Development of the adaptive PMV model for improving prediction performances

Abstract Predicted mean vote (PMV) is widely used in order to evaluate thermal comfort conditions of indoor spaces. However, recent studies show that there are discrepancies between PMV prediction and the actual mean vote (AMV) of occupants in buildings. This study has an aim to develop two types of adaptive PMV models considering physical stimulus on the human's thermal sensation and adaptive behavior. A field measurement was conducted to reveal occupant perception on and responses to indoor thermal environments and to assess the applicability of the original PMV model. We have developed two types of Adaptive PMV models based on the black-box theory and the adaptive thermal comfort theory in air-conditioned buildings. The adaptive PMV (aPMV) model based on the black-box theory shows good prediction performance only when the original PMV value ranges from −1.5 to +1.5. On the other hand, the prediction results from New PMV (nPMV) based on the adaptive comfort theory are in good agreement with actual thermal sensation of people and are reliable. Our results imply that the nPMV model could increase the prediction performance of the original PMV model and play an important role in reducing the cooling energy of air-conditioned buildings.