Occupant Behavior and Building Performance



People and building performance are intimately linked. This chapter focuses on the issue of occupant behavior; principally, its impact, and the influence of building performance on occupants. The early sections looked at how energy is consumed in buildings and identifies the range of occupant-interactive opportunity. The issue of post occupancy evaluation (POE) is covered, exposing the concept of the energy performance gap and why discrepancies occur. The emphasis then shifts toward building performance, particularly indoor environment, how it impinges on work productivity, and how it is measured. Later sections discuss occupant adaptation in achieving thermal comfort, in addition to, the role of energy management systems, smart-sensor networks, and data mining with occupant behavior as the backdrop. Finally, the chapter closes by looking at how occupants fit within the framework of building performance assessment. Learning outcomes: on successful completion of this chapter, readers will: (1) Appreciate the need to better understand how occupants behave in buildings due to their magnitude of impact on energy use; (2) Understand the range of occupant behavior including: interactive opportunity; (3) Understand adaptation in achieving indoor comfort and response to indoor environment and work productivity; (4) Gain insight into the role of POE and its importance in developing improvement cycles; (5) Grasp how technology and occupant behavior can be integrated to realize energy savings and increase the quality of indoor environments; and (6) Know about building performance assessment.


Building energy performance Energy management systems Occupant behavior Post occupancy evaluation 


