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

A data analysis framework for optimizing occupant energy use while sustaining indoor environmental quality Sharmin, Tanzia; Gül, Mustafa; Al-Hussein, Mohamed 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   A DATA ANALYSIS FRAMEWORK FOR OPTIMIZING OCCUPANT ENERGY USE WHILE SUSTAINING INDOOR ENVIRONMENTAL QUALITY Tanzia Sharmin1,2, Mustafa Gül1, and Mohamed Al-Hussein1 1 Department of Civil and Environmental Engineering, University of Alberta, Canada 2 tanzia@ualberta.ca Abstract: Sustaining standard indoor environmental quality (IEQ) is a crucial factor in promoting occupant health and comfort, and a significant proportion of a facility’s energy use is directed toward indoor climate control. Meanwhile, because operation of facilities accounts for a large share of the world’s energy consumption, it has warranted increased interest in efforts to design facility energy management systems that reduce energy consumption. In this context, facility managers aim to achieve the optimal balance between occupant comfort and overall energy consumption. The objective of this paper is to demonstrate a framework that assists facility managers in identifying residential occupant activities that influence energy consumption and also ascertaining any correlation or sequential activities patterns and their association with respect to IEQ. This work is facilitated by the installation of various sensors in a case study, the “Stony Mountain Plaza” project in Fort McMurray, Canada. It is expected that the extracted information and strategies acquired from the framework can be implemented within the facility management system to achieve financial, environmental, and health benefits. 1 BACKGROUND Generally speaking people spend most of their time indoors in a home or workplace (US Bureau of Labor Statistics 2006), a trend which rings especially true for cold climatic regions like Canada, where winters are cold and long (Statistics Canada 2001). A national survey on Canadian human activity patterns conducted in 2010–2011 indicates that the average Canadian spends 90% of their time indoors, most of which is at home (Matz et al. 2014; Public Health Agency of Canada 2010). Notably, these indoor spaces, which are considered to be safe and secure shelters, for many can become a cause of serious health problems because of poor indoor environmental quality (non-standard CO2 level, RH level, and/or indoor temperature) (Sharmin et al. 2014). It is observed that, in addition to external air sources, building materials, and furnishings, one of the major influences on indoor environment is occupant activities. Studies indicate that occupant appliance usage (including faulty usage) and comfort-related choices (use of HVAC, electrical duct heating, energy recovery ventilation, window opening patterns) affect indoor environmental quality (IEQ) and may cause indoor air contamination (Building Air Quality 1991).  Meanwhile, operation of appliances and maintaining of occupants’ desired indoor temperature requires considerable energy. In 2007, Canadian households consumed a total of 1,368,955 TJ (terajoules) of energy and in 2011 there was a 4% increase to 1,425,185 (TJ) (Statistics Canada 2011). Statistics indicate that improper use of appliances (both in residential and commercial facilities) accounts for a substantial share of total energy wastage; however, a 30% reduction in energy consumption can be 296-1 achieved by eliminating wastage in facilities, which underscores the need for an effective building management system (BMS) to manage/control operational energy usage without compromising occupant comfort. Along with technological advancements there is a growing interest in improving the intelligence of facilities by means of sensor-based building management systems capable of collecting vast amounts of building-related data. However, it should be considered that in order to take advantage of such systems, all the relevant observations need to be extracted, since less than optimal building performance may be encountered when energy use information is inadequate. Recent studies indicate that adapting different monitoring systems, using modern, energy-saving technologies and providing appropriate feedbacks to building occupants can reduce energy consumption by up to 20% (Abrahamse et al. 2005; Darby 2006; Chetty 2008; Vassileva 2012). However, very few research studies have considered examining occupant behaviour for energy management (Haas 1998). Although sustainable development require behavioural awareness (Wood 2007; Abrahamse, 2007), depending only on occupant behaviour cannot be considered an effective solution. Instead what is required is a holistic user-centric control