Skip to main content
SpringerLink
Log in

Development of an Adaptation Table to Enhance the Accuracy of the Predicted Mean Vote Model

  • Chapter
  • First Online:
Data-driven Analytics for Sustainable Buildings and Cities

Part of the book series: Sustainable Development Goals Series ((SDGS))

  • 645 Accesses

Abstract

The Predicted Mean Vote (PMV) model is extensively used by current thermal comfort standards, such as ASHRAE 55 and ISO 7730, despite its discrepancy in predicting Thermal Sensation (TS). The implicit assumption is that PMV can be applied for predicting TS of a large population. Our statistical analysis of a subset of ASHRAE global database of thermal comfort field study shows that occupants’ expectations towards TS are affected by factors that are not accounted for in the classic PMV model, such as climate, building type, age group, season and gender. The influences of the climate and building type are more determinant. An adapted PMV (PMVa) model and an adaptation table were developed based on the selected samples to reduce this discrepancy. After adaptation, the medians of each category corresponding to the discrepancy are zero or near zero. The results also show that the adapted PMV outperforms the classic PMV in predicting TS, while increasing the overall accuracy from 36 to 39%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alwetaishi MS (2017) Impact of building function on thermal comfort: a review paper. Am J Eng Appl Sci 9:928–945. https://doi.org/10.3844/ajeassp.2016.928.945

  • Al horr Y, Arif M, Katafygiotou M, Mazroei A, Kaushik A, Elsarrag E (2016) Impact of indoor environmental quality on occupant well-being and comfort: a review of the literature. Int J Sustain Built Environ 5:1–11. https://doi.org/10.1016/J.IJSBE.2016.03.006.

  • ASHRAE Standard 55 (2017) Thermal environmental conditions for human occupancy

    Google Scholar 

  • Amai H, Tanabe S, Akimoto T, Genma T (2007) Thermal sensation and comfort with different task conditioning systems. Build Environ 423955–3964. https://doi.org/10.1016/J.BUILDENV.2006.07.043

  • d’Ambrosio Alfano FR, Olesen BW, Palella BI (2017) Povl ole fanger’s impact ten years later. Energy Build 152:243–249. https://doi.org/10.1016/j.enbuild.2017.07.052

  • Beshir MY, Ramsey JD (1981) Comparison between male and female subjective estimates of thermal effects and sensations. Appl Ergon 12:29–33. https://doi.org/10.1016/0003-6870(81)90091-0

  • Cao B, Zhu Y, Ouyang Q, Zhou X, Huang L (2011) Field study of human thermal comfort and thermal adaptability during the summer and winter in Beijing. Energy Build 1051–1056. https://doi.org/10.1016/j.enbuild.2010.09.025

  • Cariou V, Qannari EM (2018) Statistical treatment of free sorting data by means of correspondence and cluster analyses. Food Qual Prefer 68:1–11. https://doi.org/10.1016/j.foodqual.2018.01.011

  • Cheng X, Yang B, Hedman A, Olofsson T, Li H, Van Gool L (2019) NIDL: A pilot study of contactless measurement of skin temperature for intelligent building. Energy Build 198:340–352. https://doi.org/10.1016/J.ENBUILD.2019.06.007.

  • Cheung T, Schiavon S, Parkinson T, Li P, Brager G (2019) Analysis of the accuracy on PMV – PPD model using the ASHRAE global thermal comfort database II. Build Environ 153:205–217. https://doi.org/10.1016/J.BUILDENV.2019.01.055

  • Daher E, Kubicki S, Guerriero A (209) Post-occupancy evaluation parameters in multi-objective optimization–based design process. Adv Inform Comput Civ Constr Eng. Springer International Publishing, Cham, pp. 463–470. https://doi.org/10.1007/978-3-030-00220-6_55

  • Dear RJD, Brager GS (1998) Developing an adaptive model of thermal comfort and preference, ASHRAE Trans 104

    Google Scholar 

  • European Commission (2016) An EU strategy on heating and cooling

    Google Scholar 

  • Fanger PO (1970) Thermal comfort: analysis and applications in environmental engineering. Copenhagen, Danish Technical Press. https://doi.org/10.1016/s0003-6870(72)80074-7

  • Fanger PO, Toftum J (2002) Extension of the PMV model to non-air-conditioned buildings in warm climate. Energy Build, Elsevier, pp. 533–536. https://doi.org/10.1016/S0378-7788(02)00003-8.

