Personal and Ubiquitous Computing

, Volume 21, Issue 3, pp 521–535 | Cite as

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

  • Dimosthenis Ioannidis
  • Marina Vidaurre-Arbizu
  • Cesar Martin-Gomez
  • Stelios Krinidis
  • Ioannis Moschos
  • Amaia Zuazua-Ros
  • Dimitrios Tzovaras
  • Spiridon Likothanassis
Original Article
First Online:
Received:
Accepted:

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.

Keywords

Occupancy Diversity factors Business process model Energy performance Building simulation Depth cameras 
This is a preview of subscription content, log in to check access.

Notes

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.

References

  1. 1.
    Dodier R, Henze G, Tiller D, Guo X (2006) Building occupancy detection through sensor belief networks. Energy Build 38(9):1033–1043CrossRefGoogle 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 67:587–595CrossRefGoogle 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 conferenceGoogle Scholar
  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 1494–1500Google Scholar
  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 78:113–122CrossRefGoogle 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):814–823CrossRefGoogle Scholar
  8. 8.
    American Society of Heating R, Engineers, AC (2013) Energy efficient design of new buildings except low-rise residential buildings. ANSI, AtlantaGoogle Scholar
  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 190–199Google Scholar
  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 3130–3135Google Scholar
  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):1038–1046CrossRefGoogle Scholar
  13. 13.
    Mahdavi A (2009) Patterns and implications of user control actions in buildings. Indoor Built Environ 18(5):440–446CrossRefGoogle 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):57–65CrossRefGoogle 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 241–244Google Scholar
  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 537–544Google Scholar
  17. 17.
    Wang D, Federspiel CC, Rubinstein F (2005) Modeling occupancy in single person offices. Energy Build 37(2):121–126CrossRefGoogle 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 309–316Google Scholar
  19. 19.
    Tabak V (2009) System for office building usage simulation. Dissertation, TU EindhovenGoogle Scholar
  20. 20.
    Goldstein R, Tessier A, Khan A (2010) Schedule-calibrated occupant behavior simulation. In: Symposium on simulation for architecture and urban design. pp 79–86Google Scholar
  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 21:148–160CrossRefGoogle 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 conferenceGoogle Scholar
  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):23–32CrossRefGoogle Scholar
  24. 24.
    Wilde PD (2014) The gap between predicted and measured energy performance of buildings: a framework for investigation. Autom Constr 41(7):40–49CrossRefGoogle 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 97:355–364CrossRefGoogle 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):135–144CrossRefGoogle 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):217–224Google 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):1–28CrossRefGoogle 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 61:100–107CrossRefGoogle 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 114:475–184CrossRefGoogle 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 224Google Scholar
  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 7Google Scholar
  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 587–592Google Scholar
  34. 34.
    Davis J, Nutter D (2010) Occupancy diversity factors for common university building types. Energy Build 42(9):1543–1551CrossRefGoogle 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 communicationsGoogle Scholar
  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). AthensGoogle Scholar
  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 805–811Google Scholar
  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):119–133Google 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 15Google Scholar
  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 23:151–169CrossRefGoogle Scholar
  42. 42.
    UAH: The central limit theorem. http://www.math.uah.edu/stat/sample/CLT.html/. Accessed on 02 Dec 2016

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Dimosthenis Ioannidis
    • 1
    • 3
  • Marina Vidaurre-Arbizu
    • 2
  • Cesar Martin-Gomez
    • 2
  • Stelios Krinidis
    • 1
  • Ioannis Moschos
    • 1
  • Amaia Zuazua-Ros
    • 2
  • Dimitrios Tzovaras
    • 1
  • Spiridon Likothanassis
    • 3
  1. 1.Information Technologies InstituteCentre for Research and Technology HellasThermi, ThessaloníkiGreece
  2. 2.Department of Construction, Building Services and StructuresUniversidad de NavarraPamplonaSpain
  3. 3.Computer Engineering and InformaticsUniversity of PatrasRio, PatrasGreece

Personalised recommendations