Improved algorithm for adaptive coefficient of adaptive Predicted Mean Vote (aPMV)

Accurate prediction of thermal comfort is the premise of proper management of the indoor environment to avoid overcooling/overheating discomfort with energy saving. Adaptive Predicted Mean Vote (aPMV) has been verified for the thermal comfort prediction in both free-running and air-conditioned buildings, and has been stipulated in the thermal comfort standard of China. However, the aPMV has a problem that it would underestimate thermal discomfort, particularly in cold and warm environments. This study firstly contributes to identifying and mathematically explaining the problem of the aPMV that it is caused by the original algorithm for the adaptive coefficient, and further contributes to developing a new algorithm for the adaptive coefficient to improve the aPMV in the thermal comfort prediction. It is found that the original algorithm has an imbalanced weighting factor which weights more the accuracy of the thermal comfort prediction for the thermal environment closer to the thermal neutrality. The imbalanced weighting factor results in the problem of the aPMV. The proposed algorithm mitigates the problem of the aPMV by balancing the weighting factor. From the proposed algorithm, the adaptive coefficient is explicitly expressed as a function of the PMV and thermal sensation vote. Case studies on free-running and air-conditioned buildings demonstrate that the proposed algorithm for the adaptive coefficient improves the accuracy and robustness of the aPMV in the thermal comfort prediction up to 61.2% and 65.7% respectively. Since the proposed algorithm expresses the adaptive coefficient in an explicit and simple form, it is convenient for practical applications.