International Construction Specialty Conference of the Canadian Society for Civil Engineering (ICSC) (5th : 2015)

Building a sustainable occupant's performance based model for institutional buildings Salem, Dalia; Elwakil, Emad; Kandil, Amr Jun 30, 2015

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5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   BUILDING A SUSTAINABLE OCCUPANT’S PERFORMANCE BASED MODEL FOR INSTITUTIONAL BUILDINGS Dalia Salem1,3, Emad Elwakil1, and Amr Kandil2  1 Building Construction management, Purdue University, USA. 2 Construction Engineering Management, Purdue University, USA. 3 dsalem@purdue.edu Abstract: The Sustainable buildings main objectives are to reduce, or avoid, depletion of resources like energy, water, and materials; prevent environmental degradation caused by facilities during the life cycle of the building. Lighting is one of the major energy consumption in institutional buildings. At 2012, the commercial sector, which includes commercial and institutional buildings, and Public Street and highway lighting, consumed about 274 billion kWh for lighting or about 7 % of the USA consumption. Most of the research works have focused predominantly on the environmental and physical factors and have neglected the daily activities of the occupants. This study examines the effects of environmental, physical, and daily activities on occupants’ performance in the institutional buildings as well as develops a model to predict the occupants’ performance using Regression analysis technique. The data was collected from the institutional buildings occupants and building facility experts using questionnaire. The model has been validated with 92 % Average Validity Percent (AVP) and R square of 0.83 which is a satisfactory result. The developed research /model benefits both architects and practitioners to choose the appropriate workplace design due to the occupants’ preferences to enhance performance, and energy efficiency. 1 INTRODUCTION Buildings are one of the major energy consumers in the U.S. as shown in Fig 1. Both commercial and residential buildings account for 42% of the national U.S. energy consumption. The majority of commercial buildings energy consumption is attributed to lighting (25%), space heating and cooling (25%), and ventilation (7%) (Azar and Menassa 2011a). Lighting and HVAC energy used in buildings are considered the main consumers of the total buildings energy consumption. Nearly lighting energy used is responsible for 23%, Heating, ventilation and cooling accounting for 38% (Guo et al. 2010). In a recent study, in US commercial building, 25-40% of the total electricity energy consumption is from electrical lighting (Ihm et. al., 2009).       303-1    Figure 1: Buildings Share of U.S. Primary Energy Consumption (2006) (“Buildings Overview | Center for Climate and Energy Solutions” 2015) Figure 2: 2006 U.S. Primary Energy End-Use Splits (“Buildings Overview | Center for Climate and Energy Solutions” 2015) Building professionals’ significant role is how to reduce energy consumption as well as considerably maintain comfort to the occupants. Artificial lighting systems are considered as a major consumer of energy in buildings and contribute significantly to building cooling load. Daylighting has two fold; contributing in determine the overall environmental quality in buildings as well as saving energy (Alashwal and Budaiwi 2011). Before 1940s, daylighting was considered the main light source in buildings design.  Recently, in sustainable buildings, daylighting is considered as energy and environmental aspect  (Edwards and Torcellini 2002). In case of office lighting, the switching patterns along with the outside conditions are at the core of investigation from the occupants’ behavior point of view. One of the studies has culminated into the fact that as much as 40% energy conservation can be realized if natural light is relied upon compared to the artificial one (Bourgeois, Reinhart, and Macdonald 2006).  Therefore, the scope of the present study is to investigate the lighting preferences in commercial buildings with focus on the institutional buildings and develop a framework for predicting occupant’s performance. 2 RESEARCH OBJECTIVES The objectives of the present study is to build a sustainable occupant’s performance based model for institutional buildings which can be summarized as follows:  i. Investigate and identify the factors affect occupants usage of lighting. ii. Data collection from a real workplace iii. Determine the significant factors that mostly contribute to the Lighting intensity in the workplace. iv. Determine the factors that affect the occupants’ performance v. Develop a model / framework based on these factors. vi. Validate the developed model / framework 3 BACKGROUND The most significant factors that influence the energy and indoor environmental performances of buildings are outdoor/indoor climate, building characteristics, and occupant behavior. The most important factor is human behavior, followed by building design. Indeed, there is often an obvious discrepancy between real 303-2 total energy use in buildings and what is predicted. The reasons for this gap are a general need to understand the role of human behavior within the buildings (Fabi et al. 2011) Several studies (Carrico and Riemer 2011, Dietz et al. 2009, Henryson, Håkansson, and Pyrko 2000) have taken two different approaches can typically be used to reduce buildings’ energy use: First, the technological approach deals with more energy efficient building systems and equipment. Second, the behavioral approach focuses on understanding building occupant presence and behavior to measure actual energy consumption and develop best practices to encourage conservation (Azar and Menassa 2011b). 