Adaptive Heating Balance Comfort Model

  • Maohui LuoEmail author
Part of the Springer Theses book series (Springer Theses)


Human thermal comfort is influenced by both external environmental attributors and personal factors. The environmental attributors include air temperature (TA), radiant temperature (TR) (Zhou et al. in Energy Build 188:98–110, 2019 [1]), air movement (VEL) (Zhu et al. in Build Environ 91:5–14, 2015 [2]), relative humidity (RH), and so forth, while the personal factors involve metabolic rate (MET) (Luo and Wang in Build Environ 131:44–52, 2018 [3]), clothing insulation (CLO), maybe age, gender, and adaptation (Ji et al. Build Environ 114:246–256, 2017 [4]). To date, many efforts have been paid to develop models quantifying how these attributors may affect building occupants’ thermal comfort.


  1. 1.
    Zhou X, Liu Y, Luo M et al (2019) Thermal comfort under radiant asymmetries of floor cooling system in 2 h and 8 h exposure durations. Energy Build 188:98–110Google Scholar
  2. 2.
    Zhu Y, Luo M, Qin O et al (2015) Dynamic characteristics and comfort assessment of airflows in indoor environments: a review. Build Environ 91:5–14Google Scholar
  3. 3.
    Luo M, Wang Z et al (2018) Human metabolic rate and thermal comfort in buildings: the problem and challenge. Build Environ 131:44–52Google Scholar
  4. 4.
    Ji W, Luo M, Cao B et al (2017) Influence of short-term thermal experience on thermal comfort evaluations: a climate chamber experiment. Build Environ 114:246–256Google Scholar
  5. 5.
    Enescu D (2017) A review of thermal comfort models and indicators for indoor environments. Renew Sustain Energy Rev 79:1353–1379CrossRefGoogle Scholar
  6. 6.
    Zaki S, Damiati S, Rijal H, Hagishima A, Razak A (2017) Adaptive thermal comfort in university classrooms in Malaysia and Japan. Build Environ 122:294–306CrossRefGoogle Scholar
  7. 7.
    Fanger PO (1970) Thermal comfort. Analysis and application in environment engineering. Danish Technology Press, CopenhagenGoogle Scholar
  8. 8.
    Technical Committee ISO/TC 159 and Technical Committee CEN/TC 122. ISO 7730 (2005) Ergonomics of the thermal environment—analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. European Committee for Standardization, UKGoogle Scholar
  9. 9.
    Humphreys M (1994) Field studies and climate chamber experiments in thermal comfort research. Therm Comfort Past Present Fut 52–72Google Scholar
  10. 10.
    Brager GS, de Dear RJ (1998) Thermal adaptation in the built environment: a literature review. Energy Build 27(1):83–96CrossRefGoogle Scholar
  11. 11.
    Cheung T, Schiavon S, Parkinson T et al (2019) Analysis of the accuracy on PMV–PPD model using the ASHRAE global thermal comfort database II. Build Environ 153:205–2017CrossRefGoogle Scholar
  12. 12.
    de Dear R, Brager G (2001) The adaptive model of thermal comfort and energy conservation in the built environment. Int J Biometeorol 45:100–108CrossRefGoogle Scholar
  13. 13.
    Mishra A, Ramgopal M (2013) Field studies on human thermal comfort—an overview. Build Environ 64:94–106CrossRefGoogle Scholar
  14. 14.
    Luo M, Cao B, Damiens J, Lin B, Ouyang Q et al (2015) Evaluating thermal comfort in mixed-mode buildings: a field study in a subtropical climate. Build Environ 88:46–54CrossRefGoogle Scholar
  15. 15.
    Nicol F, Humphreys M (2010) Derivation of the adaptive equations for thermal comfort in free-running buildings in European standard EN15251. Building and Environment 45(1):11–17CrossRefGoogle Scholar
  16. 16.
    Foldvary V, Cheung T, Zhang H et al (2018) Development of the ASHRAE global thermal comfort database II. Build Environ 142:502–512CrossRefGoogle Scholar
  17. 17.
    de Dear R (1998) Global database of thermal comfort field experiments. ASHRAE Trans 104:1141–1152Google Scholar
  18. 18.
    Deuble MP, de Dear RJ (2012) Mixed-mode buildings: a double standard in occupants’ comfort expectation. Build Environ 54:53–60CrossRefGoogle Scholar
  19. 19.
    McCartney KJ, Nicol JF (2002) Developing an adaptive control algorithm for Europe: results of the SCATs Project. Energy Build 34(6):623–635CrossRefGoogle Scholar
  20. 20.
    Carlucci S, Bai L, de Dear R, Yang L (2018) Review of adaptive thermal comfort models in built environmental regulatory documents. Build Environ 137:73–89Google Scholar
  21. 21.
    Schiavon S, Lee K (2013) Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures. Build Environ 59:250–260CrossRefGoogle Scholar
  22. 22.
    Kingma B, Frijns A, Schellen L et al (2014) Beyond the classic thermoneutral zone including thermal comfort. Temperature 1(2):142–149CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer Nature Singapore Pte Ltd.  2020

Authors and Affiliations

  1. 1.Tongji UniversityShanghaiChina

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