State of the Art

  • Pedro F. PereiraEmail author
  • Nuno M. M. Ramos
  • João M. P. Q. Delgado
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


The present book was the result of an extensive bibliographical research in order to collect information on the state of the art of intelligent buildings and the behaviour of their occupants.


Intelligent Building Occupational Behavior Heating, Ventilation And Air Conditioning System (HVAC) Reed Switch Sensors Indoor Relative Humidity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 2010/31/EU, Directive (2010) Energy Performance of Buildings Directive (EPBD)Google Scholar
  2. Aerts D, Minnen J, Glorieux I, Wouters I, Descamps F (2013) Discrete occupancy profiles from time-use data for user behaviour modelling in homes. Comunicação apresentada em Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation AssociationGoogle Scholar
  3. Afonso MJRS (2007) Paradigmas Diferencial e Sistémico de Investigação da Inteligência Humana. Perspectivas sobre o lugar e o sentido do construto. Faculdade de Psicologia e de Ciências da Educação, Universidade de LisboaGoogle Scholar
  4. Ahmed HS, Faouzi BM, Caelen J (2013) Detection and classification of the behaviour of people in an intelligent building by camera. Int J Smart Sens Intell Syst 6(4):1317–1342Google Scholar
  5. Ali AS, Zanzinger Z, Debose D, Stephens B (2016) Open Source Building Science Sensors (OSBSS): a low-cost Arduino-based platform for long-term indoor environmental data collection. Build Environ 100:114–126. Scholar
  6. Andersen RK (2012) The influence of occupants’ behaviour on energy consumption investigated in 290 identical dwellings and in 35 apartments. Comunicação apresentada em 10th International Conference on Healthy Buildings, em Brisbane, AustraliaGoogle Scholar
  7. Andersen RV, Toftum J, Andersen KK, Olesen BW (2009) Survey of occupant behaviour and control of indoor environment in Danish dwellings. Energy Build 41(1):11–16. Scholar
  8. Antretter F, Mayer C, Wellisch U (2011) An approach for a statistical model for the user behaviour regarding window ventilation in residential buildings. Comunicação apresentada em 12th Conference of International Building Performance Simulation Association, em SydneyGoogle Scholar
  9. Bao K, Allerding F, Schmeck H (2011) User behaviour prediction for energy management in smart homes. Comunicação apresentada em Proceedings—2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011Google Scholar
  10. Barbato A, Borsani L, Capone A, Melzi S (2009) Home energy saving through a user profiling system based on wireless sensors. Comunicação apresentada em 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BUILDSYS 2009, in Conjunction with ACM SenSys 2009, em Berkeley, CAGoogle Scholar
  11. Bekö G, Lund T, Nors F, Toftum J, Clausen G (2010) Ventilation rates in the bedrooms of 500 Danish children. Build Environ 45(10):2289–2295. Scholar
  12. Bonte M, Thellier F, Lartigue B (2014) Impact of occupant’s actions on energy building performance and thermal sensation. Energy Build 76:219–227. Scholar
  13. 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. Scholar
  14. Brassier P, Adell G, Salmon N (2014) Energy consumption feedback to users: lessons learnt from pilot cases in social housing and tertiary buildings. Comunicação apresentada em BEHAVE2014—Behaviour and Energy Efficiency ConferenceGoogle Scholar
  15. Calì D, Andersen RK, Müller D, Olesen BW (2016a) Analysis of occupants’ behaviour related to the use of windows in German households. Build Environ 103:54–69. Scholar
  16. Calì D, Osterhage T, Streblow R, Müller D (2016b) Energy performance gap in refurbished German dwellings: lesson learned from a field test. Energy Build 127:1146–1158. Scholar
  17. Candanedo LM, Feldheim V, Deramaix D (2017) A methodology based on Hidden Markov Models for occupancy detection and a case study in a low energy residential building”. Energy Build 148:327–341. Scholar
  18. Chen C, Cook DJ, Crandall AS (2013) The user side of sustainability: modeling behaviour and energy usage in the home. Pervasive Mobile Comput 9(1):161–175. Scholar
  19. Cheng V, Steemers K (2011) Modelling domestic energy consumption at district scale: a tool to support national and local energy policies. Environ Model Softw 26(10):1186–1198. Scholar
  20. Clear RD, Inkarojrit V, Lee ES (2006) Subject responses to electrochromic windows. Energy Build 38(7):758–779. Scholar
  21. Clements-Croome D (2004) Intelligent buildings—design, management and operation. Thomas TelfordGoogle Scholar
  22. de Meester T, Marique AF, De Herde A, Reiter S (2013) Impacts of occupant behaviours on residential heating consumption for detached houses in a temperate climate in the northern part of Europe. Energy Build 57(Suppl C):313–323.
