An adaptive predicted percentage dissatisfied model based on the air-conditioner turning-on behaviors in the residential buildings of China

Abstract

Abstract To ease the Predicted Percentage of Dissatisfied (PPD) measurement and calculation and consider the adaptive behaviors of residents, the present research proposed an adaptive thermal discomfort evaluation model: Air-conditioner based Adaptive Predicted Percentage of Dissatisfied (aaPPD). First, the indoor temperature, outdoor temperature, and humidity data of the residential buildings in five cities within three climate regions in China were collected. Second, through the air-conditioner-turning-on (ATO) judgment algorithm, the data from when the air conditioner was turned on could be extracted from the original data, and then transformed via the Monte Carlo sampling method to obtain a dataset of the ATO percentage of residents within specific indoor and outdoor environments. Finally, a nonlinear model was built according to this dataset. The final R2 of this model was 0.833. This model utilized data from resident ATO behaviors as the basis for determining the thermal discomfort and avoiding the psychological impact on the subjects when filling out the thermal sensation vote questionnaire. Moreover, when compared with the PPD model, the aaPPD model simplified the variables to obtain the calculation parameters more conveniently and ease the thermal discomfort testing and predictions, which could allow for better adaptation to the early architectural design stage working characteristics.