Comparison of detailed occupancy profile generative methods to published standard diversity profiles

Abstract

Occupancy schedules in building spaces play an important role in evaluating a building’s energy performance. This work seeks to identify disparities between different occupancy estimation techniques; standardised occupancy profiles found in literature, business processes’ based profiles through interviews and accurate profiles from real on-field measurements. The occupancy diversity profiles of secondary spaces in a healthcare facility building are analysed through descriptive statistics and t test methods over different time horizons. Occupancy measurements are obtained by utilising a novel, robust and highly accurate real-time occupancy extraction system which is established through a network of depth cameras. Results indicate that the utilisation of real occupancy data, along with elaboration of the business processes that take place in building spaces have the potential to support more precise profiles in Building Performance Simulation software tools.

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References

  1. 1.

    Dodier R, Henze G, Tiller D, Guo X (2006) Building occupancy detection through sensor belief networks. Energy Build 38(9):1033–1043

    Article  Google Scholar 

  2. 2.

    Duarte C, Wymelenberg KVD, Rieger C (2013) Revealing occupancy patterns in an office building through the use of occupancy sensor data. Energy Build 67:587–595

    Article  Google Scholar 

  3. 3.

    IEA: Annex 66 definition and simulation of occupant behavior in buildings. http://www.annex66.org/. Accessed on 06 Oct 2016

  4. 4.

    Lam K, Hoynck M, Dong B, Andrews B, Chiou Y, Zhang R, Benitez D, Choi J (2009) Occupancy detection through an extensive environmental sensor network in an open-plan office building. In: Proceedings of the 11th IEEE international IBPSA conference

  5. 5.

    Meyn S, Surana A, Lin Y, Oggianu SM, Narayanan S, Frewen TA (2009) A sensor-utility-network method for estimation of occupancy in buildings. In: Joint 48th IEEE conference on decision and control and 28th Chinese control conference, pp 1494–1500

  6. 6.

    Yang Z, Becerik-Gerber B (2014) The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use. Energy Build 78:113–122

    Article  Google Scholar 

  7. 7.

    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 Build 38(7):814–823

    Article  Google Scholar 

  8. 8.

    American Society of Heating R, Engineers, AC (2013) Energy efficient design of new buildings except low-rise residential buildings. ANSI, Atlanta

  9. 9.

    NREL: Openstudio. https://openstudio.nrel.gov/. Accessed on 06 Oct 2016

  10. 10.

    Kashif A, Le XHB, Dugdale J, Ploix S (2011) Agent based framework to simulate inhabitants. In: Proceedings of the 3rd international conference on agents and artificial intelligence. pp 190–199

  11. 11.

    Liao C, Barooah P, Liao CLC (2015) An integrated approach to occupancy modeling and estimation in commercial buildings. In: American control conference (ACC), pp 3130–3135

  12. 12.

    Dong B, Andrews B, Lam KP, Hynck M, Zhang R, Chiou YS, Benitez D (2010) An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network. Energy Build 42(7):1038–1046

    Article  Google Scholar 

  13. 13.

    Mahdavi A (2009) Patterns and implications of user control actions in buildings. Indoor Built Environ 18(5):440–446

    Article  Google Scholar 

  14. 14.

    Mahdavi A, Mohammadi A, Kabir E, Lambeva L (2008) Occupants operation of lighting and shading systems in office buildings. Build Perform Simul 1(1):57–65

    Article  Google Scholar 

  15. 15.

    Hutchins J, Ihler A, Smyht P, Smyth P (2007) Modeling count data from multiple sensors: a building occupancy model. In: Computational advances in multi-sensor adaptive processing 2nd IEEE international workshop. pp 241–244

  16. 16.

    Mahdavi A, Proglhof C (2009) Toward empirically-based models of peoples presence and actions in buildings. In: Proceedings of the 11th international IBPSA conference. pp 537–544

  17. 17.

    Wang D, Federspiel CC, Rubinstein F (2005) Modeling occupancy in single person offices. Energy Build 37(2):121–126

    Article  Google Scholar 

  18. 18.

    Zimmermann G (2006) Modelling and simulation of dynamic user behavior in buildings: a lighting control case study. In: Proceedings of the 6th European conference on product and process modelling. pp 309–316

  19. 19.

    Tabak V (2009) System for office building usage simulation. Dissertation, TU Eindhoven

  20. 20.

    Goldstein R, Tessier A, Khan A (2010) Schedule-calibrated occupant behavior simulation. In: Symposium on simulation for architecture and urban design. pp 79–86

  21. 21.

    Shen W, Shen Q, Sun Q (2012) Building information modeling-based user activity simulation and evaluation method for improving designer user communications. Autom Constr 21:148–160

    Article  Google Scholar 

  22. 22.

    Tahmasebi F, Mostofi S, Mahdavi A (2015) Exploring the implications of different occupancy modelling approaches for building performance simulation results. In: Proceedings of the 6th international building physics conference

  23. 23.

    Chang WK, Hong T (2013) Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data. Build Simul 6(1):23–32

    Article  Google Scholar 

  24. 24.

    Wilde PD (2014) The gap between predicted and measured energy performance of buildings: a framework for investigation. Autom Constr 41(7):40–49

    Article  Google Scholar 

  25. 25.