3.1 Occupants behavior  Occupants behavior definition is; “the result of a continuous combination of several factors crossing different disciplines.” The factors effecting occupant interactions with building control systems are classified into external and internal. The external factors which related to the building science area (e.g. outdoor and indoor temperature) can be categorized in two categories: the physical environment and the context. The internal drivers concern the social science area can be defined into three categories: physiological, social and psychological. These External and Internal factors influence occupant behavior, defined as “Drivers.” Drivers can be defined as:  “the reasons leading to a reaction in the building occupant and suggesting him or her to act. 3.2 Occupants interactions with indoor environmental controls Several studies have investigated occupants’ preferences of the windows in their workplace; window size, position in the walls, and its degree of transparency (Galasiu and Veitch 2006). Many studies investigate the occupants interact with the lighting system without providing the occupants satisfaction or performance in the workplace. These models predict how occupants interact to the lighting system depending on the lighting intensity, the occupant’s schedules and the surrounded factors to predict the occupant’s use of lighting, and therefore predict the lighting energy consumption as a result (Reinhart 2004; Bourgeois et. al. 2006). It can be deduced here that the previous studies investigated the occupants’ behavior inside the workplace and the interactions with the environment; otherwise, the impact of the interactions on the consumption. These studies however seem to have overlooked the effect on lighting preferences in the workplace due to the difference in environmental, physical, occupants activities and the policies on the occupant performance. To that effect, this study proposes the occupants’ preferences in the commercial building regarding to the lighting in the workplace has a significant effect on the occupants’ performance. 4 ACTORS AFFECT OCCUPANTS USAGE OF LIGHTING INCORPORATED IN THE CURRENT RESEARCH Based on the above review of literature and focusing on the institutional buildings, the lighting preferences in institutional buildings that affect the lighting intensity in the workplaces are identified and selected as shown in Table 1. These factors are considered in the present study. Fourteen factors are incorporated in this research, which represents the environmental, physical, activities, and policies factors. The factors that influence occupants’ usage of lighting are hard to quantify and thus a qualitative approach is followed.    303-3 The factors selected to be incorporated in Regression analysis model are clustered into four main categories and their factors, as shown in Figure 3. The four main categories include environmental, physical, users and Tasks lighting required. Each category includes several factors. 5 RESEARCH METHODOLOGY To achieve the objectives of the present research, several steps are accomplished as shown in the schematic diagram Figure 4. The proposed framework for this project consists of 5 main steps. It starts with a comprehensive literature followed by data collection, which in itself consists of two parts studying the lighting factors that affecting the occupants usage and a semi structured questionnaire and open ended interviews is adopted in order to identify the occupants’ artificial lighting preferences due to environmental, physical and activities. A Regression Analysis model is developed using model information data which is then underwent a verification process. The next part of the research methodology is to develop a daylighting usage scale which will guide the architects to best design their office buildings. The model is used to assess daylighting efficiency in private and two-person offices. Table 1: Lighting preferences factors in institutional buildings Category variables Description Environmental Factors Orientation Well-orientated buildings maximize daylighting through building facades reducing the need for artificial lighting.  Time of Day time of the day affects the lighting intensity; ex, before noon, at noon, after noon Sky Condition The brightness of the sky; ex, Full Daylight, Overcast Day, Dark Day  View out the effect of view out windows on the daylighting preferences in the workplace Glare Glare is difficulty seeing in the presence of bright light such as direct or reflected sunlight  Physical Factors Window size to Wall Ratio the window size percentage to the wall ratio glazing color the effect of window glazing color on the daylighting intensity in the workspace Seating position regarding to the window the position of the seat to the window Lighting location control regarding to the seat the capability of controlling the artificial lighting in the workspace Activities Non-computer based Activities ex; Reading, writing, meetings Computer based activities ex; typing, browsing, etc. Policies and Incentives Word Of Mouth (Co-workers in the same space influencing each other's preferences) Energy Awareness Campaigns (Campaigns that increase awareness of energy and its impacts) Financial Incentives (Monetary or other material incentives for reducing energy use) Feedback Techniques (Employers providing workers feedback on their energy use behaviors) 303-4 6 DATA COLLECTION After identifying the lighting preferences factors that may affect the occupants performance, a questionnaire was prepared to assess the effect of these factors on occupants performance. The data is collected via a questionnaire collected form 87 occupants in the institutional buildings at Purdue University.  