strategy for building management (Jiang 2009). This study aims to provide greater insight into occupant activity patterns and their effect on energy consumption and IEQ in order to equip BMS with an effective user-centric energy and IEQ management strategy. This study proposes a framework to extract useful observations pertaining to occupant energy usage patterns by means of sensor-based monitoring, using the case study of a four-storey multi-family residential facility in Fort McMurray, Alberta, Canada. Previous research by the authors has involved extract useful information by studying correlations among occupant activities. In this study, the authors investigate occupant activities to determine whether any sequential activity patterns exist, as well as to identify any correlations among activities, or between activities and IEQ. It is expected that these observations will enhance BMS through effective energy and IEQ management. 2 OBJECTIVE AND METHODOLOGICAL APPROACH 2.1 Objective The objective of this study is to propose a methodological approach for extracting useful information from sensor-based monitoring for the purpose of user-centric energy and IEQ management of a multi-family residential facility. The study first aims to identify the most significant occupant activities affecting overall household energy consumption. Occupant energy usage patterns are also investigated for different unit types (different directional orientations and unit sizes), based upon which occupants can be categorized based on usage patterns. This study further considers identifying occupants’ sequential energy usage patterns and the correlations among them by analyzing time-ordered energy usage measurements. It is expected that these information will assist facility managers to understand occupant energy load and estimate future energy demand so they can plan accordingly. Facility managers will also be able to calculate internal heat gain from occupant activities in order to estimate optimized heating energy requirements. It is expected that this research will yield insightful observations of occupant energy usage, which may in turn be useful in shaping energy and IEQ management strategies. 2.2 Methodology To achieve the above mentioned objective, a sensor-based monitoring system is developed to measure occupant operating energy and IEQ. In order to investigate whether occupant energy usage patterns and IEQ are affected by unit characteristics, this study measures occupant usage patterns from different unit types. At each floor of the four-storey case study building, 1 ‘one-bedroom’ unit facing north, 1 ‘one-bedroom’ unit facing south, and 1 ‘two-bedroom’ unit facing north are selected as case units. The sensor network is designed such that sensors are installed and data is collected from these 12 case study units. In order to ensure anonymity, in this study, the case study units are assigned codes (numbered 1 through 12). Figure 1 shows a schemata of the sensors used and their locations in a case ‘one-bedroom’ unit. The locations and types of sensors installed in the ‘two-bedroom’ units are similar to those of the ‘one-bedroom’ units. Table 1 shows the data collected by the installed sensors. 296-2 Figure 4 shows that, for the above three groups, (representing both ‘one-bedroom’ units and ‘two-bedroom’ units), hot water tank (14%-35%), lighting (13%-19%), and refrigerators (10%-15%) are high energy consumers. It is observed that heating energy consumption is comparatively higher in ‘two-bedroom’ units (14%-18%) than in ‘one-bedroom’ units (2%-13%), presumably due to the larger size of these units. It is also found that south-facing units have lower heating consumption (2%-9%) than north-facing units; solar heat may be the reason for lower heating demand in the south-facing units. With the major energy consuming factors related to occupant usage patterns identified for a multi-family residential facility, it is possible for the facility manager to set energy usage limits for each unit. 3.2 Correlation and Sequential Energy Usage Pattern and their Association with IEQ A study of the daily energy usage data shows that different sequential activity patterns can be observed among occupants. For example, it is observed that when kitchen plugs are in use (presumably for the purpose of food preparation), the range and range hood fan are found to be in operation shortly afterward (Figure 5). It is also observed that during that time kitchen lighting is also turned on. Data also shows that when bathroom lighting is on, water consumption tends to take place during the same timeframe. These sequential activity patterns for specific units can be used to develop a predictive sequential energy usage pattern model. Facility managers can use this information for estimating future-state energy load for improved energy management.        