  • Fountain M, Brager G, De Dear R (1996) Expectations of indoor climate control. Energy Build 24:179–182. https://doi.org/10.1016/S0378-7788(96)00988-7.

  • Frontczak M, Wargocki P (2011) Literature survey on how different factors influence human comfort in indoor environments. Build Environ 46:922–937. https://doi.org/10.1016/j.buildenv.2010.10.021

  • Frontczak M, Schiavon S, Goins J, Arens E, Zhang H, Wargocki P(2012) Quantitative relationships between occupant satisfaction and satisfaction aspects of indoor environmental quality and building design. Indoor Air 22:119–131. https://doi.org/10.1111/j.1600-0668.2011.00745.x

  • Földváry LiÄŤina V, Cheung T, Zhang H, de Dear R, Parkinson T, Arens E, Chun C, Schiavon S, Luo M, Brager G, Li P, Kaam S, Adebamowo MA, Andamon MM, Babich F, Bouden C, Bukovianska H, Candido C, Cao B, Carlucci S, Cheong DKW, Choi JH, Cook M, Cropper P, Deuble M, Heidari S, Indraganti M, Jin Q, Kim H, Kim J, Konis K, Singh MK, Kwok A, Lamberts R, Loveday D, Langevin J, Manu S, Moosmann C, Nicol F, Ooka R, Oseland NA, Pagliano L, Petráš D, Rawal R, Romero R, Rijal HB, Sekhar C, Schweiker M, Tartarini F, ichi Tanabe S, Tham KW, Teli D, Toftum J, Toledo L, Tsuzuki K, De Vecchi R, Wagner A, Wang Z, Wallbaum H, Webb L, Yang L, Zhu Y, Zhai Y, Zhang Y, Zhou X (2018) Development of the ASHRAE global thermal comfort database II. Build Environ 142:502–512. https://doi.org/10.1016/j.buildenv.2018.06.022

  • Ghahramani A, Castro G, Becerik-Gerberm B, Yu X (2016) Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort. Build Environ 109:1–11. https://doi.org/10.1016/j.buildenv.2016.09.005.

  • Guergova S, Dufour A (2011) Thermal sensitivity in the elderly: a review. Ageing Res Rev 10:80–92. https://doi.org/10.1016/j.arr.2010.04.009

  • Greenacre M (1984) Correspondence analysis

    Google Scholar 

  • Halawa E, Van Hoof J (0212) The adaptive approach to thermal comfort: a critical overview. Energy Build 51:101–110. https://doi.org/10.1016/j.enbuild.2012.04.011

  • Hancock AM, Witonsky DB, Alkorta-Aranburu G, Beall CM, Gebremedhin A, Sukernik R, Utermann G, Pritchard JK, Coop G, Di Rienzo A (2011) Adaptations to climate-mediated selective pressures in humans. PLoS Genet 7:e1001375. https://doi.org/10.1371/journal.pgen.1001375

  • Höppe P, Martinac I (1998) Indoor climate and air quality. Review of current and future topics in the field of ISB study group 10. Int J Biometeorol 42:1–7. https://doi.org/10.1007/s004840050075

  • Humphreys MA, Nicol JF (2002) The validity of ISO-PMV for predicting comfort votes in every-day thermal environments. Elsevier. https://doi.org/10.1016/S0378-7788(02)00018-X.

  • Humphreys MA, Nicol JF (2002) Adaptive thermal comfort and sustainable thermal standards for buildings. Energy Build 34:563–572. https://doi.org/10.1016/S0378-7788(02)00006-3

  • 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

    Google Scholar 

  • Karmann C, Schiavon S, Arens E (2018) Percentage of commercial buildings showing at least 80% occupant satisfied with their thermal comfort. 10th WinD Con. Rethink Comf Cumberl Lodg

    Google Scholar 

  • Karjalainen S (2007) Gender differences in thermal comfort and use of thermostats in everyday thermal environments. Build Environ 42:1594–1603. https://doi.org/10.1016/j.buildenv.2006.01.009.

  • Karjalainen S (2009) Thermal comfort and use of thermostats in Finnish homes and office. Build Environ 44:1237–1245. https://doi.org/10.1016/j.buildenv.2008.09.002.