  23. Delzendeh E, Wu S, Lee A, Zhou Y (2017) The impact of occupants’ behaviours on building energy analysis: a research review. Renew Sustain Energy Rev 80:1061–1071. Scholar
  24. D’Oca S, Hong T (2015) Occupancy schedules learning process through a data mining framework. Energy Build 88:395–408. Scholar
  25. D’Oca S, Corgnati SP, Buso T (2014a) Smart meters and energy savings in Italy: determining the effectiveness of persuasive communication in dwellings. Energy Res Soc Sci 3(C):131–142. Scholar
  26. D’Oca S, Fabi V, Corgnati SP, Andersen RK (2014b) Effect of thermostat and window opening occupant behaviour models on energy use in homes. Build Simul 7(6):683–694. Scholar
  27. D’Oca S, Hong T, Langevin J (2018) The human dimensions of energy use in buildings: a review. Renew Sustain Energy Rev 81:731–742. Scholar
  28. Dong B, Lam KP, Neuman CP (2011) Integrated building control based on occupant behaviour pattern detection and local weather forecasting. Comunicação apresentada em Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation AssociationGoogle Scholar
  29. Fabi V, Andersen RV, Corgnati S, Olesen BW (2012) Occupants’ window opening behaviour: a literature review of factors influencing occupant behaviour and models. Build Environ 58:188–198. <Go to ISI>://WOS:000309332800018CrossRefGoogle Scholar
  30. Fabi V, Andersen RV, Corgnati SP, Olesen BW (2013) A methodology for modelling energy-related human behaviour: application to window opening behaviour in residential buildings. Build Simul 6(4):415–427. Scholar
  31. Fabi V, Sugliano M, Andersen RK, Corgnati SP (2015) Validation of occupants’ behaviour models for indoor quality parameter and energy consumption prediction. Procedia Eng 121(Suppl C):1805–1811. Scholar
  32. Fanger PO (1970) Thermal comfort—analysis and applications in environmental engineeringGoogle Scholar
  33. Foldbjerg P, Rasmussen C, Asmussen T (2011). Thermal comfort in two European active houses: analysis of the effects of solar shading and ventilative coolingGoogle Scholar
  34. Frontczak M, Andersen RV, Wargocki P (2012) Questionnaire survey on factors influencing comfort with indoor environmental quality in Danish housing. Build Environ 50:56–64CrossRefGoogle Scholar
  35. Gao G, Whitehouse K (2009) The self-programming thermostat: optimizing setback schedules based on home occupancy patterns. Comunicação apresentada em 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BUILDSYS 2009, in Conjunction with ACM SenSys 2009, em Berkeley, CAGoogle Scholar
  36. Gill ZM, Tierney MJ, Pegg IM, Allan N (2010) Low-energy dwellings: the contribution of behaviours to actual performance. Build Res Inf 38(5):491–508. Scholar
  37. Gomes MG, Santos AJ, Rodrigues AM (2014) Solar and visible optical properties of glazing systems with venetian blinds: numerical, experimental and blind control study. Build Environ 71:47–59. Scholar
