摘要
In order to build a prediction model of the indoor thermal comfort for a given human group, the original predicted mean vote （PMV） equation is reconstructed and simplified, the modified PMV equation is named PMVR （PMV for region） , where five variables are needed to be fitted with the dataset of actual thermal sense of a definite human group. As the fitting algorithm, the particle swarm optimization algorithm is used to optimize the solution, and a fixed PMVR can be finally determined. Experiment results indicate that for a definite human group, PMVR is more accurate on the prediction of thermal sense compared with some other models.
In order to build a prediction model of the indoor thermal comfort for a given human group, the original predicted mean vote （PMV） equation is reconstructed and simplified, the modified PMV equation is named PMVR （PMV for region） , where five variables are needed to be fitted with the dataset of actual thermal sense of a definite human group. As the fitting algorithm, the particle swarm optimization algorithm is used to optimize the solution, and a fixed PMVR can be finally determined. Experiment results indicate that for a definite human group, PMVR is more accurate on the prediction of thermal sense compared with some other models.
基金
Sponsored by International Cooperation Project of BIT-UL (20070542002)
