavior.” American Society of Civil Engineers (ASCE), Seattle, Washington  Abraham, Yewande and Anumba, Chimay. (2016). “Development of a Preference Monitoring Application for Office Building Occupants.” In 13th Annual College of Engineering Research Symposium, Paper presented at College of Engineering Research Symposium, University Park.  Anumba, C. J., Abraham, Y., and Kaneda, D. (2015). “Net-Zero Carbon Buildings: A US Perspective.” (C. Wing, Ed.) Zero Carbon Journal, Volume 4, 22-29  Abraham Y., Amasyali K., El-Gohary N. and Anumba C. J. (2015): “The Need for Human-Centered and Value-Sensitive improvement of Building Energy Efficiency,” 7th International Conference on Sustainable Urban Design in the Built Environment (SudBE): ICT and Energy Management in Buildings, Reading UK, pages 1-8