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A user-centric space heating energy management framework for multi-family residential facilities based on occupant pattern prediction modeling

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Abstract

Space heating is the highest energy consumer in the operation of residential facilities in cold regions. Energy saving measures for efficient space heating operation are thus of paramount importance in efforts to reduce energy consumption in buildings. For effective functioning of space heating systems, efficient facility management coupled with relevant occupant behaviour information is necessary. However, current practice in space heating control is event-driven rather than user-centric, and in most cases relevant occupant information is not incorporated into space heating energy management strategies. This causes system inefficiency during the occupancy phase. For multi-family residential facilities, integrating occupant information within space heating energy management strategies poses several challenges; unlike with commercial facilities, in multi-family facilities occupant behavior does not follow any fixed activity-schedule pattern. In this study, a framework is developed for extracting relevant information about the uncertainties pertaining to occupant patterns (i.e., demand load) in multi-family residential facilities by identifying the factors affecting space heating energy consumption. This is achieved using sensor-based data monitoring during the occupancy phase. Based on the analysis of the monitoring data, a structure is defined for developing an occupant pattern prediction model that can be integrated with energy management strategies to reduce energy usage in multi-family residential facilities. To demonstrate the developed framework, a multi-family residential building in Fort McMurray, Canada, is chosen as a case study. This paper shows that integrating the developed occupant pattern prediction model within space heating energy management strategies can assist facility managers to achieve space heating energy savings in multi-family residential facilities.

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References

  • Ahmad MW, Mourshed M, Yuce B, Rezgui Y (2016). Computational intelligence techniques for HVAC systems: A review. Building Simulation, 9: 359–398.

    Article  Google Scholar 

  • Aksamija A (2009). Integration in architectural design: Methods and implementations. Design Principles and Practices: An International Journal—Annual Review, 3(6): 151–160.

    Article  Google Scholar 

  • Andersen RV, Olesen B, Toftum J (2007). Simulation of the effects of occupant behavior on indoor climate and energy consumption. In: Proceedings of the 9th RHEVA World Congress, Helsinki, Finland.

    Google Scholar 

  • Augenbroe G, de Wilde P, Moon HJ, Malkawi A (2004). An interoperability workbench for design analysis integration. Energy and Buildings, 36: 737–748.

    Article  Google Scholar 

  • Bourgeois D, Reinhart C, 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: 814–823.

    Article  Google Scholar 

  • Brambley M, Hasen D, Haves P, Holmberg D, McDonald S, Roth K, Torcellini P (2005). Advanced sensors and controls for building applications: Market assessment and potential R&D pathways. Pacific Northwest National Lab. Report No. 15149.

    Book  Google Scholar 

  • Brohus H, Heiselberg P, Hesselholt A, Rasmussen H (2009). Application of partial safety factors in building energy performance assessment. In: Proceedings of the 11th International IBPSA Building Simulation Conference, Glasgow, UK, pp. 1014–1021.

    Google Scholar 

  • Cho SH, Zaheer-uddin M (2003). Predictive control of intermittently operated radiant floor heating systems. Energy and Conversion Management, 44: 1333–1342.

    Article  Google Scholar 

  • Degelman LO (1999). A model for simulation of daylighting and occupancy sensors as an energy control strategy for office buildings. In: Proceedings of the 6th International IBPSA Building Simulation Conference, Kyoto, Japan, pp. 571–578.

    Google Scholar 

  • DOE (2003). Commercial buildings energy consumption survey. U.S. Department of Energy, Energy Information Administration. Available at www.eia.doe.gov/emeu/cbecs. Accessed 1 Jul 2012.

    Google Scholar 

  • Dong B (2010). Integrated building heating, cooling and ventilation control. PhD Thesis, Carnegie Mellon University, USA.

    Google Scholar 

  • Dong B, Lam KP (2014). A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Building Simulation, 7: 89–106.