The questionnaire was designed to identify factors that affect lighting intensity in the workplace and then to predict the occupants performance in an abstract approach. It had two parts where the first part (1) was asking the occupants how strongly the factors contributes to the daylighting intensity as shown in Figure 5. Part (2) was asking the occupants using a specified 5 point subjective scale to represent their performance.  The data collected is the weights of various factors to be incorporated in the model and the performance of each factor.  This paper presents findings from a web-based survey on the current use of in building design. The survey was administered from October 2014 to December 2014. Two hundred and thirty four individuals from 5 institutional buildings at Purdue University completed the survey. The respondents are Faculty, staff and students have an office at Purdue University. They worked in offices with or without windows in the workplace. Among those participants 134 about 59% who have windows in their workplace. The rest of them 92 respondents has no window and skipped from the questionnaire. The total respondents who complete the questionnaire are 87 respondents about 37% of the total respondents. See Fig 5 as sample of the questionnaire and Fig 6 the Questionnaire statistics.      Figure 5: Sample of Questionnaire questions    303-6 Table 2: Analysis of Variance for the Developed Regression Model Factor X Predictor Coefficient P-value 1 Orientation 0.326 0.459 2 Time of Day 0.083 0.839 3 Sky Condition -0.056 0.900 4 View out -0.179 0.668 5 Window size to Wall Ratio -0.023 0.951 6 glazing color -0.124 0.789 7 Seating position regarding to the window -0.034 0.931 8 Window size is big 0.507 0.295 9 Window size is medium 0.510 0.279 10 Window size is small 0.394 0.407 11 Windows facing the seat -0.125 0.599 12 Windows next to the seat -0.090 0.715 13 Non-computer based activities -0.120 0.510 14 Word of Mouth 0.347 0.414 15 Energy awareness campaigns 0.173 0.629 16 Financial Incentives 0.350 0.425  7.2 Validation of Developed Occupants Performance Model  The validation process is to guarantee that the developed models best fit the available data. In order to determine the efficiency of the developed model to derive real world results, the model is tested statistically, logically, and practically. The collected data are divided into two data sets, model building (80%) and validation (20%). The validation data set, that is 20%, selected randomly and kept away while modeling the regression analysis. After developing the regression analysis model, the validation data set is used to test the capability of the developed lighting factors model to predict the occupant’s performance. The developed model is validated by comparing the predicted results with the actual values of the validation data set. [2] AIP= {∑ 1- I (Ei /Ci) I} * 100/n        [3] AVP = 100 – AIP  Where AIP is the Average Invalidity Percent, AVP is the Average Validity Percent, Ei is the ith predicted value, Ci is the ith actual value, and n is the number of observations. Equation 2 expresses the average invalidity, which indicates the prediction error, while Equation 3 presents the average validity percent. The AVP values for the developed performance prediction models regression is 92.55%. These values indicate that the obtained results are satisfactory. 8 CONCLUSIONS AND FUTURE RESEARCH Lighting energy consumption is considered a highly energy consumer in commercial buildings. Achieving higher energy efficiency at commercial buildings demands considering the lighting preferences to the users and their performance in their workplace. It is difficult to measure the occupant’s performance due to their diversity and complexity. The proposed framework is an effective methodology for developing an institutional building sustainable occupant’s performance based model. This model will help the decision 303-8 makers and the designers at different levels to design the work places that accomplish the required levels of visual comfort for the users, while saving energy used in lighting. A multi-dimensional study on performance of the occupants in the commercial buildings has been conducted using 87 surveys obtained from intuitional buildings. The obtained data are analyzed using regression-based performance model and predict the performance of occupants. The developed model benefit both architects and practitioners to choose the appropriate workplace design due to the occupants’ preferences to enhance performance, and energy efficiency. It also provide energy modeling professionals with the various essential factors that affect occupants performance and how it can be assessed/predicted, i.e. performance assessment/ prediction tool. The model has been validated with 92 % Average Validity Percent (AVP) and R square of 0.83 that is a satisfactory result. The research study shows a room for improvement for future study like modeling and simulate the occupant’s interaction.  Acknowledgements The authors wish to acknowledge Assiut University, Egypt for its shared-fund for this research study.  References  Alashwal, N. T., and Budaiwi, I. M. 2011. Energy Savings due to Daylight and Artificial Lighting Integration in Office Buildings in Hot Climate. International Journal of Energy and Environment 2(6): 999–1012. Azar, E., and Menassa, C. C. 2011a. Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings. Journal of Computing in Civil Engineering 26(4): 506–18.  Azar, E., and Menassa, C. C. 2011b. “A Decision Framework for Energy Use Reduction Initiatives in Commercial Buildings.” In Simulation Conference (WSC), Proceedings of the 2011 winter, 816–27.  Bourgeois, D., Reinhart, C., and Macdonald, I. 2006. Adding Advanced Behavioural Models in Whole Building Energy Simulation: A Study on the Total Energy Impact of Manual and Automated Lighting Control. Energy and Buildings 38(7): 814–23.  Buildings Overview | Center for Climate and Energy Solutions. 2015. Accessed February 5. http://www.c2es.org/technology/overview/buildings. Carrico, A. R., and Riemer, M. 2011. Motivating Energy Conservation in the Workplace: An Evaluation of the Use of Group-Level Feedback and Peer Education. Journal of Environmental Psychology 31(1): 1–13. Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C., and Vandenbergh, M. P.  2009. Household Actions Can Provide a Behavioral Wedge to Rapidly Reduce US Carbon Emissions. Proceedings of the National Academy of Sciences 106(44): 18452–56. http://www.pnas.org/content/106/44/18452.short. Edwards, L., and Torcellini, P. A. 2002. A Literature Review of the Effects of Natural Light on Building Occupants. National Renewable Energy Laboratory Golden, CO.  Fabi, V., Andersen, R. V., Corgnati, S. P., Olesen, B. W. and Filippi, M.  2011. Description of Occupant Behaviour in Building Energy Simulation: State-of-Art and Concepts for Improvements. In 12th Conference of International Building Performance Simulation Association, Sydney, 14–16. http://ibpsa.org/proceedings/BS2011/P_1923.pdf. Galasiu, A. D., and Veitch, J. A. 2006. Occupant Preferences and Satisfaction with the Luminous Environment and Control Systems in Daylit Offices: A Literature Review. Energy and Buildings 38(7): 728–42.  Guo, X., Tiller, D. K., Henze, G. P. and Waters. C. E. 2010. The Performance of Occupancy-Based Lighting Control Systems: A Review. Lighting Research and Technology 42(4): 415–31. Henryson, J., Håkansson, T. and Pyrko, J. 2000. Energy Efficiency in Buildings through Information – Swedish Perspective. Energy Policy 28(3): 169–80.  Pyonchan, I., Nemri, A. and Krarti. M. 2009. Estimation of Lighting Energy Savings from Daylighting. Building and Environment 44(3): 509–14.  MINITAB 2006. “User’s Manual,” Release 14, Mintab Inc. Reinhart, C.F. 2004. Lightswitch-2002: A Model for Manual and Automated Control of Electric Lighting and Blinds. Solar Energy 77(1): 15–28.   303-9  5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction    Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015   BUILDING A SUSTAINABLE OCCUPANT’S PERFORMANCE BASED MODEL FOR INSTITUTIONAL BUILDINGS Dalia Salem1,3, Emad Elwakil1, and Amr Kandil2  1 Building Construction management, Purdue University, USA. 2 Construction Engineering Management, Purdue University, USA. 3 dsalem@purdue.edu Abstract: The Sustainable buildings main objectives are to reduce, or avoid, depletion of resources like energy, water, and materials; prevent environmental degradation caused by facilities during the life cycle of the building. Lighting is one of the major energy consumption in institutional buildings. At 2012, the commercial sector, which includes commercial and institutional buildings, and Public Street and highway lighting, consumed about 274 billion kWh for lighting or about 7 % of the USA consumption. Most of the research works have focused predominantly on the environmental and physical factors and have neglected the daily activities of the occupants. This study examines the effects of environmental, physical, and daily activities on occupants’ performance in the institutional buildings as well as develops a model to predict the occupants’ performance using Regression analysis technique. The data was collected from the institutional buildings occupants and building facility experts using questionnaire. The model has been validated with 92 % Average Validity Percent (AVP) and R square of 0.83 which is a satisfactory result. The developed research /model benefits both architects and practitioners to choose the appropriate workplace design due to the occupants’ preferences to enhance performance, and energy efficiency. 1 INTRODUCTION Buildings are one of the major energy consumers in the U.S. as shown in Fig 1. Both commercial and residential buildings account for 42% of the national U.S. energy consumption. The majority of commercial buildings energy consumption is attributed to lighting (25%), space heating and cooling (25%), and ventilation (7%) (Azar and Menassa 2011a). Lighting and HVAC energy used in buildings are considered the main consumers of the total buildings energy consumption. Nearly lighting energy used is responsible for 23%, Heating, ventilation and cooling accounting for 38% (Guo et al. 2010). In a recent study, in US commercial building, 25-40% of the total electricity energy consumption is from electrical lighting (Ihm et. al., 2009).       303-1    Figure 1: Buildings Share of U.S. Primary Energy Consumption (2006) (“Buildings Overview | Center for Climate and Energy Solutions” 2015) Figure 2: 2006 U.S. Primary Energy End-Use Splits (“Buildings Overview | Center for Climate and Energy Solutions” 2015) Building professionals’ significant role is how to reduce energy consumption as well as considerably maintain comfort to the occupants. Artificial lighting systems are considered as a major consumer of energy in buildings and contribute significantly to building cooling load. Daylighting has two fold; contributing in determine the overall environmental quality in buildings as well as saving energy (Alashwal and Budaiwi 2011). Before 1940s, daylighting was considered the main light source in buildings design.  