Figure 5: Sequential activity pattern   Figure 6: Internal heat gain associated with activity pattern  296-7 Correlations across different activities and their association with IEQ can be observed from the measured data. For instance, it is also observed that during the time the above mentioned food preparation activities are performed, the internal heat gain from kitchen appliances increases the indoor temperature even though heating consumption is low for that specific time (Figure 6). Facility managers can take into account this information regarding internal heat gain while estimating heating load. Analyzing recorded data identifies that the apartments generally show peak electricity consumption during hot water consumption (Figure 7). Furthermore, when water consumption is high in a given unit, relative humidity is also high for that unit. It is also observed that the indoor CO2 level is significantly affected by occupant energy recovery ventilation usage patterns (Figure 8).     Figure 7: Peak electricity consumption and IEQ associated with water usage pattern    Figure 8: IEQ associated with occupants’ ERV usage pattern Cold water Hot water 296-8  Occupant presence and absence patterns can also be observed by studying the data. Figure 9 shows that occupants of unit 3 were not at home for two days (January 3-5, 2013). It is also observed that three Sundays of this month show higher CO2 concentration and heating and water consumption, which indicates higher demand during that time. This information can assist building managers to understand demand load for effective energy management.    Figure 9: Occupant presence-absence information  4 CONCLUSION The framework presented in this study demonstrates the use of real-time sensor-based data to support identification of occupant energy usage patterns and associated IEQ in order to improve energy and IEQ management. The knowledge generated from this study can be used to assess the demand load and timing of peaks loads. Aggregating the energy demand for all occupants in the multi-family building can assist with the development of an energy demand profile. Accordingly, the BMS can propose an appliance scheduling scheme based on time-varying retail pricing. This information can be used to estimate the internal heat gains (from occupants, office equipment, and lighting) in order to calculate the energy needed to maintain adequate comfort levels. Through the combination of these knowledge the BMS will be able to minimize the consumption of resources while maintaining a comfortable IEQ level. Acknowledgements The authors would like to thank Gurjeet Singh and Ahmed Alrifai (Research Engineers, University of Alberta), and Veselin Ganev, (graduate student, University of Alberta) for their valuable contributions to this research project. The authors also would like to thank the contributors who have funded or otherwise supported this research project: Cormode & Dickson Construction Ltd., Integrated Management and Realty Ltd., Hydraft Development Services Inc., TLJ Engineering Consultants, BCT Structures, and Wood Buffalo Housing and Development Corporation. References Abrahamse, W., Steg, L., Vlek Ch, and Rothengatter, T. 2007. The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviours, and behavioral antecedents. Journal of Environmental Psychology, 27(4): 265-76. Abrahamse, W., Steg, L., Vlek, C., and Rothengatter, T. 2005. A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25(3), 273-291. Branco, G., Lachal, B., Gallinelli, P., and Weber, W. 2004. Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings, 36(6): 543-555. 296-9 Building Air Quality. 1991. A Guide for Building Owners and Facility Managers, CDC, Centre for Disease Control and Prevention. Retrieved on January 11, 2015 from http://www.cdc.gov/search.do?queryText=factors+affecting+indoor+air+quality&searchButton.x=39&searchButton.y=6&action=search  Chetty, M., Tran, D., and Grinter, R.E. 2008. Getting to Green: Understanding Resource Consumption in the Home. Proc. UbiComp, New York, NY, USA. Darby, S. 2006. The Effectiveness of Feedback on Energy Consumption. Technical Report, Environmental Change Institute, University of Oxford, UK. Grini, C., Mathisen, H.-M., Sartori, I., Haase, M., Sørensen, H.W.J., Petersen, A., Bryn, I., and Wigenstad, T.. LECO – Energibruk i fem kontorbygg i Norge. Technical report, SINTEF, 2009.  Haas, R., Auer, H., and Biermayr, P. 1998. The impact of consumer behavior on residential energy demand for space heating. Energy and Buildings, 27(2): 195-205. Jeeninga, H., Uyterlimde, M., and Uitzinger, J. 2001. Energy Use of Energy Efficient Residences, Report ECN & IVAM.  