    Article  Google Scholar 

  • Dong B, Li Z, Mcfadden G (2015). An investigation on energy-related occupancy behavior for low-income residential buildings. Science and Technology for the Built Environment, 21: 892–901.

    Article  Google Scholar 

  • Dong B, Li Z, Rahman SMM, Vega R (2016). A hybrid model approach for forecasting future residential electricity consumption. Energy and Buildings, 117: 341–351.

    Article  Google Scholar 

  • Duong TV, Phung DQ, Bui HH, Venkatesh S (2006). Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR), Hong Kong, China, pp. 202–207.

    Chapter  Google Scholar 

  • Ghahramani A, Jazizadeh F, Becerik-Gerber B (2014). A knowledge based approach for selecting energy-aware and comfort-driven HVAC temperature set points. Energy and Buildings, 85: 536–548.

    Article  Google Scholar 

  • Ghahramani A, Tang C, Becerik-Gerber B (2015). An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling. Building and Environment, 92: 86–96.

    Article  Google Scholar 

  • Ghahramani A, Zhang K, Dutta K, Yang Z, Becerik-Gerber B (2016). Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings. Applied Energy, 165: 930–942.

    Article  Google Scholar 

  • Ghiaus C, Hazyuk I (2010). Calculation of optimal thermal load of intermittently heated buildings. Energy and Buildings, 42: 1248–1258.

    Article  Google Scholar 

  • Glicksman LR, Taub S (1997). Thermal and behavioral modeling of occupant-controlled heating, ventilating and air conditioning systems. Energy and Buildings, 25: 243–249.

    Article  Google Scholar 

  • Gwerder M, Tödli J (2005). Predictive control for integrated room automation. In: Proceedings of the 8th REHVA World Congress for Building Technologies, Lausanne, Switzerland.

    Google Scholar 

  • Grünenfelder WJ, Tödtli J (1985). The use of weather predictions and dynamic programming in the control of solar domestic hot water systems. In: Proceedings of the 3rd Mediterranean Electrotechnical Conference, Madrid, Spain.

    Google Scholar 

  • Han HJ, Jeon YI, Lim SH, Kim WW, Chen K (2010). New developments in illumination, heating and cooling technologies for energyefficient buildings. Energy, 35: 2647–2653.

    Article  Google Scholar 

  • Henze GP, Felsmann C, Knabe G (2004a). Evaluation of optimal control for active and passive building thermal storage. International Journal of Thermal Sciences, 43: 173–183.

    Article  Google Scholar 

  • Henze GP, Kalz DE, Felsmann C, Knabe G (2004b). Impact of forecasting accuracy on predictive optimal control of active and passive building thermal storage inventory. HVAC & Research, 10: 153–178.

    Article  Google Scholar 

  • Henze GP, Kalz DE, Liu S, Felsmann C (2005). Experimental analysis of model-based predictive optimal control for active and passive building thermal storage inventory. HVAC & Research, 11: 189–214.

    Article  Google Scholar 

  • Heydarian A, Carneiro J P, Gerber D, Becerik-Gerber B (2015). Immersive virtual environments, understanding the impact of design features and occupant choice upon lighting for building performance. Building and Environment, 89: 217–228.

    Article  Google Scholar 

  • Jazizadeh F, Ghahramani A, Becerik-Gerber B, Kichkaylo T, Orosz M (2014a). User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings. Energy and Buildings, 70: 398–410.

    Article  Google Scholar 

  • Jazizadeh F, Ghahramani A, Becerik-Gerber B, Kichkaylo T, Orosz M (2014b). Human-building interaction framework for personalized thermal comfort-driven systems in office buildings. ASCE Journal of Computing in Civil Engineering, 28: 2–16.

    Article  Google Scholar 

  • Khashe S, Heydarian A, Gerber D, Becerik-Gerber B, Hayes T, Wood W (2015). Influence of LEED branding on building occupants’ pro-environmental behavior. Building and Environment, 94: 477–488.