Recently, in sustainable buildings, daylighting is considered as energy and environmental aspect  (Edwards and Torcellini 2002). In case of office lighting, the switching patterns along with the outside conditions are at the core of investigation from the occupants’ behavior point of view. One of the studies has culminated into the fact that as much as 40% energy conservation can be realized if natural light is relied upon compared to the artificial one (Bourgeois, Reinhart, and Macdonald 2006).  Therefore, the scope of the present study is to investigate the lighting preferences in commercial buildings with focus on the institutional buildings and develop a framework for predicting occupant’s performance. 2 RESEARCH OBJECTIVES The objectives of the present study is to build a sustainable occupant’s performance based model for institutional buildings which can be summarized as follows:  i. Investigate and identify the factors affect occupants usage of lighting. ii. Data collection from a real workplace iii. Determine the significant factors that mostly contribute to the Lighting intensity in the workplace. iv. Determine the factors that affect the occupants’ performance v. Develop a model / framework based on these factors. vi. Validate the developed model / framework 3 BACKGROUND The most significant factors that influence the energy and indoor environmental performances of buildings are outdoor/indoor climate, building characteristics, and occupant behavior. The most important factor is human behavior, followed by building design. Indeed, there is often an obvious discrepancy between real 303-2 total energy use in buildings and what is predicted. The reasons for this gap are a general need to understand the role of human behavior within the buildings (Fabi et al. 2011) Several studies (Carrico and Riemer 2011, Dietz et al. 2009, Henryson, Håkansson, and Pyrko 2000) have taken two different approaches can typically be used to reduce buildings’ energy use: First, the technological approach deals with more energy efficient building systems and equipment. Second, the behavioral approach focuses on understanding building occupant presence and behavior to measure actual energy consumption and develop best practices to encourage conservation (Azar and Menassa 2011b). 3.1 Occupants behavior  Occupants behavior definition is; “the result of a continuous combination of several factors crossing different disciplines.” The factors effecting occupant interactions with building control systems are classified into external and internal. The external factors which related to the building science area (e.g. outdoor and indoor temperature) can be categorized in two categories: the physical environment and the context. The internal drivers concern the social science area can be defined into three categories: physiological, social and psychological. These External and Internal factors influence occupant behavior, defined as “Drivers.” Drivers can be defined as:  “the reasons leading to a reaction in the building occupant and suggesting him or her to act. 3.2 Occupants interactions with indoor environmental controls Several studies have investigated occupants’ preferences of the windows in their workplace; window size, position in the walls, and its degree of transparency (Galasiu and Veitch 2006). Many studies investigate the occupants interact with the lighting system without providing the occupants satisfaction or performance in the workplace. These models predict how occupants interact to the lighting system depending on the lighting intensity, the occupant’s schedules and the surrounded factors to predict the occupant’s use of lighting, and therefore predict the lighting energy consumption as a result (Reinhart 2004; Bourgeois et. al. 2006). It can be deduced here that the previous studies investigated the occupants’ behavior inside the workplace and the interactions with the environment; otherwise, the impact of the interactions on the consumption. These studies however seem to have overlooked the effect on lighting preferences in the workplace due to the difference in environmental, physical, occupants activities and the policies on the occupant performance. To that effect, this study proposes the occupants’ preferences in the commercial building regarding to the lighting in the workplace has a significant effect on the occupants’ performance. 4 ACTORS AFFECT OCCUPANTS USAGE OF LIGHTING INCORPORATED IN THE CURRENT RESEARCH Based on the above review of literature and focusing on the institutional buildings, the lighting preferences in institutional buildings that affect the lighting intensity in the workplaces are identified and selected as shown in Table 1. These factors are considered in the present study. Fourteen factors are incorporated in this research, which represents the environmental, physical, activities, and policies factors. The factors that influence occupants’ usage of lighting are hard to quantify and thus a qualitative approach is followed.    303-3 The factors selected to be incorporated in Regression analysis model are clustered into four main categories and their factors, as shown in Figure 3. The four main categories include environmental, physical, users and Tasks lighting required. Each category includes several factors. 