Jiang, X., Van Ly, M., Taneja, J., Dutta, P., and Culler, D. 2009. Experiences with a High-Fidelity Wireless Building Energy Auditing Network. Proc. ACM SenSys 2009, Berkeley, CA, USA. Levinson, A. and Niemann, S. 2004. Energy use by apartment tenants when landlords pay for utilities. Resource and Energy Economics, 26(1): 51-75. Matz, C.J., Stieb, D.M., Davis, K., Egyed, M., Rose, A., Chou, B., and Brion, O. 2014. Effects of age,season, gender and urban-rural status on time-activity: Canadian human activity pattern survey 2 (CHAPS 2), International Journal of Environmental Research and Public Health, 11(2): 2108-2124. Retrieved on July 7, 2014 from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945588/ Papakostas, K.T. and Sotiropoulos, B.A. 1997. Occupational and energy behavior patterns in Greek residences. Energy and Buildings, 26(2): 207-213. Public Health Agency of Canada, Chronic Diseases in Canada, Volume 29 • Supplement 2 • 2010. Retrieved on January 11, 2015 from http://www.phac-aspc.gc.ca/publicat/cdic-mcbc/29-2-supp/ar_04-eng.php Quinn, P. 2011. Illinois Department of Public Health Guidelines for Indoor Air Quality. Illinois Department of Public Health, Environmental Health Fact Sheet. Retrieved on July 7, 2014 from http://www.idph.state.il.us/envhealth/factsheets/indoorairqualityguidefs.htm Sharmin, T., Gül, M., Li, X., Ganev, V., Nikolaidis, I., and Al-Hussein, M. 2014. Monitoring building energy consumption, thermal performance, and indoor air quality in a cold climate region. Sustainable Cities and Society, 13: 57-68. Sjögren J-U, Andersson S, and Olofsson T. 2007. An approach to evaluate the energy performance of buildings based on incomplete monthly data. Energy and Buildings, 39(8): 945-953. Statistics Canada. 2011. Households and the Environment: Energy Use. Retrieved on January 11, 2015 from http://www.statcan.gc.ca/pub/11-526-s/2013002/part-partie1-eng.htm  Statistics Canada. 2001 Households and the Environment. Retrieved on January 11, 2015 from http://www.statcan.gc.ca/pub/11-526-x/2013001/part-partie1-eng.htm  US Bureau of Labor Statistics. 2006. Retrieved on January 11, 2015 from www.bls.gov/oco/ US DOE Energy Information Administration. 2003. Commercial Buildings Energy Consumption Survey. Retrieved on January 11, 2015 from www.eia.doe.gov/emeu/cbecs Vassileva, I., Wallin, F., and Dahlquist, E. 2012. Analytical comparison between electricity consumption and behavioral characteristics of Swedish households in rented apartments. Applied Energy, 90(1): 182-188. Wood, G. and Newborough, M. 2003. Dynamic energy-consumption for domestic appliances: environment, behavior and design. Energy and Buildings, 35(8): 821-841. Wyon, P. and Wargocki, D.P. 2006. Indoor air quality effects on office work. In D.Clements- Croome (Ed.). The productive workplace. London: E&FN Spon. Yu, Z., Fung, B.C.M., Haghighat, F., Yoshino, H., and Morofsky, E. 2011. A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43(6): 1409-1417. 296-10  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   A DATA ANALYSIS FRAMEWORK FOR OPTIMIZING OCCUPANT ENERGY USE WHILE SUSTAINING INDOOR ENVIRONMENTAL QUALITY Tanzia Sharmin1,2, Mustafa Gül1, and Mohamed Al-Hussein1 1 Department of Civil and Environmental Engineering, University of Alberta, Canada 2 tanzia@ualberta.ca Abstract: Sustaining standard indoor environmental quality (IEQ) is a crucial factor in promoting occupant health and comfort, and a significant proportion of a facility’s energy use is directed toward indoor climate control. Meanwhile, because operation of facilities accounts for a large share of the world’s energy consumption, it has warranted increased interest in efforts to design facility energy management systems that reduce energy consumption. In this context, facility managers aim to achieve the optimal balance between occupant comfort and overall energy consumption. The objective of this paper is to demonstrate a framework that assists facility managers in identifying residential occupant activities that influence energy consumption and also ascertaining any correlation or sequential activities patterns and their association with respect to IEQ. This work is facilitated by the installation of various sensors in a case study, the “Stony Mountain Plaza” project in Fort McMurray, Canada. It is expected that the extracted information and strategies acquired from the framework can be implemented within the facility management system to achieve financial, environmental, and health benefits. 