This study examined a representative college activity centre in China’s cold regions and conducted a field measurement study and questionnaire analysis on the indoor thermal environment in winter. Data fitting and comparative analysis were performed on the predicted mean vote (PMV) calculated from measured environmental parameters and the seven-point vote value actual mean vote (AMV) based on the questionnaire. According to the least square method, the corresponding human adaptive coefficient λm in the motion state and λs in the stationary state were obtained. Based on the research, the thermal comfort survey results were reported for the two states for recreational spaces of colleges and universities. These results suggest that the AMV and PMV do not coincide in the two states. The actual thermal sensation is less sensitive than predicted through the sensitivity analysis for the two states. The respondents’ actual thermal sensation is less sensitive when exercising than stationary state. Moreover, both PMV and AMV in motion state are higher than those in stationary state, illustrating that the state of motion has a greater influence on thermal sensation. This research provides a theoretical basis and data reference for the low energy design of recreational spaces of colleges and universities.
National Bureau of Statistics of China. China statistical yearbook 2008.
Beijing:
China Statistics Press, 2009.
3.
YuanXLZuoJHuisinghD.Green Universities in China – what matters?J Clean Prod2013;
61: 36–45.
4.
AlmeidaRRamosNMMFreitasV.Thermal comfort models and pupils’ perception in free-running school buildings of a mild climate country. Energy Build2016;
111: 64–75.
5.
LeeMCMuiKWWongLTChanWYLeeEWMCheungCT.Student learning performance and indoor environmental quality (IEQ) in air-conditioned university teaching rooms. Build Environ2012;
49: 238–244.
6.
YangZBecerik-GerberBMinoL.A study on student perceptions of higher education classrooms: impact of classroom attributes on student satisfaction and performance. Build Environ2013;
70: 171–188.
7.
WargockiPWyonDP.Providing better thermal and air quality conditions in school classrooms would be cost-effective. Build Environ2013;
59: 581–589.
8.
Haverinen-ShaughnessyUShaughnessyRJColeECToyinboOMoschandreasDJ.An assessment of indoor environmental quality in schools and its association with health and performance. Build Environ2015;
93: 35–40.
9.
BurattiCBelloniEMerliFRicciardiP.A new index combining thermal, acoustic, and visual comfort of moderate environments in temperate climates. Build Environ2018;
139: 27–37.
10.
CaoBZhuYXOuyangQZhouXHuangL.Field study of human thermal comfort and thermal adaptability during the summer and winter in Beijing. Energy Build2011;
43: 1051–1056.
11.
YaoRMLiuJLiBZ.Occupants’ adaptive responses and perception of thermal environment in naturally conditioned university classrooms. Appl Energy2010;
87: 1015–1022.
12.
BurattiCRicciardiP.Adaptive analysis of thermal comfort in university classrooms: correlation between experimental data and mathematical models. Build Environ2009;
44: 674–687.
13.
DascalakiEGSermpetzoglouVG.Energy performance and indoor environmental quality in Hellenic schools. Energy Build2011;
43: 718–727.
14.
BurattiCPalladinoDRicciardiP.Application of a new 13-value thermal comfort scale to moderate environments. Appl Energy2016;
180: 859–866.
15.
ZomorodianZSTahsildoostMHafeziM.Thermal comfort in educational buildings: a review article. Renew Sust Energy Rev2016;
59: 895–906.
16.
WangDJJiangJLiuYFWangYYXuYCLiuJP.Student responses to classroom thermal environments in rural primary and secondary schools in winter. Build Environ2017;
115: 104–117.
17.
TrebilcockMSoto-MuñozJYañezMMartinRF.The right to comfort: a field study on adaptive thermal comfort in free-running primary schools in Chile. Build Environ2017;
114: 455–469.
18.
RicciardiPBurattiC.Environmental quality of university classrooms: subjective and objective evaluation of the thermal, acoustic, and lighting comfort conditions. Build Environ2018;
127: 23–36.
19.
BurattiCRicciardiPVergoniM.HVAC systems testing and check: a simplified model to predict thermal comfort conditions in moderate environments. Appl Energy2013;
104: 117–127.
20.
TeliDJentschMFJamesP.The role of a building’s thermal properties on pupils’ thermal comfort in junior school classrooms as determined in field studies. Build Environ2014;
82: 640–654.
21.
RuppRFVásquezNGLambertsR.A review of human thermal comfort in the built environment. Energy Build2015;
105: 178–205.
22.
YunHNamIKimJYangJLeeKSohnJ.A field study of thermal comfort for kindergarten children in Korea: an assessment of existing models and preferences of children. Build Environ2014;
75: 182–189.
23.
GilaniSKhanMHAliM.Revisiting Fanger’s thermal comfort model using mean blood pressure as a bio-marker: an experimental investigation. Appl Therm Eng2016;
109: 35–43.
24.
LiYGengSBZhangXSZhangH.Study of thermal comfort in underground construction based on field measurements and questionnaires in China. Build. Environ2017;
116: 45–54.
25.
RuppRFGhisiE.Predicting thermal comfort in office buildings in a Brazilian temperate and humid climate. Energy Build2017;
144: 152–166.
26.
AzizpourFMoghimiSSallehEMatSLimCHSopianK.Thermal comfort assessment of large-scale hospitals in tropical climates: a case study of University Kebangsaan Malaysia Medical Centre (UKMMC). Energy Build2013;
64: 317–322.
27.
MaitiR.PMV model is insufficient to capture subjective thermal response from Indians. Int J Ind Ergonom2014;
44: 349–361.
28.
de DearRBragerGS.The adaptive model of thermal comfort and energy conservation in the built environment. Int J Biometeorol2001;
45: 100–108.
29.
HumphreysM.Outdoor temperatures and comfort indoors. Build Res Prac1978;
6: 92–92.
30.
BragerGSde DearRJ.Thermal adaptation in the built environment: a literature review. Energy Build1998;
27: 83–96.
31.
YaoRMLiBZLiuJ.A theoretical adaptive model of thermal comfort-adaptive predicted mean vote (aPMV). Build and Environ2009;
44: 2089–2096.
32.
KimJTLimJHChoSHYunGY.Development of the adaptive PMV model for improving prediction performances. Energy Build2015;
98: 100–105.
33.
GB/T 50785:2012. Evaluation standard for indoor thermal environment in civil buildings in China.
Beijing:
The Standardization Administration of China, 2012.
34.
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.
Geneva:
The International Organization for Standardization, 2005.
35.
HungC.Building thermal environment.
Beijing:
China Machine Press, 2016, p. 118.
36.
LiBZZhengJYaoRMJingSL.Indoor thermal environment and human thermal comfort.
Chongqing:
Chongqing University Press, 2012, p. 159.
37.
LiuNXQinYG.Building thermal environment.
Beijing:
Tsinghua University Press, 2016, p. 45.
38.
ISO 10551: 1995. Ergonomics of the thermal environment – assessment of the influence of thermal environment using subjective judgment scales. Geneva: The International Organization for Standardization, 1995.
39.
ISO 8996: 2004. Determination of metabolic heat production.
Geneva:
The International Organization for Standardization, 2004.
40.
ISO 9920: 2007. Ergonomics of the thermal environment – estimation of thermal insulation and water vapour resistance of a clothing ensemble.
Geneva:
The International Organization for Standardization, 2007.
41.
Meteorological Information Centre in China Meteorological Administration.Special meteorological data set for China building thermal environment analysis.
Beijing:
China Building Industry Press, 2005.