  1. Alwaer H, Clements-Croome D (2010) Key performance indicators (KPIs) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings. Build Environ 45:799–807Google Scholar
  2. ASHRAE (2007) ANSI/ASHRAE Standard 105–2007, standard methods of measuring, expressing and comparing building energy performance. American Society of Heating, Refrigerating and Air-conditioning Engineers, Inc., AtlantaGoogle Scholar
  3. Auliciems A (1981) Toward a psychophysiological model of thermal perception. Int J Biometeorol 25:109–122CrossRefGoogle Scholar
  4. Bahaj A, James P (2007) Urban energy generation: the added value of photovoltaics in social housing. Renew Sustain Energy Rev 11:2121–2136CrossRefGoogle Scholar
  5. Baker N, Standeven M (1994) Comfort criteria for passively cooled buildings a pascool task. Renew Energy 5:977–984CrossRefGoogle Scholar
  6. Baker N, Steemers K (2000) Energy and environment in architecture: a technical design guide, E & FN Spon, LondonGoogle Scholar
  7. Bordass B, Cohen R, Standeven M, Leaman A (2001) Assessing building performance in use 3: energy performance of probe buildings. Build Res Inf 29:114–128CrossRefGoogle Scholar
  8. Bordass B, Cohen R, Field J (2004) Energy performance of non-domestic buildings—closing the credibility gap. In: International conference on improving energy efficiency in commercial buildings. Frankfurt, GermanyGoogle Scholar
  9. Brager GS, de Dear RJ (1998) Thermal adaptation in the built environment: a literature review. Energy Build 27:83–96CrossRefGoogle Scholar
  10. BRE (2011) The 40 percent symposium, building research establishment. RIBA, LondonGoogle Scholar
  11. Carbon Trust (2011) Closing the gap—lessons learned on realising the potential of low carbon building design. CTG047, LondonGoogle Scholar
  12. Choi H-H, Loftness V, Aziz A (2012) Post-occupancy evaluation of 20 office buildings as basis for future IEQ standards and guidelines. Energy Build 46:167–175CrossRefGoogle Scholar
  13. CIBSE (2006) CIBSE TM22, energy assessment and reporting method. The Chartered Institution of Building Services Engineers, LondonGoogle Scholar
  14. Clements-Croome D (2000) Creating the productive workplace. Taylor & Francis Group Ltd., OxfordGoogle Scholar
  15. Clements-Croome D (2004) Intelligent buildings. Thomas Telford Books, LondonGoogle Scholar
  16. Clements-Croome DJ, Li B (1995) Impact of indoor environment on productivity. Workplace comfort forum. RIBA, LondonGoogle Scholar
  17. Cohen J, Cohen P (1983) Applied multiple regression, correlation analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, Publishers, HillsdaleGoogle Scholar
  18. Darby S (2006) The effectiveness of feedback on energy consumption: a review for DEFRA of the literature on metering, billing and direct displays. Environmental Change Institute, OxfordGoogle Scholar
  19. DECC (2006) The carbon emissions reduction target (CERT). HM Government Department of Energy and Climate Change, HMSO, LondonGoogle Scholar
  20. de Dear RJ, Brager GS (1998) Developing an adaptive model of thermal comfort and preference. ASHRAE Trans 104:145–167Google Scholar
  21. Demanuele C, Tweddell T, Davies M (2010) Bridging the gap between predicted and actual energy performance in schools. In: World renewable energy congress XI, Abu Dhabi, 25–30 Sept 2010Google Scholar
  22. Dempster AP (1968) Upper and lower probabilities generated by a random closed interval. Ann Math Stat 39:145–167CrossRefGoogle Scholar
  23. Farshchi MA, Fisher N (2000) Emotion and the environment: the forgotten dimension. In: Clements-Croome D (ed) Creating the productive workplace. E & FN Spon, LondonGoogle Scholar
  24. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From Data Mining to Knowledge discovery. Advances in knowledge discovery and Data Mining. AAAI/MIT Press, Menlo ParkGoogle Scholar
  25. Fanger PO (1970) Thermal comfort. Danish Technical Press, CopenhagenGoogle Scholar
  26. Gagge AP, Stolwijk JAJ, Hardy JD (1967) Comfort and thermal sensation and associated physiological responses at various ambient temperatures. Environ Res 1:1–20Google Scholar
  27. Haldi F, Robinson D (2009) Interactions with window openings by office occupants. Build Environ 44(12):2378–2395CrossRefGoogle Scholar
  28. Heerwagen JH (1998) Productivity and well-being: what are the links? In: Proceedings of the American institute of architects conference on highly effective facilities, Cincinnati 12–14 March 1998Google Scholar
  29. Humphreys MA (1978) Outdoor temperatures and comfort indoors. Build Res Pract 6(2):92–105Google Scholar
  30. Ilgen DR, Schneider J (1991) In: Cooper C, Robertson A (eds), International review of industrial and organisational psychology, vol 6. Wiley, London Chapter 3, 71–108Google Scholar
  31. Li B (1998) Assessing the influence of indoor environment on self-reported productivity in offices. Ph.D. thesis, The University of Reading, ReadingGoogle Scholar
  32. Lim D, Yao R (2012) A combined engineering and statistical model of UK domestic appliance loads. In: proceedings Western load research association Spring conference 2012, Boise, Idaho, 7–9 March 2012Google Scholar
  33. Liu J, Yao R, Wang J, Li B (2012a) Occupants’ behavioral adaptation in workplaces with non-central heating and cooling systems. Appl Therm Eng 35:40–54. doi: 10.1016/j.applthermaleng.2011.09.037 CrossRefGoogle Scholar
  34. Liu J, Yao R, McCloy R (2012b) A method to weight three categories of adaptive thermal comfort. Energy Build 47:312–320CrossRefGoogle Scholar
  35. Mansouri I, Newborough M, Probert D (1996) Energy-consumption in UK households: impact of domestic electrical appliances. Appl Energy 54(3):211–285CrossRefGoogle Scholar
  36. Menezes AC, Cripps A, Bouchlaghem D, Buswell R (2011) Predicted vs. actual energy performance of non-domestic buildings: using post-occupancy evaluation data to reduce the performance gap. Appl Energy. doi: 10.1016/j.apenergy.2011.11.075 Google Scholar
  37. Nishiara N, Yamamoto Y, Tanabe S (2002) Effect of thermal environment on productivity evaluated by task peformances, fatigue feelings and cerebral blood oxygenation changes, In: Proceedings: 9th international conference in indoor air quality and climate, vol 1. Monterey, 30 June–5 July 2002, pp 828–833Google Scholar
  38. Ouyang J, Hokao K (2009) Energy-saving potential by improving occupants’ behavior in urban residential sector in Hangzhou City, China. Energy Build 41:711–720CrossRefGoogle Scholar
  39. Probe (2011) Archive held by the usable buildings trust (UBT). Accessed 17 Feb 2012
  40. Saaty TL (1972) The analytic hierarchy process. McGraw-Hill, New YorkGoogle Scholar
  41. Seligman C, Darley JM, Becker LJ (1978) Behavioral approaches to residential energy conservation. Energy Build 1:325–337CrossRefGoogle Scholar
  42. Shafer G (1976) A mathematical theory of evidence. Princeton University Press, PrincetonMATHGoogle Scholar
  43. Shiomi K, Hirose S (2000) Fatigue and drowsiness predictor for pilots and air traffic controllers. In: Proceedings 45th annual ACTA conference, pp 1–4Google Scholar
  44. Soclow R (1978) Saving energy in the home: Princeton’s experiments at Twin Rivers. Ballinger Publishing Company, CambridgeGoogle Scholar
  45. Van Dam SS, Bakker CA, van Hal JDM, Keyson DV (2010) Knowledge and Learning for sustainable innovation. In: ERSCP-EMSU conference, Delft, The Netherlands, 25–29 Oct 2010Google Scholar
  46. Van Raaij WF, Verhallen TMM (1982) A behavioral model of residential energy use. J Econ Psychol 3:39–63CrossRefGoogle Scholar
  47. VDI (2007) VDI 3807, Part 1, characteristic values of energy and water consumption values in buildings—fundamentals. The Association of German Engineers, Dussseldorf (Verein Deutscher Ingenieure e.V)Google Scholar
  48. Wong NH, Jan WLS (2003) Total building performance evaluation of academic institution in Singapore. Build Environ 38:161–176CrossRefGoogle Scholar
  49. Wood G, Newborough M (2003) Dynamic energy-consumption indicators for domestic appliances: environment, behavior and design. Energy Build 35:821–841CrossRefGoogle Scholar
  50. World Business Council for Sustainable Development (2008) Global trends in energy efficient buildings. Accessed 13 Feb 2012
  51. Wu S, Clements-Croome D (2007) Understanding the indoor environment through mining sensory data—a case study. Energy Build 39:1183–1191CrossRefGoogle Scholar
  52. Wyon DP (1996) Indoor environmental effects on productivity. In: Proceedings of IAQ’96, ASHRAE, AtlantaGoogle Scholar
  53. Yao R, Steemers K (2005) A method of formulating energy load profile for domestic buildings in the UK. Energy Build 37:663–671CrossRefGoogle Scholar
  54. Yao R, Li B, Liu J (2009) A theoretical adaptive model of thermal comfort—adaptive predicted mean vote (aPMV). Build Environ 44:2089–2096CrossRefGoogle Scholar
  55. Yao R, Zheng J (2010) A model of intelligent building energy management for the indoor environment. Intell Build Int 2:72–80CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Faculty of Urban Construction and Environmental EngineeringChongqing UniversityShapingba, ChongqingChina
  2. 2.School of Construction Management and Engineering The University of ReadingReadingUK

Personalised recommendations