1 BACKGROUND Generally speaking people spend most of their time indoors in a home or workplace (US Bureau of Labor Statistics 2006), a trend which rings especially true for cold climatic regions like Canada, where winters are cold and long (Statistics Canada 2001). A national survey on Canadian human activity patterns conducted in 2010–2011 indicates that the average Canadian spends 90% of their time indoors, most of which is at home (Matz et al. 2014; Public Health Agency of Canada 2010). Notably, these indoor spaces, which are considered to be safe and secure shelters, for many can become a cause of serious health problems because of poor indoor environmental quality (non-standard CO2 level, RH level, and/or indoor temperature) (Sharmin et al. 2014). It is observed that, in addition to external air sources, building materials, and furnishings, one of the major influences on indoor environment is occupant activities. Studies indicate that occupant appliance usage (including faulty usage) and comfort-related choices (use of HVAC, electrical duct heating, energy recovery ventilation, window opening patterns) affect indoor environmental quality (IEQ) and may cause indoor air contamination (Building Air Quality 1991).  Meanwhile, operation of appliances and maintaining of occupants’ desired indoor temperature requires considerable energy. In 2007, Canadian households consumed a total of 1,368,955 TJ (terajoules) of energy and in 2011 there was a 4% increase to 1,425,185 (TJ) (Statistics Canada 2011). Statistics indicate that improper use of appliances (both in residential and commercial facilities) accounts for a substantial share of total energy wastage; however, a 30% reduction in energy consumption can be 296-1 achieved by eliminating wastage in facilities, which underscores the need for an effective building management system (BMS) to manage/control operational energy usage without compromising occupant comfort. Along with technological advancements there is a growing interest in improving the intelligence of facilities by means of sensor-based building management systems capable of collecting vast amounts of building-related data. However, it should be considered that in order to take advantage of such systems, all the relevant observations need to be extracted, since less than optimal building performance may be encountered when energy use information is inadequate. Recent studies indicate that adapting different monitoring systems, using modern, energy-saving technologies and providing appropriate feedbacks to building occupants can reduce energy consumption by up to 20% (Abrahamse et al. 2005; Darby 2006; Chetty 2008; Vassileva 2012). However, very few research studies have considered examining occupant behaviour for energy management (Haas 1998). Although sustainable development require behavioural awareness (Wood 2007; Abrahamse, 2007), depending only on occupant behaviour cannot be considered an effective solution. Instead what is required is a holistic user-centric control strategy for building management (Jiang 2009). This study aims to provide greater insight into occupant activity patterns and their effect on energy consumption and IEQ in order to equip BMS with an effective user-centric energy and IEQ management strategy. This study proposes a framework to extract useful observations pertaining to occupant energy usage patterns by means of sensor-based monitoring, using the case study of a four-storey multi-family residential facility in Fort McMurray, Alberta, Canada. Previous research by the authors has involved extract useful information by studying correlations among occupant activities. In this study, the authors investigate occupant activities to determine whether any sequential activity patterns exist, as well as to identify any correlations among activities, or between activities and IEQ. It is expected that these observations will enhance BMS through effective energy and IEQ management. 2 OBJECTIVE AND METHODOLOGICAL APPROACH 2.1 Objective The objective of this study is to propose a methodological approach for extracting useful information from sensor-based monitoring for the purpose of user-centric energy and IEQ management of a multi-family residential facility. The study first aims to identify the most significant occupant activities affecting overall household energy consumption. Occupant energy usage patterns are also investigated for different unit types (different directional orientations and unit sizes), based upon which occupants can be categorized based on usage patterns. This study further considers identifying occupants’ sequential energy usage patterns and the correlations among them by analyzing time-ordered energy usage measurements. It is expected that these information will assist facility managers to understand occupant energy load and estimate future energy demand so they can plan accordingly. Facility managers will also be able to calculate internal heat gain from occupant activities in order to estimate optimized heating energy requirements. It is expected that this research will yield insightful observations of occupant energy usage, which may in turn be useful in shaping energy and IEQ management strategies. 