    Article  Google Scholar 

  • Lam KP, Zhao J, Ydstie EB, Wirick J, Qi M, Park J (2014). An Energy Plus whole building energy model calibration method for office buildings using occupant behavior data mining and empirical data. In: Proceedings of the 2014 ASHRAE/IBPSA-USA Building Simulation Conference, pp. 160–167.

    Google Scholar 

  • Li X (2013). A framework to improve the control system design for integrated residential heating systems in cold regions. Master Thesis, University of Alberta, Canada.

    Google Scholar 

  • Li X, Gül M, Sharmin T, Nikolaidis I, Al-Hussein M (2014). A framework to monitor the integrated multi-source space heating systems to improve the design of the control system. Energy and Buildings, 72: 398–410.

    Article  Google Scholar 

  • Li Z, Dong B, Vega R (2015). A hybrid model for electrical load forecasting: A new approach integrating data-mining with physicsbased models. ASHRAE Transactions, 121(2): 1–8.

    Google Scholar 

  • Ma Y (2012). Model predictive control for energy efficient buildings. PhD Thesis, University of California, Berkeley, USA.

    Google Scholar 

  • Ma Y, Borrelli F, Hencey B, Packard A, Bortoff S (2009). Model Predictive Control of thermal energy storage in building cooling systems. In: Proceedings of the 48th IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, Shanghai, China, pp. 392–397.

    Google Scholar 

  • Maasoumy M, Rosenberg C, Vincentelli AS, Callaway DS (2014). Model predictive control approach to online computation of demand-side flexibility of commercial buildings HVAC systems for supply following. In: Proceedings of the 2014 American Control Conference, Portland, OR, USA, pp. 1082–1089.

    Chapter  Google Scholar 

  • Mihalakakou G, Santamouris M, Tsangrassoulis A (2002). On the energy consumption in residential buildings. Energy and Buildings, 34: 727–736.

    Article  Google Scholar 

  • Natural Resources Canada (2006). Operating energy in buildings. Available at http://cn-sbs.cssbi.ca/operating-energy-buildings. Accessed 20 May 2012.

    Google Scholar 

  • Natural Resources Canada (2007). Average home energy usage. Available at http://oee.nrcan.gc.ca/publications/statistics/sheusummary07/sheu.cfm. Accessed 20 May 2012.

    Google Scholar 

  • Neumann C, Jacob D (2010). Results of the project building EQ. Tools and methods for linking EPBD and continuous commissioning. Available at http://www.buildingeq-online.net/fileadmin/user_upload/Results/BEQ_publishable_results_report_final_rev_100624.pdf. Accessed 20 Aug 2015.

    Google Scholar 

  • Newsham GR (1995). Lightswitch: A stochastic model for predicting office lighting energy consumption. In: Proceedings of Right Light Three: 3rd European Conference on Energy Efficient Lighting, Newcastle Upon Tyne, UK, pp. 59–66.

    Google Scholar 

  • Oldewurtel F, Parisio A, Jones CN, Gyalistras D, Gwerder M, Stauch V, Lehmann B, Morari M (2012). Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45: 15–27.

    Article  Google Scholar 

  • Page J, Robinson D, Morel N, Scartezzini JL (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40: 83–98.

    Article  Google Scholar 

  • Prívara S, Široký J, Ferkl L, Cigler J (2011). Model predictive control of a building heating system: The first experience. Energy and Buildings, 43: 564–572.

    Article  Google Scholar 

  • Reinhart CF (2004). Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy, 77: 15–28.

    Article  Google Scholar 

  • Richardson I, Thomson M, Infield D (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40: 1560–1566.

    Article  Google Scholar 

  • Rose J (2010). Modeling user behaviour in whole building simulation. Occupants influence on the energy consumption of Danish domestic buildings. DCE Technical Report No. 110.

    Google Scholar 

  • Roy A, Das SK, Basu K (2007). A predictive framework for locationaware resource management in smart homes. IEEE Transactions on Mobile Computing, 6: 1270–1283.

    Article  Google Scholar 

  • Sharmin T, Gül M, Li X, Ganev V, Nikolaidis I, 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.