5 RESEARCH METHODOLOGY To achieve the objectives of the present research, several steps are accomplished as shown in the schematic diagram Figure 4. The proposed framework for this project consists of 5 main steps. It starts with a comprehensive literature followed by data collection, which in itself consists of two parts studying the lighting factors that affecting the occupants usage and a semi structured questionnaire and open ended interviews is adopted in order to identify the occupants’ artificial lighting preferences due to environmental, physical and activities. A Regression Analysis model is developed using model information data which is then underwent a verification process. The next part of the research methodology is to develop a daylighting usage scale which will guide the architects to best design their office buildings. The model is used to assess daylighting efficiency in private and two-person offices. Table 1: Lighting preferences factors in institutional buildings Category variables Description Environmental Factors Orientation Well-orientated buildings maximize daylighting through building facades reducing the need for artificial lighting.  Time of Day time of the day affects the lighting intensity; ex, before noon, at noon, after noon Sky Condition The brightness of the sky; ex, Full Daylight, Overcast Day, Dark Day  View out the effect of view out windows on the daylighting preferences in the workplace Glare Glare is difficulty seeing in the presence of bright light such as direct or reflected sunlight  Physical Factors Window size to Wall Ratio the window size percentage to the wall ratio glazing color the effect of window glazing color on the daylighting intensity in the workspace Seating position regarding to the window the position of the seat to the window Lighting location control regarding to the seat the capability of controlling the artificial lighting in the workspace Activities Non-computer based Activities ex; Reading, writing, meetings Computer based activities ex; typing, browsing, etc. Policies and Incentives Word Of Mouth (Co-workers in the same space influencing each other's preferences) Energy Awareness Campaigns (Campaigns that increase awareness of energy and its impacts) Financial Incentives (Monetary or other material incentives for reducing energy use) Feedback Techniques (Employers providing workers feedback on their energy use behaviors) 303-4 6 DATA COLLECTION After identifying the lighting preferences factors that may affect the occupants performance, a questionnaire was prepared to assess the effect of these factors on occupants performance. The data is collected via a questionnaire collected form 87 occupants in the institutional buildings at Purdue University.  The questionnaire was designed to identify factors that affect lighting intensity in the workplace and then to predict the occupants performance in an abstract approach. It had two parts where the first part (1) was asking the occupants how strongly the factors contributes to the daylighting intensity as shown in Figure 5. Part (2) was asking the occupants using a specified 5 point subjective scale to represent their performance.  The data collected is the weights of various factors to be incorporated in the model and the performance of each factor.  This paper presents findings from a web-based survey on the current use of in building design. The survey was administered from October 2014 to December 2014. Two hundred and thirty four individuals from 5 institutional buildings at Purdue University completed the survey. The respondents are Faculty, staff and students have an office at Purdue University. They worked in offices with or without windows in the workplace. Among those participants 134 about 59% who have windows in their workplace. The rest of them 92 respondents has no window and skipped from the questionnaire. The total respondents who complete the questionnaire are 87 respondents about 37% of the total respondents. See Fig 5 as sample of the questionnaire and Fig 6 the Questionnaire statistics.      Figure 5: Sample of Questionnaire questions    303-6 Table 2: Analysis of Variance for the Developed Regression Model Factor X Predictor Coefficient P-value 1 Orientation 0.326 0.459 2 Time of Day 0.083 0.839 3 Sky Condition -0.056 0.900 4 View out -0.179 0.668 5 Window size to Wall Ratio -0.023 0.951 6 glazing color -0.124 0.789 7 Seating position regarding to the window -0.034 0.931 8 Window size is big 0.507 0.295 9 Window size is medium 0.510 0.279 10 Window size is small 0.394 0.407 11 Windows facing the seat -0.125 0.599 12 Windows next to the seat -0.090 0.715 13 Non-computer based activities -0.120 0.510 14 Word of Mouth 0.347 0.414 15 Energy awareness campaigns 0.173 0.629 16 Financial Incentives 0.350 0.425  7.2 Validation of Developed Occupants Performance Model  The validation process is to guarantee that the developed models best fit the available data. In order to determine the efficiency of the developed model to derive real world results, the model is tested statistically, logically, and practically. The collected data are divided into two data sets, model building (80%) and validation (20%). The validation data set, that is 20%, selected randomly and kept away while modeling the regression analysis. After developing the regression analysis model, the validation data set is used to test the capability of the developed lighting factors model to predict the occupant’s performance. The developed model is validated by comparing the predicted results with the actual values of the validation data set. [2] AIP= {∑ 1- I (Ei /Ci) I} * 100/n        [3] AVP = 100 – AIP  Where AIP is the Average Invalidity Percent, AVP is the Average Validity Percent, Ei is the ith predicted value, Ci is the ith actual value, and n is the number of observations. Equation 2 expresses the average invalidity, which indicates the prediction error, while Equation 3 presents the average validity percent. The AVP values for the developed performance prediction models regression is 92.55%. These values indicate that the obtained results are satisfactory. 