A new method for determining the optimal adaptive thermal comfort model with grey relational analysis

The adaptive thermal comfort (ATC) model can be used not only to guide building design but also to achieve energy savings by specifying the temperature at the air conditioning set point throughout the year. There are many versions of ATC models, but few studies have explored how to determine the optimal prediction model. This paper proposes a method to determine the optimal model from the perspective of statistics and uses an actual case to illustrate the method. Through the investigation of 7 buildings with natural ventilation, 1331 valid questionnaires were obtained in combination with the instrument test. The optimal ATC model was obtained using grey relational analysis. The effectiveness of the optimal model was verified by comparing the predicted and measured values of the neutral temperature. The results show that the optimal ATC model determined by the method presented in the article is effective, and the best model for the whole year can explain 69.6% of the observed values. The prediction accuracy and stability of the model established separately by climate region are better than those of the general model. Compared with the annual model, the prediction accuracy of the seasonal models improved by 7.7% (winter), 13.6% (summer), and 8.5% (mid-season), and the prediction accuracy of the gender models improved by 0.7% (male) and 0.6% (female).