An introduction to the Chinese Evaluation Standard for the indoor thermal environment

Li, Baizhan; Yao, Runming; Wang, Qinqing; Pan, Yungang

doi:10.1016/j.enbuild.2014.06.032

Cited by 117 publications

(56 citation statements)

References 17 publications

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“…A theoretical adaptive model of thermal comfort based on the cybernetic engineering 'black box' theory was proposed by Yao et al [19,20]. The adaptive predicted mean vote (aPMV) model takes into account factors such as culture, climate and social, psychological and behavioral adaptations.…”

Section: Apmv Modelmentioning

confidence: 99%

Development of the adaptive PMV model for improving prediction performances

Kim

Lim

Cho

et al. 2015

Energy and Buildings

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Section: Apmv Modelmentioning

confidence: 99%

Development of the adaptive PMV model for improving prediction performances

Kim

Lim

Cho

et al. 2015

Energy and Buildings

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“…Recently the Chinese Evaluation Standard for the indoor thermal environment was proposed [64]. It is based on Fanger's and adaptive theories as well as field and laboratory studies from different climate zones in China.…”

mentioning

confidence: 99%

“…It is based on Fanger's and adaptive theories as well as field and laboratory studies from different climate zones in China. The proposal included different evaluation methods for heated/cooled environments (PMV model with different assessment criteria than ISO 7730) and free-running buildings (an adaptive graphic method or an adaptive predicted mean vote may be used) [64].…”

mentioning

confidence: 99%

A review of human thermal comfort in the built environment

Rupp

Vásquez

Lamberts

2015

Energy and Buildings

695

282

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“…The occupants' thermal comfort directly influences the building energy consumption and greenhouse gas emissions [1][2][3]. Extensive studies have been conducted worldwide to reveal the mechanism of thermal comfort [4][5][6].…”

Section: Research Backgroundsmentioning

confidence: 99%

“…The SCV entropy is determined by Equation (2) and the relationship between the information entropy of SCV and the indoor air temperature is shown in Fig. 6.…”

Section: The Impact Of Indoor Air Temperature On the Scv Entropymentioning

confidence: 99%

Exploring the “black box” of thermal adaptation using information entropy

Jing

Yao

2018

Building and Environment

Self Cite

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Thermal adaptation has been interpreted well by behavioral, physiological, and psychological factors, but the mechanism and interaction between the three factors remain in the "black box". This paper aims to apply the theory of general system and information entropy to investigate the quantitative relationships of the three thermal adaptation processes. Based on the database from the field survey and laboratory experiments conducted in the hot summer and cold winter climate zone of China, three typical adaptive indices: clothing insulation (Clo), thermal sensation votes (TSV) and sensory nerve conduction velocity (SCV) were selected to calculate Clo entropy, TSV entropy, SCV entropy and total entropy. The regression models were developed between these entropies and the indoor air temperature to quantify the weights of the three adaptive categories. The models were used to compare the differences between China and Pakistan as well as between adaptive approaches and climate chamber experiments. The thermal comfort and acceptable temperature ranges were obtained using the entropy models. Our findings propose a new perspective using entropy to quantify the behaviorally, physiologically, and psychologically adaptive approaches, which contribute to a better understanding of opening the "black box" of thermal adaptation.

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An introduction to the Chinese Evaluation Standard for the indoor thermal environment

Cited by 117 publications

References 17 publications

Development of the adaptive PMV model for improving prediction performances

Development of the adaptive PMV model for improving prediction performances

A review of human thermal comfort in the built environment

Exploring the “black box” of thermal adaptation using information entropy

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