Abstract
An adaptive Predicted Mean Vote model of thermal comfort based on “Black Box” theory is proposed, which takes into account factors such as culture, indoor climate, social, physiological, psychological and behavioral adaptations, which have an impact on the senses used to detect thermal comfort. By applying the cybernetics concept, the aPMV model shows that the Predicted Mean Vote (PMV) is greater than actual thermal comfort in free running buildings, which has been revealed by many researchers in field studies. An adaptive coefficient λ representing the adaptive factors that affect the sense of thermal comfort is proposed. The relation to environmental data and the thermal comfort is analyzed, the empirical coefficients in warm and cold conditions for the Beijing area in China are acquired based on Genetic Algorithm, which can supply theory evidence for building indoor thermal comfort model.
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