  38. Gram-Hanssen K (2010) Residential heat comfort practices: understanding users. Build Res Inf 38(2):175–186. Scholar
  39. Gratia E, De Herde A (2004) Optimal operation of a south double-skin facade. Energy Build 36(1):41–60. Scholar
  40. Guerra Santin O (2011) Behavioural patterns and user profiles related to energy consumption for heating. Energy Build 43(10):2662–2672. Scholar
  41. Guerra Santin O, Itard L (2010) Occupants’ behaviour: determinants and effects on residential heating consumption. Build Res Inf 38(3):318–338. Scholar
  42. Guerra Santin O, Itard L, Visscher H (2009) The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy Build 41(11):1223–1232. Scholar
  43. Guerra-Santin O, Romero Herrera N, Cuerda E, Keyson D (2016) Mixed methods approach to determine occupants’ behaviour—analysis of two case studies. Energy Build 130:546–566. Scholar
  44. Gunay HB, O’Brien W, Beausoleil-Morrison I (2013) A critical review of observation studies, modeling, and simulation of adaptive occupant behaviours in offices. Build Environ 70(Suppl C):31–47. Scholar
  45. Hastie T, Tibshirani R (1986) Generalized additive models. Stat Sci 1(3):297–318MathSciNetCrossRefGoogle Scholar
  46. Hawarah L, Ploix S, Jacomino M (2010) User behaviour prediction in energy consumption in housing using Bayesian networks. In: 10th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2010. Zakopane. Scholar
  47. Hendron R, Engebrecht C (2010) Building America research benchmark definitionGoogle Scholar
  48. Herkel S, Knapp U, Pfafferott J (2008) Towards a model of user behaviour regarding the manual control of windows in office buildings. Build Environ 43(4):588–600. Scholar
  49. Hinkle DE, Wiersma W, Jurs SG (2003) Applied statistics for the behavioural sciences. In Mifflin H (ed), 5th edn. Malawai Med J, BostonGoogle Scholar
  50. Hnat TW, Srinivasan V, Lu J, Sookoor TI, Dawson R, Stankovic J, Whitehouse K (2011) The hitchhiker’s guide to successful residential sensing deployments. Comunicação apresentada em 9th ACM Conference on Embedded Networked Sensor Systems, SenSys 2011, em Seattle, WAGoogle Scholar
  51. Hollander M, Wolfe DA, Chicken E (2013) Nonparametric statistical methodsGoogle Scholar
  52. Hong T, D’Oca S, Turner WJN, Taylor-Lange SC (2015) An ontology to represent energy-related occupant behaviour in buildings. Part I: Introduction to the DNAs framework. Build Environ 92:764–777. Scholar
  53. Hong T, Yan D, D’Oca S, Chen CF (2017) Ten questions concerning occupant behaviour in buildings: the big picture. Build Environ 114:518–530. Scholar
  54. Howard-Reed C, Wallace LA, Ott WR (2002) The effect of opening windows on air change rates in two homes. J Air Waste Manag Assoc 52(2):147–159. Scholar
  55. Huang KL, Feng GH, Li HX, Yu S (2014) Opening window issue of residential buildings in winter in north China: a case study in Shenyang. Energy Build 84:567–574. <Go to ISI>://WOS:000345182000056CrossRefGoogle Scholar