8 CONCLUSIONS AND FUTURE RESEARCH Lighting energy consumption is considered a highly energy consumer in commercial buildings. Achieving higher energy efficiency at commercial buildings demands considering the lighting preferences to the users and their performance in their workplace. It is difficult to measure the occupant’s performance due to their diversity and complexity. The proposed framework is an effective methodology for developing an institutional building sustainable occupant’s performance based model. This model will help the decision 303-8 makers and the designers at different levels to design the work places that accomplish the required levels of visual comfort for the users, while saving energy used in lighting. A multi-dimensional study on performance of the occupants in the commercial buildings has been conducted using 87 surveys obtained from intuitional buildings. The obtained data are analyzed using regression-based performance model and predict the performance of occupants. The developed model benefit both architects and practitioners to choose the appropriate workplace design due to the occupants’ preferences to enhance performance, and energy efficiency. It also provide energy modeling professionals with the various essential factors that affect occupants performance and how it can be assessed/predicted, i.e. performance assessment/ prediction tool. The model has been validated with 92 % Average Validity Percent (AVP) and R square of 0.83 that is a satisfactory result. The research study shows a room for improvement for future study like modeling and simulate the occupant’s interaction.  Acknowledgements The authors wish to acknowledge Assiut University, Egypt for its shared-fund for this research study.  References  Alashwal, N. T., and Budaiwi, I. M. 2011. Energy Savings due to Daylight and Artificial Lighting Integration in Office Buildings in Hot Climate. International Journal of Energy and Environment 2(6): 999–1012. Azar, E., and Menassa, C. C. 2011a. Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings. Journal of Computing in Civil Engineering 26(4): 506–18.  Azar, E., and Menassa, C. C. 2011b. “A Decision Framework for Energy Use Reduction Initiatives in Commercial Buildings.” In Simulation Conference (WSC), Proceedings of the 2011 winter, 816–27.  Bourgeois, D., Reinhart, C., and Macdonald, I. 2006. Adding Advanced Behavioural Models in Whole Building Energy Simulation: A Study on the Total Energy Impact of Manual and Automated Lighting Control. Energy and Buildings 38(7): 814–23.  Buildings Overview | Center for Climate and Energy Solutions. 2015. Accessed February 5. http://www.c2es.org/technology/overview/buildings. Carrico, A. R., and Riemer, M. 2011. Motivating Energy Conservation in the Workplace: An Evaluation of the Use of Group-Level Feedback and Peer Education. Journal of Environmental Psychology 31(1): 1–13. Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C., and Vandenbergh, M. P.  2009. Household Actions Can Provide a Behavioral Wedge to Rapidly Reduce US Carbon Emissions. Proceedings of the National Academy of Sciences 106(44): 18452–56. http://www.pnas.org/content/106/44/18452.short. Edwards, L., and Torcellini, P. A. 2002. A Literature Review of the Effects of Natural Light on Building Occupants. National Renewable Energy Laboratory Golden, CO.  Fabi, V., Andersen, R. V., Corgnati, S. P., Olesen, B. W. and Filippi, M.  2011. Description of Occupant Behaviour in Building Energy Simulation: State-of-Art and Concepts for Improvements. In 12th Conference of International Building Performance Simulation Association, Sydney, 14–16. http://ibpsa.org/proceedings/BS2011/P_1923.pdf. Galasiu, A. D., and Veitch, J. A. 2006. Occupant Preferences and Satisfaction with the Luminous Environment and Control Systems in Daylit Offices: A Literature Review. Energy and Buildings 38(7): 728–42.  Guo, X., Tiller, D. K., Henze, G. P. and Waters. C. E. 2010. The Performance of Occupancy-Based Lighting Control Systems: A Review. Lighting Research and Technology 42(4): 415–31. Henryson, J., Håkansson, T. and Pyrko, J. 2000. Energy Efficiency in Buildings through Information – Swedish Perspective. Energy Policy 28(3): 169–80.  Pyonchan, I., Nemri, A. and Krarti. M. 2009. Estimation of Lighting Energy Savings from Daylighting. Building and Environment 44(3): 509–14.  MINITAB 2006. “User’s Manual,” Release 14, Mintab Inc. Reinhart, C.F. 2004. Lightswitch-2002: A Model for Manual and Automated Control of Electric Lighting and Blinds. Solar Energy 77(1): 15–28.   303-9  BUILDING A SUSTAINABLE OCCUPANT’S  PERFORMANCE BASED MODEL FOR INSTITUTIONAL BUILDINGS  Presenter name: Zenith Rahore Master student, Purdue Polytechnic Institute, Purdue University  June, 09, 2015 5th International/11th Construction Specialty Conference 5e International/11e Conférence spécialisée sur la construction   Vancouver, British Columbia June 8 to June 10, 2015 / 8 juin au 10 juin 2015  Dalia Salem1,4, Emad Elwakil2, and Amr Kandil3  1 PhD student, Purdue Polytechnic Institute, Purdue University, USA. 2 Assistant Professor, Purdue Polytechnic Institute, Purdue University, USA. 3 Associate Professor, Construction Engineering Management, Purdue University, USA. 4 dsalem@purdue.edu Outline §  Introduction §  Background §  Research Objectives §  Factors Affect Occupants Usage Of Lighting §  Research Methodology  §  Data Collection §  Occupants’ Performance Model Outline §  Regression-based Occupants Performance Model §  Results §  Validation Of Developed Occupants Performance Model §  Conclusions And Future Research  4 INTRODUCTION Commercial Buildings •  Buildings are one of the major energy consumers in the U.S. •  Commercial Buildings. •  Lightning and HVAC are t h e  m a i n  e n e r g y consumers in commercial buildings. 