Evaluation of the accuracy of PMV and its several revised models using the Chinese thermal comfort Database

The predicted mean vote (PMV) and its several revised models are widely used for the prediction of ther-mal comfort. This study aims to assess their performances using the Chinese Thermal Comfort Database (N = 41977). In air-conditioned buildings, the PMV prediction accuracy (P) and the mean absolute error (MAE) are 41.2 % and 0.86, respectively, which is better than the performance in free-running buildings (P = 31.9 %, MAE = 1.09). The performance of the PMV model is also tested under different HVAC modes, climate zones, and building types. The prediction accuracy varies but does not exceed 60 % for all subset cases. Three typical revised models (ePMV, nPMV and aPMV) considering thermal adaptation show better accuracy than the PMV, but the improvements are still limited and do not exceed 5 %. It appears that the PMV and revised models are reliable under thermal neutrality conditions, while their accuracy decreased towards the ends of the thermal sensation scale, especially on the cooler side. For further improvement of the prediction performance, it may be necessary to consider the effect of thermal adaptation in parallel with other approaches, such as revising the PMV core structure and considering individual differences.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study assessed the performance of PMV and its revised models using the Chinese Thermal Comfort Database, finding better accuracy in air-conditioned buildings but limited improvements in different subset cases.