Abstract
The role that occupants have on energy consumption and performance of buildings is known, but still requires a great deal of research. In this paper, the most common techniques to detect occupancy and occupant behavior in buildings are categorized with their advantages and disadvantages. Being the buildings characterized by different energy usage, the presentation of the studies that applied surveys and monitoring campaigns is conducted with a differentiation between residential and office buildings.References
IEA. Energy Efficiency 2017. Paris: 2017. doi:10.1787/9789264284234-en.
European Union. Directive 2010/31/EU on the energy performance of buildings (recast).
UCLA Samueli School Of Engineering. Phase Change Composite Materials for Energy Efficient Building Envelopes. Viewed 10 November 2018, https://www.seas.ucla.edu/~pilon/PCMIntro.html.
Pereira PF, Ramos NMM, Almeida RMSF, et al. Methodology for detection of occupant actions in residential buildings using indoor environment monitoring systems. Building and Environment 2018;146:107–18. doi:10.1016/j.buildenv.2018.09.047.
Brager GS, Paliaga G, de Dear R. Operable Windows, Personal Control and comfort. ASHRAE Transactions 4695 2004;110:17–35.
Branco G, Lachal B, Gallinelli P, et al. Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data. Energy and Buildings 2004;36:543–55. doi:10.1016/j.enbuild.2004.01.028.
Lindén AL, Carlsson-Kanyama A, Eriksson B. Efficient and inefficient aspects of residential energy behaviour: What are the policy instruments for change? Energy Policy 2006;34:1918–27. doi:10.1016/j.enpol.2005.01.015.
Zhang Y, Bai X, Mills FP, et al. Rethinking the role of occupant behavior in building energy performance: A review. Energy & Buildings 2018;172:279–94. doi:10.1016/j.enbuild.2018.05.017.
Labeodan T, Zeiler W, Boxem G, et al. Occupancy measurement in commercial office buildings for demand-driven control applications - A survey and detection system evaluation. Energy and Buildings 2015;93:303–14. doi:10.1016/j.enbuild.2015.02.028.
Technical Report: Studying Occupant Behavior in Buildings: Methods and Challenges. Annex 66: Definition and Simulation of Occupant Behavior in Buildings, 2017.
De Simone M, Carpino C, Mora D, et al. Reference procedures for obtaining occupancy profiles in residential buildings. IEA EBC Annex 66 – Subtask A Deliverable, 2018, p. 1–5.
Carpino C, Fajilla G, Gaudio A, et al. Application of survey on energy consumption and occupancy in residential buildings. An experience in Southern Italy. Energy Procedia 2018;148C:1082–9. doi:10.1016/j.egypro.2018.08.051.
Barbosa J, Mateus R, Bragança L. Occupancy Patterns and Building Performance – Developing occupancy patterns for Portuguese residential buildings. Sustainable Urban Communities towards a Nearly Zero Impact Built Environment 2016. doi:10.13140/RG.2.2.27296.58881.
Aerts D, Minnen J, Glorieux I, et al. A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison. Building and Environment 2014;75:67–78. doi:10.1016/j.buildenv.2014.01.021.
Widén J, Wäckelgård E. A high-resolution stochastic model of domestic activity patterns and electricity demand. Applied Energy 2010;87:1880–92. doi:10.1016/j.apenergy.2009.11.006.
Wilke U, Haldi F, Scartezzini JL, et al. A bottom-up stochastic model to predict building occupants’ time-dependent activities. Building and Environment 2013;60:254–64. doi:10.1016/j.buildenv.2012.10.021.
Richardson I, Thomson M, Infield D. A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings 2008;40:1560–6. doi:10.1016/j.enbuild.2008.02.006.
Buttitta G, Turner W, Finn D. Clustering of Household Occupancy Profiles for Archetype Building Models. Energy Procedia 2017;111:161–70. doi:10.1016/j.egypro.2017.03.018.
Albert A, Rajagopal R. Smart Meter Driven Segmentation: What Your Consumption Says About You. IEEE Transactions on Power Systems 2013;28:4019–30.
Akbar A, Nati M, Carrez F, et al. Contextual occupancy detection for smart office by pattern recognition of electricity consumption data. IEEE International Conference on Communications 2015;2015–Septe:561–6. doi:10.1109/ICC.2015.7248381.
Lee S, Chon Y, Kim Y, et al. Occupancy prediction algorithms for thermostat control systems using mobile devices. IEEE Transactions on Smart Grid 2013;4:1332–40. doi:10.1109/TSG.2013.2247072.
von Bomhard T, Wörner D, Röschlin M. Towards smart individual-room heating for residential buildings. Computer Science - Research and Development 2016;31:127–34. doi:10.1007/s00450-014-0282-8.