    Article  Google Scholar 

  • Shi S, Zhao B (2016). Occupants’ interactions with windows in 8 residential apartments in Beijing and Nanjing, China. Building Simulation, 9: 221–231.

    Article  Google Scholar 

  • Shih O, Rowe A (2015). Occupancy estimation using ultrasonic chirps. In: Proceedings of the ACM/IEEE 6th International Conference on Cyber-Physical Systems, Seattle, USA, pp. 149–158.

    Chapter  Google Scholar 

  • Široký J, Oldewurtel F, Cigler J, Prívara S (2011). Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy, 88: 3079–3087.

    Article  Google Scholar 

  • Sossan F, Bindner H, Madsen, Torregrossa HD, Chamorro LR, Paolone M (2014). A model predictive control strategy for the space heating of a smart building including cogeneration of a fuel cell-electrolyzer system. Electrical Power & Energy Systems, 62: 879–889.

    Article  Google Scholar 

  • Statistics Canada (2007). Households and the environment: Energy use. Available at http://www.statcan.gc.ca/pub/11-526-s/2010001/part-partie1-eng.htm. Accessed 2 Jun 2012.

    Google Scholar 

  • Statistics Canada (2011). Households and the environment: Energy use. Available at http://www.statcan.gc.ca/pub/11-526-s/2013002/part-partie1-eng.htm. Accessed 11 Jan 2015.

    Google Scholar 

  • Straube J (2006). Green building and sustainability, building science digests, building science press. Available at http://www. buildingscience.com/documents/digests/bsd-005-green-building -and-sustainability. Accessed 11 Jul 2012.

    Google Scholar 

  • UMBC (2014). Energy Conservation. Available at http://www.umbc.edu/fm/energy-conservation. Accessed 11 Aug 2014.

    Google Scholar 

  • Underwood CP (1999). HVAC Control Systems: Modelling, Analysis and Design. London: Routledge.

    Book  Google Scholar 

  • Wang D, Federspiel CC, Rubinstein F (2005). Modeling occupancy in single person offices. Energy and Buildings, 37: 121–126.

    Article  Google Scholar 

  • Wetter M (2011). A view on future building system modeling and simulation. In: Hensen J, Lamberts R (eds), Building Performance Simulation for Design and Operation, London: Routledge.

    Google Scholar 

  • Yang Z, Li N, Becerik-Gerber B, Orosz M (2014). A systematic approach to occupancy modeling in ambient sensor-rich buildings. Simulation, 90: 960–977.

    Article  Google Scholar 

  • Yu Z, Haghighat F, Fung BCM, Yoshino H (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42: 1637–1646.

    Article  Google Scholar 

  • Zaraket T, Yannou B, Leroy Y, Minel S, Chapotot E (2015). An occupant-based energy consumption model for user-focused design of residential buildings. ASME Journal of Mechanical Design, 137(7): 071412.

    Article  Google Scholar 

  • Zhai ZJ, McNeill JS (2014). Roles of building simulation tools in sustainable building design. Building Simulation, 7: 107–109.

    Article  Google Scholar 

  • Zhao J, Lasternas B, Lam KP, Yun R, Loftness V (2014). Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energy and Buildings, 82: 341–355.

    Article  Google Scholar 

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Acknowledgements

The authors 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.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Hole School of Construction Engineering, University of Alberta, Edmonton, Canada

    Tanzia Sharmin & Mohamed Al-Hussein

  2. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada

    Mustafa Gül

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  1. Tanzia Sharmin
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  2. Mustafa Gül
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Correspondence to Mustafa Gül.

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Sharmin, T., Gül, M. & Al-Hussein, M. A user-centric space heating energy management framework for multi-family residential facilities based on occupant pattern prediction modeling. Build. Simul. 10, 899–916 (2017). https://doi.org/10.1007/s12273-017-0376-x

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  • DOI: https://doi.org/10.1007/s12273-017-0376-x

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