  56. IBG (2014) Intelligent Building Group. Accessed 15 May.
  57. Iwashita G, Akasaka H (1997) The effects of human behaviour on natural ventilation rate and indoor air environment in summer—a field study in southern Japan. Energy Build 25(3):195–205. Scholar
  58. Jain RK, Gulbinas R, Taylor JE, Culligan PJ (2013) Can social influence drive energy savings? Detecting the impact of social influence on the energy consumption behaviour of networked users exposed to normative eco-feedback. Energy Build 66:119–127. Scholar
  59. Jang H, Kang J (2016) A stochastic model of integrating occupant behaviour into energy simulation with respect to actual energy consumption in high-rise apartment buildings. Energy Build 121(Suppl C):205–216. Scholar
  60. Jelle BP (2013) Solar radiation glazing factors for window panes, glass structures and electrochromic windows in buildings—measurement and calculation. Sol Energy Mater Sol Cells 116:291–323. Scholar
  61. Jia M, Srinivasan RS, Raheem AA (2017) From occupancy to occupant behaviour: an analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency. Renew Sustain Energy Rev 68:525–540. Scholar
  62. Johansson P, Pallin S, Shahriari M (2010) Risk assessment model applied on building physics: statistical data acquisition and stochastic modeling of indoor moisture supply in Swedish multi-family dwellingsGoogle Scholar
  63. Kleiminger W, Mattern F, Santini S (2014) Predicting household occupancy for smart heating control: a comparative performance analysis of state-of-the-art approaches. Energy Build 85:493–505. Scholar
  64. Kvisgaard B, Collet PF (1986) Occupants’ influence on air change in dwellings. Comunicação apresentada em 7th AIC Conference, em Stratford-upon-Avon, UKGoogle Scholar
  65. Lee ES, DiBartolomeo DL, Selkowitz SE (1998) Thermal and daylighting performance of an automated venetian blind and lighting system in a full-scale private office. Energy Build 29(1):47–63. Scholar
  66. Lee ES, Claybaugh ES, Lafrance M (2012) End user impacts of automated electrochromic windows in a pilot retrofit application. Energy Build 47:267–284. Scholar
  67. Lehman A, O’Rourke N, Hatcher L, Stepanski E (2013) JMP for basic univariate and multivariate statistics: methods for researchers and social scientists, 2nd edn. SASGoogle Scholar
  68. Lu J, Sookoor T, Srinivasan V, Gao G, Holben B, Stankovic J, Field E, Whitehouse K (2010) The smart thermostat: using occupancy sensors to save energy in homes. Comunicação apresentada em 8th ACM International Conference on Embedded Networked Sensor Systems, SenSys 2010, em ZurichGoogle Scholar
  69. Luo M, Cao B, Zhou X, Li M, Zhang J, Ouyang Q, Zhu Y (2014) Can personal control influence human thermal comfort? A field study in residential buildings in China in winter. Energy Build 72:411–418. Scholar
  70. Manz H (2003) Numerical simulation of heat transfer by natural convection in cavities of facade elements. Energy Build 35(3):305–311. Scholar
  71. Marcus SJ (1983). The ‘intelligent’ buildings. Accessed 15 Apr.
  72. Marques da Silva F, Gomes MG, Rodrigues AM (2015) Measuring and estimating airflow in naturally ventilated double skin facades. Build Environ 87:292–301. Scholar
  73. Melfi R, Rosenblum B, Nordman B, Christensen K (2011) Measuring building occupancy using existing network infrastructure. Comunicação apresentada em 2011 International Green Computing Conference and Workshops, IGCC 2011Google Scholar
  74. Messerve T, Duszczyk K, Martins N, Dymarski P, Samset H, Salmon N, Decorme R, Ramiro M (2010) E3SoHo—ICT for energy efficiency in social housingsGoogle Scholar
  75. Möllers F, Seitz S, Hellmann A, Sorge C (2014) Short paper: extrapolation and prediction of user behaviour from wireless home automation communication. Comunicação apresentada em WiSec 2014—Proceedings of the 7th ACM Conference on Security and Privacy in Wireless and Mobile NetworksGoogle Scholar
  76. Mora D, Carpino C, de Simone M (2017) Energy consumption of residential buildings and occupancy profiles. A case study in Mediterranean climatic conditions. Energy Effi 1–25.