V. Callaghan, M. Colley, H. Hagras, et al. Programming iSpaces: A Tale of Two Paradigms. Programming iSpaces: A Tale of Two Paradigms, Springer-Verlag Book; 2005.
eDIANA Project 2011. Viewed 10 November 2018, https://artemis-ia.eu/project/11-ediana.html.
AIM Project 2011. Viewed 10 November 2018, https://setis.ec.europa.eu/energy-research/sites/default/files/static-projects/files/AIM-FPSR-v1-0.pdf.
O’Brien W, Gunay HB. The contextual factors contributing to occupants’ adaptive comfort behaviors in offices - A review and proposed modeling framework. Building and Environment 2014;77:77–88. doi:10.1016/j.buildenv.2014.03.024.
D’Oca S, Chen CF, Hong T, et al. Synthesizing building physics with social psychology: An interdisciplinary framework for context and occupant behavior in office buildings. Energy Research and Social Science 2017;34:240–51. doi:10.1016/j.erss.2017.08.002.
Antoniadou P, Papadopoulos AM. Occupants’ thermal comfort: State of the art and the prospects of personalized assessment in office buildings. Energy and Buildings 2017;153:136–49. doi:10.1016/j.enbuild.2017.08.001.
Bluyssen PM, Aries M, van Dommelen P. Comfort of workers in office buildings: The European HOPE project. Building and Environment 2011;46:280–8. doi:10.1016/j.buildenv.2010.07.024.
Roulet C-A, Johner N, Foradini F, et al. Perceived health and comfort in relation to energy use and building characteristics. Building Research & Information 2006;34:467–74. doi:10.1080/09613210600822279.
Karyono TH, Wonohardjo S, Soelami FN, et al. Report on thermal comfort study in Bandung, Indonesia. Proceedings of International Conference ’Comfort and Energy Use in Building Getting Them Right 2006:1–9.
Indraganti M, Ooka R, Rijal HB. Thermal comfort in offices in India: Behavioral adaptation and the effect of age and gender. Energy and Buildings 2015;103:284–95. doi:10.1016/j.enbuild.2015.05.042.
Dong B, Kjærgaard MB, De Simone M, et al. Sensing and data acquisition. Exploring Occupant Behavior in Buildings - Methods and Challenges. Springer, 2017, p. 77–105.
Li N, Calis G, Becerik-Gerber B. Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations. Automation in Construction 2012;24:89–99. doi:10.1016/j.autcon.2012.02.013.
Chen Z, Masood MK, Soh YC. A fusion framework for occupancy estimation in office buildings based on environmental sensor data. Energy and Buildings 2016;133:790–8. doi:10.1016/j.enbuild.2016.10.030.
Caucheteux A, Es Sabar A, Boucher V. Occupancy measurement in building: A litterature review, application on an energy efficiency research demonstrated building. International Journal of Metrology and Quality Engineering 2013;4:135–44. doi:10.1051/ijmqe/2013044.
Chen Z, Jiang C, Xie L. Building occupancy estimation and detection: A review. Energy and Buildings 2018;169:260–70. doi:10.1016/j.enbuild.2018.03.084.
Hailemariam E, Goldstein R, Attar R, et al. Real-time occupancy detection using decision trees with multiple sensor types. Procs of the 2011 Symposium on Simulation for Architecture and Urban Design 2011:141–8.
Ni LM, Liu Y, Lau YC, et al. LANDMARC: Indoor Location Sensing Using Active RFID. Wireless Networks 2004;10:701–10. doi:10.1023/B:WINE.0000044029.06344.dd.
Zou J, Zhao Q, Yang W, et al. Occupancy detection in the office by analyzing surveillance videos and its application to building energy conservation. Energy and Buildings 2017;152:385–98. doi:10.1016/j.enbuild.2017.07.064.
Mora D, De Simone M, Fajilla G, et al. Occupancy profiles modelling based on indoor measurements and clustering analysis : Application in an office building. ESTEC Conference Proceedings 6th Engineering, Science and Technology Conference 2018;2018:711–20. doi:10.18502/keg.v3i1.1474.
Dong B, Andrews B, Lam KP, et al. An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network. Energy and Buildings 2010;42:1038–46. doi:10.1016/j.enbuild.2010.01.016.
Wang W, Chen J, Hong T. Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings. Automation in Construction 2018;94:233–43. doi:10.1016/j.autcon.2018.07.007.
Ekwevugbe T, Brown N, Pakka V, et al. Improved occupancy monitoring in non-domestic buildings. Sustainable Cities and Society 2017;30:97–107. doi:10.1016/j.scs.2017.01.003.