    Menezes AC, Cripps A, Bouchlaghem D, Buswell R (2012) Predicted vs. actual energy performance of non-domestic buildings: using post-occupancy evaluation data to reduce the performance gap. Appl Energy 97:355–364

    Article  Google Scholar 

  26. 26.

    Caucheteux A, Sabar AE, Boucher V (2013) Occupancy measurement in building: a literature review, application on an energy efficiency research demonstrated building. Metrol Qual Eng 4(2):135–144

    Article  Google Scholar 

  27. 27.

    Egan AM (2012) Occupancy of australian office buildings: How accurate are typical assumptions used in energy performance simulation and what is the impact of inaccuracy. ASHRAE Trans 118(1):217–224

    Google Scholar 

  28. 28.

    Erickson VL, Carreira-Perpinan MA, Cerpa AE (2014) Analysis of building energy consumption parameters and energy savings measurement and verification by applying equest software. ACM Trans Sens Netw 10(3):1–28

    Article  Google Scholar 

  29. 29.

    Ke M, Yeh C, Jian J (2013) Analysis of building energy consumption parameters and energy savings measurement and verification by applying equest software. Energy Build 61:100–107

    Article  Google Scholar 

  30. 30.

    Eguaras-Martinez M, Vidaurre-Arbizu M, Martin-Gomez C (2014) Simulation and evaluation of building information modeling in a real pilot site. Appl Energy 114:475–184

    Article  Google Scholar 

  31. 31.

    Kuutti J, Saarikko P, Sepponen RE (2014) Real time building zone occupancy detection and activity visualization utilizing a visitor counting sensor network. In: Proceedings of the 11th international conference on remote engineering and virtual instrumentation (REV). p 224

  32. 32.

    Erickson VL, Cerpa AE (2010) Occupancy based demand response hvac control strategy. In: Proceedings of the 2nd ACM workshop on embedded sensing systems for energy-efficiency in building—BuildSys 10. p 7

  33. 33.

    Li S, Li N, Becerik-Gerber B, Calis G (2011) Rfid-based occupancy detection solution for optimizing hvac energy consumption. In: Proceedings of the 28th ISARC, Seoul, pp 587–592

  34. 34.

    Davis J, Nutter D (2010) Occupancy diversity factors for common university building types. Energy Build 42(9):1543–1551

    Article  Google Scholar 

  35. 35.

    Ghai S, Thanayankizil LV, Seetharam DP, Chakraborty D (2012) Occupancy detection in commercial buildings using opportunistic context sources. In: IEEE international conference in pervasive computing and communications

  36. 36.

    Krinidis S, Stavropoulos G, Ioannidis D, Tzovaras D (2014) A robust and real-time multi-space occupancy extraction system exploiting privacy-preserving sensors. In: Proceedings of the international symposium on communications, control, and signal processing (ISCCSP’14). Athens

  37. 37.

    US Department of energy: Energyplus energy simulation software. http://apps1.eere.energy.gov/buildings/energyplus/. Accessed on 06 Oct 2016

  38. 38.

    Eguaras-Martinez M, Martin-Gomez C, Vidaurre-Arbizu M, Brennan T, Krinidis S, Ioannidis D, Tzovaras D (2015) Architectural simulation of the integration of building information modelling (BIM) and business process modelling (BPM). In: Mahdavi A, Martens B, Schrerer R (eds) eWork and eBusiness in Architecture, Engineering and Construction, vol. 1. London, pp 805–811

  39. 39.

    Martin-Gomez C, Vidaurre-Arbizu M, Eguaras-Martinez M, Krinidis S, Ioannidis D, Tzovaras D (2014) Sensor placement for BPM analysis of buildings in use to implement energy savings through building performance simulation. Eng Archit 2(2):119–133

    Google Scholar 

  40. 40.

    Ioannidis D, Krinidis S, Stavropoulos G, Tzovaras D, Likothanassis S (2014) Full-automated acquisition system for occupancy and energy measurement data extraction. In: SimAUD 14 proceedings of the symposium on simulation for architecture and urban design. p 15

  41. 41.

    Lumley T, Diehr P, Emerson S, Chen L (2002) The importance of the normality assumption in large public health data sets. Annu Rev Public Health 23:151–169

    Article  Google Scholar 

  42. 42.

    UAH: The central limit theorem. http://www.math.uah.edu/stat/sample/CLT.html/. Accessed on 02 Dec 2016

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Acknowledgements

This work has been partially supported by the European Commission through the projects FP7 ICT STREP-288150-Adapt4EE and HORIZON 2020-RESEARCH and INNOVATION ACTIONS (RIA)-696129-GREENSOUL.

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Correspondence to Stelios Krinidis.

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Ioannidis, D., Vidaurre-Arbizu, M., Martin-Gomez, C. et al. Comparison of detailed occupancy profile generative methods to published standard diversity profiles. Pers Ubiquit Comput 21, 521–535 (2017). https://doi.org/10.1007/s00779-017-1013-5

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Keywords

  • Occupancy
  • Diversity factors
  • Business process model
  • Energy performance
  • Building simulation
  • Depth cameras