  77. Mozer MC (2005) Lessons from an adaptive house. Smart environments: technologies, protocols, and applications, pp 273–294CrossRefGoogle Scholar
  78. Nguyen TA, Aiello M (2013) Energy intelligent buildings based on user activity: a survey. Energy Build 56:244–257. Scholar
  79. Nicol JF (2001) Characterising occupant behaviour in buildings: towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans. In: Proceedings of the 7th International IBPSA Conference (7), pp 1073–1078Google Scholar
  80. Nikolaou T, Kolokotsa D, Stavrakakis G, Ghiaus C, Inard C, Zinzi M, Fasano G, Liao Z, Stherland G, Karatasou S, Santamouris M (2004) Intelligent building assessmengt methodology—smart accelerate projectGoogle Scholar
  81. Ochoa CE, Capeluto IG (2009) Advice tool for early design stages of intelligent facades based on energy and visual comfort approach. Energy Build 41(5):480–488. Scholar
  82. Peng C, Yan D, Wu R, Wang C, Zhou X, Jiang Y (2012) Quantitative description and simulation of human behaviour in residential buildings. Build Simul 5(2):85–94. Scholar
  83. Pereira PF, Ramos NMM (2018) Detection of occupant actions in buildings through change point analysis of in-situ measurements. Energy Build 173:365–377. Scholar
  84. Pereira PF, Ramos NMM, Almeida RMSF, Simões ML, Barreira E (2017) Occupant influence on residential ventilation patterns in mild climate conditions. Energy Procedia 132(Suppl C):837–842. Scholar
  85. Ramos NMM, Curado A, Almeida RMSF (2015) Analysis of user behaviour profiles and impact on the indoor environment in social housing of mild climate countries. Comunicação apresentada em 6th International Building Physics Conference, IBPC 2015, em TurimGoogle Scholar
  86. Reinisch C, Kofler MJ, Iglesias F, Kastner W (2011) ThinkHome energy efficiency in future smart homes. EURASIP J Embed Syst—Special issue on networked embedded systems for energy management and buildings 2011Google Scholar
  87. REMODECE (2015) Residential monitoring to decrease energy use and carbon emissions in Europe.
  88. Rijal HB, Humphreys M, Nicol F (2015) Adaptive thermal comfort in Japanese houses during the summer season: behavioural adaptation and the effect of humidity. Buildings 5(3):1037–1054. Scholar
  89. Russel SJ, Norvig P (2003) Artificial intelligence: a modern approachGoogle Scholar
  90. Silva AS, Ghisi E (2014) Uncertainty analysis of user behaviour and physical parameters in residential building performance simulation. Energy Build 76:381–391. Scholar
  91. Sinnott D, Dyer M (2011) Airtightness of dwellings in Ireland: design, workmanship and control, em SalfordGoogle Scholar
  92. Sinopoli J (2010) Smart building systems for architects, owners, and builders. Elsevier Inc.Google Scholar
  93. So AT, Chan WL (1999) Intelligent building systems. Kluwer Academic PublishersGoogle Scholar
  94. Stazi F, Naspi F, D’Orazio M (2017a) Modelling window status in school classrooms. Results from a case study in Italy. Build Environ 111:24–32. Scholar
  95. Stazi F, Naspi F, D’Orazio M (2017b) A literature review on driving factors and contextual events influencing occupants’ behaviours in buildings. Build Environ 118:40–66. Scholar
  96. Stazi F, Naspi F, Ulpiani G, Di Perna C (2017c) Indoor air quality and thermal comfort optimization in classrooms developing an automatic system for windows opening and closing. Energy Build 139:732–746. Scholar
  97. Steemers K, Yun GY (2009) Household energy consumption: a study of the role of occupants. Build Res Inf 37(5–6):625–637. Scholar
  98. Szczurek A, Maciejewska M, Wyłomańska A, Zimroz R, Żak G, Dolega A (2016) Detection of occupancy profile based on carbon dioxide concentration pattern matching. Meas: J Int Meas Confederation 93:265–271.
  99. Van Den Wymelenberg K (2012) Patterns of occupant interaction with window blinds: a literature review. Energy Build 51:165–176.