5 INTRODUCTION Occupants role in buildings •  factors that influence the e n e r g y a n d i n d o o r e n v i r o n m e n t a l  i n Commercial buildings. •  Discrepancy between real total energy use in buildings and what is predicted.  •  The most important factor i s human behav io r, fo l lowed by bui ld ing design.  Energy	  Consump.on	  in	  Buildings	  responsibility	  Users	  behavior	  Buildings	  Characteris.cs	  outdoor/indoor	  climate	  6 Background Occupants’ preferences and performace  •  (Galasiu and Veitch 2006) investigated occupants’ preferences of the windows in their workplace.  •  Developed models predict occupants interact to the lighting system (Reinhart 2004; Bourgeois et. al. 2006). •  Many studies investigate the occupants interact with the lighting system without providing the occupants satisfaction or performance in the workplace.  7 Research Objectives Aim of the research  •  Investigate and identify the factors affect occupants usage of lighting. •  Data collection from a real workplace •  Determine the significant factors that mostly contribute to the Lighting intensity in the workplace. •  Determine the factors that affect the occupants’ performance •  Develop a model / framework based on these factors. •  Validate the developed model / framework  8 FACTORS AFFECT OCCUPANTS USAGE  OF LIGHTING 9 RESEARCH METHODOLOGY  Conducting	  a	  surveyDevelop	  Regression	  Analysis	  ModelLighting	  Behavioral	  modelingResults	  and	  DiscussionLiterature	  ReviewOccupancyLighting	  Behavioral	  Usage	  FactorsRegression	  AnalysisEnvironmentalPhysicalUsersPolicies	  &	  IncentivesEnergy	  consumption	  in	  buildings10 Data Collection Ques.onnaire	  Sta.s.cs Ques.onnaire	  Ques.ons	  Sample 11 OCCUPANTS’ PERFORMANCE MODEL Regression-Based Occupants Performance Model •  MINITAB is utilized to develop a regression model. •  Four selection criteria are used to distinguish between different proposed models. •  These criteria are R-square, adjusted R-square, mean square error (S or MSE), and Mallow’s Cp. •  the selected model has the highest R2 of 0.85 and adjust R2 of 0.83, the Cp value of 8.2 close to 9 (i.e. number of variables), and the minimum MSE value of 2.2524. 12 	  Y	  =	  5.7	  +	  0.326	  x1	  +	  0.083	  x2	   -­‐	  0.056	  x3	   -­‐	  0.179	  x4	   -­‐	  0.023	  x5	   -­‐	  0.124	  x6	  -­‐	  0.034	  x7	  +	  0.507	  x8	  +	  0.510	  x9	  +	  0.394	  x10	  -­‐	  0.125	  x11	  -­‐	  0.090	  x12	  -­‐	  0.120	  x13	  +	  0.347	  x14	  +	  0.173	  x15	  +	  0.350	  x16	  	  •  Y	  denotes	  the	  occupants’	  performance	  	  •  Xs	  denote	  the	  ligh.ng	  factors	  OCCUPANTS’ PERFORMANCE MODEL Regression-Based Occupants Performance Model Factor	  X Predictor Coefficient P-­‐value 1 Orienta.on 0.326 0.459 2 Time	  of	  Day 0.083 0.839 3 Sky	  Condi.on -­‐0.056 0.900 4 View	  out -­‐0.179 0.668 5 Window	  size	  to	  Wall	  Ra.o -­‐0.023 0.951 6 glazing	  color -­‐0.124 0.789 7 Sea.ng	  posi.on	  regarding	  to	  the	  window -­‐0.034 0.931 8 Window	  size	  is	  big 0.507 0.295 9 Window	  size	  is	  medium 0.510 0.279 10 Window	  size	  is	  small 0.394 0.407 11 Windows	  facing	  the	  seat -­‐0.125 0.599 12 Windows	  next	  to	  the	  seat -­‐0.090 0.715 13 Non-­‐computer	  based	  ac.vi.es -­‐0.120 0.510 14 Word	  of	  Mouth 0.347 0.414 15 Energy	  awareness	  campaigns 0.173 0.629 16 Financial	  Incen.ves 0.350 0.425 13 OCCUPANTS’ PERFORMANCE MODEL Regression-Based Occupants Performance Model 14 Validation of Developed Occupants Performance Model OCCUPANTS’ PERFORMANCE MODEL AIP=	  {∑	  1-­‐	  I	  (Ei	  /Ci)	  I}	  *	  100/n	  	   	  	  	   	   	   	  	  AVP	  =	  100	  –	  AIP	  	  	  The	   AVP	   values	   for	   the	   developed	   performance	  predic.on	  models	  regression	  is	  92.55%.	  	  These	  values	   indicate	   that	   the	  obtained	  results	  are	  sa.sfactory.	  15 Conclusions & Future Research Conclusions •  Lighting energy consumption is a highly energy consumer in commercial buildings.  •  Considering the lighting preferences to the users and their performance is difficult due to their diversity and complexity. •  The proposed framework is an effective methodology for predicting an occupant’s performance based model. •  This model will help the decision makers and the designers at different levels to design the work places that accomplish the required levels of visual comfort for the users, while saving energy used in lighting.    16 Conclusions & Future Research Conclusions •  A multi-dimensional study on performance of the occupants in the commercial buildings has been conducted using 87 surveys obtained from intuitional buildings.  •  The obtained data are analyzed using regression-based performance model. •  It provides energy modeling professionals with the various essential factors that affect occupants performance and how it can be assessed/predicted. •  The research study shows a room for improvement for future study like modeling and simulate the occupant’s interaction.   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