  • Karjalainen S (2012) Thermal comfort and gender: a literature review. Indoor Air 22:96–109. https://doi.org/10.1111/j.1600-0668.2011.00747.x

  • Kingma B, van Marken Lichtenbelt W (2015) Energy consumption in buildings and female thermal demand. Nat Clim Chang 5:1054–1056. https://doi.org/10.1038/nclimate2741

  • Lee J-B, Kim T-W, Min Y-K, Yang H-M (2015) Seasonal acclimatization in summer versus winter to changes in the sweating response during passive heating in Korean young adult men. Korean J Physiol Pharmacol 19:9. https://doi.org/10.4196/kjpp.2015.19.1.9.

  • Liu H, Wu Y, Li B, Cheng Y, Yao R (2017) Seasonal variation of thermal sensations in residential buildings in the hot summer and cold winter zone of China. Energy Build 140:9–18. https://doi.org/10.1016/j.enbuild.2017.01.066

  • Li Y, Rezgui Y, Zhu H (2017) District heating and cooling optimization and enhancement – Towards integration of renewables, storage and smart grid. Renew Sustain Energy Rev 72:281–294. https://doi.org/10.1016/j.rser.2017.01.061

  • Li Y, Kubicki S, Guerriero A, Rezgui Y(2019) Review of building energy performance certification schemes towards future improvement, Renew. Sustain. Energy Rev 113:109244. https://doi.org/10.1016/J.RSER.2019.109244

  • Maykot JK, Rupp RF, Ghisi E (2018) A field study about gender and thermal comfort temperatures in office buildings. Energy Build 178:254–264. https://doi.org/10.1016/j.enbuild.2018.08.033

  • McCartney KJ, Fergus Nicol J (2002) Developing an adaptive control algorithm for Europe. Energy Build, Elsevier, 623–635. https://doi.org/10.1016/S0378-7788(02)00013-0

  • Nakamura Y, Okamura K (1998) Seasonal variation of sweating responses under identical heat stress. Appl Hum Sci J Physiol Anthropol 17:167–172. https://doi.org/10.2114/jpa.17.167

  • Natsume K, Ogawa T, Sugenoya J, Ohnishi N, Imai K (1992) Preferred ambient temperature for old and young men in summer and winter. Int J Biometeorol 36:1–4. https://doi.org/10.1007/BF01208726

  • Nicol JF, Humphreys MA (2002) Adaptive thermal comfort and sustainable thermal standards for buildings. Energy Build 34:563–572. https://doi.org/10.1016/S0378-7788(02)00006-3

  • Nicol F, Humphreys M (2007) Maximum temperatures in European office buildings to avoid heat discomfort. Sol Energy 81:295–304. https://doi.org/10.1016/j.solener.2006.07.007

  • Oseland NA (1995) Predicted and reported thermal sensation in climate chambers, offices and homes. Energy Build 23:105–115. https://doi.org/10.1016/0378-7788(95)00934-5

  • Pantavou K, Lykoudis S, Nikolopoulou M, Tsiros IX (2018) Thermal sensation and climate: a comparison of UTCI and PET thresholds in different climates. Int J Biometeorol 62:1695–1708. https://doi.org/10.1007/s00484-018-1569-4

  • Rijal HB, Yoshida H, Umemiya N (n.d.) Seasonal and regional differences in neutral temperatures in Nepalese traditional vernacular houses. https://doi.org/10.1016/j.buildenv.2010.06.002.

  • Schellen L, Van Marken Lichtenbelt WD, Loomans MGLC, Toftum J, De Wit MH (2010) Differences between young adults and elderly in thermal comfort, productivity, and thermal physiology in response to a moderate temperature drift and a steady-state condition. Indoor Air 20:273–283. https://doi.org/10.1111/j.1600-0668.2010.00657.x.

  • Soebarto V, Zhang H, Schiavon S (2019) A thermal comfort environmental chamber study of older and younger people. Build Environ 155:1–14. https://doi.org/10.1016/j.buildenv.2019.03.032

  • Sourial N, Wolfson C, Zhu B, Quail J, Fletcher J, Karunananthan S, Bandeen-Roche K, BĂ©land F, Bergman H (2010) Correspondence analysis is a useful tool to uncover the relationships among categorical variables J Clin Epidemiol 63:638–646. https://doi.org/10.1016/j.jclinepi.2009.08.008

  • Teli D, Jentsch MF, James PAB (2012) Naturally ventilated classrooms: an assessment of existing comfort models for predicting the thermal sensation and preference of primary school children. Energy Build 53:166–182. https://doi.org/10.1016/j.enbuild.2012.06.022