2.2 Methodology To achieve the above mentioned objective, a sensor-based monitoring system is developed to measure occupant operating energy and IEQ. In order to investigate whether occupant energy usage patterns and IEQ are affected by unit characteristics, this study measures occupant usage patterns from different unit types. At each floor of the four-storey case study building, 1 ‘one-bedroom’ unit facing north, 1 ‘one-bedroom’ unit facing south, and 1 ‘two-bedroom’ unit facing north are selected as case units. The sensor network is designed such that sensors are installed and data is collected from these 12 case study units. In order to ensure anonymity, in this study, the case study units are assigned codes (numbered 1 through 12). Figure 1 shows a schemata of the sensors used and their locations in a case ‘one-bedroom’ unit. The locations and types of sensors installed in the ‘two-bedroom’ units are similar to those of the ‘one-bedroom’ units. Table 1 shows the data collected by the installed sensors. 296-2 Figure 4 shows that, for the above three groups, (representing both ‘one-bedroom’ units and ‘two-bedroom’ units), hot water tank (14%-35%), lighting (13%-19%), and refrigerators (10%-15%) are high energy consumers. It is observed that heating energy consumption is comparatively higher in ‘two-bedroom’ units (14%-18%) than in ‘one-bedroom’ units (2%-13%), presumably due to the larger size of these units. It is also found that south-facing units have lower heating consumption (2%-9%) than north-facing units; solar heat may be the reason for lower heating demand in the south-facing units. With the major energy consuming factors related to occupant usage patterns identified for a multi-family residential facility, it is possible for the facility manager to set energy usage limits for each unit. 3.2 Correlation and Sequential Energy Usage Pattern and their Association with IEQ A study of the daily energy usage data shows that different sequential activity patterns can be observed among occupants. For example, it is observed that when kitchen plugs are in use (presumably for the purpose of food preparation), the range and range hood fan are found to be in operation shortly afterward (Figure 5). It is also observed that during that time kitchen lighting is also turned on. Data also shows that when bathroom lighting is on, water consumption tends to take place during the same timeframe. These sequential activity patterns for specific units can be used to develop a predictive sequential energy usage pattern model. Facility managers can use this information for estimating future-state energy load for improved energy management.        Figure 5: Sequential activity pattern   Figure 6: Internal heat gain associated with activity pattern  296-7 Correlations across different activities and their association with IEQ can be observed from the measured data. For instance, it is also observed that during the time the above mentioned food preparation activities are performed, the internal heat gain from kitchen appliances increases the indoor temperature even though heating consumption is low for that specific time (Figure 6). Facility managers can take into account this information regarding internal heat gain while estimating heating load. Analyzing recorded data identifies that the apartments generally show peak electricity consumption during hot water consumption (Figure 7). Furthermore, when water consumption is high in a given unit, relative humidity is also high for that unit. It is also observed that the indoor CO2 level is significantly affected by occupant energy recovery ventilation usage patterns (Figure 8).     Figure 7: Peak electricity consumption and IEQ associated with water usage pattern    Figure 8: IEQ associated with occupants’ ERV usage pattern Cold water Hot water 296-8  Occupant presence and absence patterns can also be observed by studying the data. Figure 9 shows that occupants of unit 3 were not at home for two days (January 3-5, 2013). It is also observed that three Sundays of this month show higher CO2 concentration and heating and water consumption, which indicates higher demand during that time. This information can assist building managers to understand demand load for effective energy management.    