  100. Wallace LA, Emmerich SJ, Howard-Reed C (2002) Continuous measurements of air change rates in an occupied house for 1 year: the effect of temperature, wind, fans, and windows. J Expo Anal Environ Epidemiol 12(4):296–306. Scholar
  101. Wang S (2010) Intelligent buildings and building automation. Taylor & FrancisGoogle Scholar
  102. Wei S, Jones R, de Wilde P (2014) Driving factors for occupant-controlled space heating in residential buildings. Energy Build 70:36–44. Scholar
  103. Weng T, Agarwal Y (2012) From buildings to smart buildings-sensing and actuation to improve energy efficiency. IEEE Des Test Comput 29(4):36–44. Scholar
  104. Whitehouse K, Ranjan J, Lu J, Sookoor T, Saadat M, Burke CM, Staengl G, Canfora A, Haj-Hariri H (2012) Towards occupancy-driven heating and cooling. IEEE Des Test Comput 29(4):17–25. Scholar
  105. Wigginton M, Harris J (2002) Intelligent skins. Editado por ElsevierGoogle Scholar
  106. Wilke U, Haldi F, Scartezzini JL, Robinson D (2013) A bottom-up stochastic model to predict building occupants’ time-dependent activities. Build Environ 60:254–264. Scholar
  107. Winston PH (1993) Artificial intelligenceGoogle Scholar
  108. Wong JKW, Li H, Wang SW (2005a) Intelligent building research: a review. Autom Constr 14(1):143–159. Scholar
  109. Wong KC, So TPA, Leung YTA (2005b) Intelligent building index. Editado por Asian Institute of Intelligent Buildings, 3rd ednGoogle Scholar
  110. Wood G, Newborough M (2007) Energy-use information transfer for intelligent homes: enabling energy conservation with central and local displays. Energy Build 39(4):495–503. Scholar
  111. Wyckmans A (2005) Intelligent Building envelopes—architectural concept & applications for daylighting quality. Department of Architectural Design, History and Technology Norwegian University of Science and TechnologyGoogle Scholar
  112. Yan D, Hong T (2014) EBC Annex 66—definition and simulation of occupant behaviour in buildingsGoogle Scholar
  113. Yan D, O’Brien W, Hong T, Feng X, Burak Gunay H, Tahmasebi F, Mahdavi A (2015) Occupant behaviour modeling for building performance simulation: current state and future challenges. Energy Build 107:264–278. Scholar
  114. Yan D, Hong T, Dong B, Mahdavi A, D’Oca S, Gaetani I, Feng X (2017) IEA EBC Annex 66: definition and simulation of occupant behaviour in buildings. Energy Build 156:258–270. Scholar
  115. Yang J, Peng H (2001) Decision support to the application of intelligent building technologies. Renew Energy 22(1):67–77. Accessed 1–28 Feb 1999. Scholar
  116. Yang Z, Becerik-Gerber B, Li N, Orosz M (2014) A systematic approach to occupancy modeling in ambient sensor-rich buildings. Simulation 90(8):960–977. Scholar
  117. Yao M, Zhao B (2017) Window opening behaviour of occupants in residential buildings in Beijing. Build Environ 124:441–449. Scholar
  118. ZeroCarbonHub (2015) Post-occupancy evaluation. Em Rowner Research Project Phase TwoGoogle Scholar
  119. Zhou J, Chen Y (2010) A review on applying ventilated double-skin facade to buildings in hot-summer and cold-winter zone in China. Renew Sustain Energy Rev 14(4):1321–1328. Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pedro F. Pereira
    • 1
    Email author
  • Nuno M. M. Ramos
    • 1
  • João M. P. Q. Delgado
    • 2
  1. 1.CONSTRUCT-LFC, Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.Department of Civil Engineering, Faculty of EngineeringUniversity of PortoPortoPortugal

Personalised recommendations