  • Umemiya N (2006) Seasonal variations of physiological characteristics and thermal sensation under identical thermal conditions. J Physiol Anthropol 25:29–39. https://doi.org/10.2114/jpa2.25.29

  • Van Hoof J (2008) Forty years of fanger’s model of thermal comfort: comfort for all? Indoor Air 18:182–201. https://doi.org/10.1111/j.1600-0668.2007.00516.x

  • Wang Z, de Dear R, Luo M, Lin B, He Y, Ghahramani A, Zhu V (2018) Individual difference in thermal comfort: a literature review. Build Environ 138:181–193. https://doi.org/10.1016/J.BUILDENV.2018.04.040

  • Wang L, Kim J, Xiong J, Yin H (2019) Optimal clothing insulation in naturally ventilated buildings. Build Environ 154:200–210. https://doi.org/10.1016/j.buildenv.2019.03.029

  • Wang X, Li D, Menassa CC, Kamat VR (2019) Investigating the effect of indoor thermal environment on occupants’ mental workload and task performance using electroencephalogram. Build Environ 158:120–132. https://doi.org/10.1016/j.buildenv.2019.05.012

  • Wenzel HG, Mehnert C, Schwarzenau S (1989) Evaluation of tolerance limits for humans under heat stress and the problems involved. Scand J Work Environ Health 15(1):7–14

    Google Scholar 

  • Yang B, Olofsson T (2017) A questionnaire survey on sleep environment conditioned by different cooling modes in multistorey residential buildings of Singapore. Indoor Built Environ 26:21–31. https://doi.org/10.1177/1420326X15604206

  • Yang B, Wang F (2018) Supplementary opinions on alternative cooling technologies in hot climate. Int J Biometeorol 62:1927–1928. https://doi.org/10.1007/s00484-018-1588-1

  • Yang B, Cheng X, Dai D, Olofsson T, Li H, Meier A (2019) Real-time and contactless measurements of thermal discomfort based on human poses for energy efficient control of buildings. Build Environ 162:106284. https://doi.org/10.1016/J.BUILDENV.2019.106284.

  • Yao Y, Lian Z, Liu W, Shen Q (2007) Experimental study on skin temperature and thermal comfort of the human body in a recumbent posture under uniform thermal environments. Indoor Built Environ 16:505–518. https://doi.org/10.1177/1420326X07084291

  • Yao R, Li B, Liu J (2009) A theoretical adaptive model of thermal comfort - adaptive predicted mean vote (aPMV). Build Environ 44:2089–2096. https://doi.org/10.1016/j.buildenv.2009.02.014

  • Zhang Y, Chen H, Wang J, Meng Q (2016) Thermal comfort of people in the hot and humid area of China—impacts of season, climate, and thermal history. Indoor Air 26:820–830. https://doi.org/10.1111/ina.12256

  • Zhang F, de Dear R (2019) Impacts of demographic, contextual and interaction effects on thermal sensation—Evidence from a global database. Build Environ 162:106286. https://doi.org/10.1016/j.buildenv.2019.106286

Download references

Author information

Authors and Affiliations

  1. School of Environmental Science and Engineering, Donghua University, Shanghai, China

    Yu Li

  2. Luxembourg Institute of Science and Technology LIST, 5, avenue des Hauts-Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg

    Yu Li, Annie Guerriero & Sylvain Kubicki

  3. BRE Trust Centre for Sustainable Engineering, Cardiff University, Cardiff, C24 3AA, UK

    Yacine Rezgui

  4. Department of Energy and Community Buildings, Dalarna University, 79188, Falun, Sweden

    Xingxing Zhang

  5. Micro Data Analysis , Dalarna University, 79188, Falun, Sweden

    Mengjie Han

  6. Building Energy Conservation Research Center, Tsinghua University, Beijing, 100084, China

    Yan Da

Authors
  1. Yu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yacine Rezgui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Annie Guerriero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingxing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mengjie Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sylvain Kubicki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yan Da
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Li .

Editor information

Editors and Affiliations

  1. Dalarna University, Falun, Sweden

    Xingxing Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Li, Y. et al. (2021). Development of an Adaptation Table to Enhance the Accuracy of the Predicted Mean Vote Model. In: Zhang, X. (eds) Data-driven Analytics for Sustainable Buildings and Cities. Sustainable Development Goals Series. Springer, Singapore. https://doi.org/10.1007/978-981-16-2778-1_11

Download citation

Publish with us

Policies and ethics

Search

Navigation