Figure 9: Occupant presence-absence information  4 CONCLUSION The framework presented in this study demonstrates the use of real-time sensor-based data to support identification of occupant energy usage patterns and associated IEQ in order to improve energy and IEQ management. The knowledge generated from this study can be used to assess the demand load and timing of peaks loads. Aggregating the energy demand for all occupants in the multi-family building can assist with the development of an energy demand profile. Accordingly, the BMS can propose an appliance scheduling scheme based on time-varying retail pricing. This information can be used to estimate the internal heat gains (from occupants, office equipment, and lighting) in order to calculate the energy needed to maintain adequate comfort levels. Through the combination of these knowledge the BMS will be able to minimize the consumption of resources while maintaining a comfortable IEQ level. Acknowledgements The authors would like to thank Gurjeet Singh and Ahmed Alrifai (Research Engineers, University of Alberta), and Veselin Ganev, (graduate student, University of Alberta) for their valuable contributions to this research project. The authors also would like to thank the contributors who have funded or otherwise supported this research project: Cormode & Dickson Construction Ltd., Integrated Management and Realty Ltd., Hydraft Development Services Inc., TLJ Engineering Consultants, BCT Structures, and Wood Buffalo Housing and Development Corporation. References Abrahamse, W., Steg, L., Vlek Ch, and Rothengatter, T. 2007. The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviours, and behavioral antecedents. Journal of Environmental Psychology, 27(4): 265-76. Abrahamse, W., Steg, L., Vlek, C., and Rothengatter, T. 2005. A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25(3), 273-291. Branco, G., Lachal, B., Gallinelli, P., and Weber, W. 2004. Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings, 36(6): 543-555. 296-9 Building Air Quality. 1991. A Guide for Building Owners and Facility Managers, CDC, Centre for Disease Control and Prevention. Retrieved on January 11, 2015 from http://www.cdc.gov/search.do?queryText=factors+affecting+indoor+air+quality&searchButton.x=39&searchButton.y=6&action=search  Chetty, M., Tran, D., and Grinter, R.E. 2008. Getting to Green: Understanding Resource Consumption in the Home. Proc. UbiComp, New York, NY, USA. Darby, S. 2006. The Effectiveness of Feedback on Energy Consumption. Technical Report, Environmental Change Institute, University of Oxford, UK. Grini, C., Mathisen, H.-M., Sartori, I., Haase, M., Sørensen, H.W.J., Petersen, A., Bryn, I., and Wigenstad, T.. LECO – Energibruk i fem kontorbygg i Norge. Technical report, SINTEF, 2009.  Haas, R., Auer, H., and Biermayr, P. 1998. The impact of consumer behavior on residential energy demand for space heating. Energy and Buildings, 27(2): 195-205. Jeeninga, H., Uyterlimde, M., and Uitzinger, J. 2001. Energy Use of Energy Efficient Residences, Report ECN & IVAM.  Jiang, X., Van Ly, M., Taneja, J., Dutta, P., and Culler, D. 2009. Experiences with a High-Fidelity Wireless Building Energy Auditing Network. Proc. ACM SenSys 2009, Berkeley, CA, USA. Levinson, A. and Niemann, S. 2004. Energy use by apartment tenants when landlords pay for utilities. Resource and Energy Economics, 26(1): 51-75. Matz, C.J., Stieb, D.M., Davis, K., Egyed, M., Rose, A., Chou, B., and Brion, O. 2014. Effects of age,season, gender and urban-rural status on time-activity: Canadian human activity pattern survey 2 (CHAPS 2), International Journal of Environmental Research and Public Health, 11(2): 2108-2124. Retrieved on July 7, 2014 from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945588/ Papakostas, K.T. and Sotiropoulos, B.A. 1997. Occupational and energy behavior patterns in Greek residences. Energy and Buildings, 26(2): 207-213. Public Health Agency of Canada, Chronic Diseases in Canada, Volume 29 • Supplement 2 • 2010. Retrieved on January 11, 2015 from http://www.phac-aspc.gc.ca/publicat/cdic-mcbc/29-2-supp/ar_04-eng.php Quinn, P. 2011. Illinois Department of Public Health Guidelines for Indoor Air Quality. Illinois Department of Public Health, Environmental Health Fact Sheet. Retrieved on July 7, 2014 from http://www.idph.state.il.us/envhealth/factsheets/indoorairqualityguidefs.htm Sharmin, T., Gül, M., Li, X., Ganev, V., Nikolaidis, I., and Al-Hussein, M. 2014. Monitoring building energy consumption, thermal performance, and indoor air quality in a cold climate region. Sustainable Cities and Society, 13: 57-68. Sjögren J-U, Andersson S, and Olofsson T. 2007. An approach to evaluate the energy performance of buildings based on incomplete monthly data. Energy and Buildings, 39(8): 945-953. Statistics Canada. 2011. Households and the Environment: Energy Use. Retrieved on January 11, 2015 from http://www.statcan.gc.ca/pub/11-526-s/2013002/part-partie1-eng.htm  Statistics Canada. 2001 Households and the Environment. Retrieved on January 11, 2015 from http://www.statcan.gc.ca/pub/11-526-x/2013001/part-partie1-eng.htm  US Bureau of Labor Statistics. 2006. Retrieved on January 11, 2015 from www.bls.gov/oco/ US DOE Energy Information Administration. 2003. Commercial Buildings Energy Consumption Survey. Retrieved on January 11, 2015 from www.eia.doe.gov/emeu/cbecs Vassileva, I., Wallin, F., and Dahlquist, E. 2012. Analytical comparison between electricity consumption and behavioral characteristics of Swedish households in rented apartments. Applied Energy, 90(1): 182-188. Wood, G. and Newborough, M. 2003. Dynamic energy-consumption for domestic appliances: environment, behavior and design. Energy and Buildings, 35(8): 821-841. Wyon, P. and Wargocki, D.P. 2006. Indoor air quality effects on office work. In D.Clements- Croome (Ed.). The productive workplace. London: E&FN Spon. Yu, Z., Fung, B.C.M., Haghighat, F., Yoshino, H., and Morofsky, E. 2011. A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43(6): 1409-1417. 296-10  Presented By:Xinming LiDepartment of Civil & Environmental Engineering Hole School of ConstructionSharmin, T., Gül, M., Al-Hussein, M.The CSCE International Construction Specialty Conference, VancouveroutlineObjectiveMethodologyData Analysis & ObservationsConclusion & Future ScopeMotivation03/15motivationModern lifestyle requires operation of variety of appliances Operation of these appliances:• Affect indoor environmental quality (IEQ)• Consume energyHeating system consumes energy; contributes to indoor temperatureHot water usage consumes energy; contributes to generating RHLighting, electrical appliances consume energy; contribute to internal heat gain Presence of occupants contribute to internal heat gain, generating RH,CO2Oven (cooking) consumes energy; contributes to internal heat gain, generating RH04/15motivation & objectiveA methodological approach to extract useful information regardingoccupant energy usage pattern and their association with IEQ toassist facility manager for efficient management of occupant energyconsumption and IEQ Eliminating improper use ofappliances, can reduceapproximately 30% of energyconsumption [US DOE EnergyInformation Administration, 2003].O B J E C T I V E Canadian households consumeda total of 1,425,185 TJ of energy[Statistics Canada 2011].05/15methodology Selection of case study units Installation of sensors Occupant energy usage pattern study1. Identifying appliances with higherenergy load2. Identifying impact of unit characteristicson energy usage3. Identifying correlation among activitiesand IEQ4. Identifying occupant behaviouralpatternBuilding 1 Building 2Building 2 (55units)Building 1 (70 units)Type A Type BNBuilding 1: [case study] 13937 sq.ft.[1294.8 sq.m.] 12 case study units:8 one bed room units4 two bed room unitsselection of case study units 06/1507/15installation of sensors• Around 3.5 years of data• Around 33000 MB ofdata08/152-bedroom unit: North-facing1-bedroom unit: North-facing1-bedroom unit: South-facing• Hot water tank (up to 35%)• Artificial lighting (up to 19%)• Space heating (up to 18%)• Refrigerators (up to 15%)1. energy consumption load09/152. consumption pattern [unit types]10/15Hot waterCold water3. correlation [energy usage & ieq]Increased indoor temperature ERV usage decreases CO2 levelRHDecreased heating consumption11/15Interval patternSequential activity pattern Presence/absence pattern4. behavioural usage patternThursdaysTuesdaysPeriod of absences12/151. Some appliances (HWT, lighting, heating system) havehigher energy load compared to others Set usage limit, time of use, behavioural awareness2. Unit characteristics (orientation, unit size) affect occupantenergy usage pattern Set different usage limits for different user groups3. Occupant activities sometimes follow behavioral pattern:like sequence in activities, regular interval between the activities Appliance usage scheduling based on time varyingretail energy pricing Estimate future energy load4. Correlation exists between energy usage and IEQ Detecting the cause of non-standard IEQobservations13/15conclusion & future scopeEstimate optimum heating load considering predictive internal heat gainDetecting non-standard IEQ [control ERV usage, identify faulty appliance] Set energy usage limits for different user groupsAppliance usage scheduling considering peak usage time & time varying retail  energy pricing Cormode & Dickson Construction Ltd., Integrated Managementand Realty Ltd., Wood Buffalo Housing and DevelopmentCorporation, TLJ Engineering Consultants, BCT Structures,Hydraft Development Services Inc. and University of AlbertaacknowledgementThe authors also would like to thank the contributors who have funded or otherwise supported this research project:14/15

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