Application of logistic function in a new PMV modification model for elderly people: Combining age and TSV

The accurate prediction of thermal perception paves the way to create a thermal comfort environment. This study proposes a modified predicted mean vote (PMV) model for elderly people. Data from three age groups in two pensioners’ buildings are collected and divided: 80 % for calculating key parameters (B and BT) via logistic function, establishing the mPMV model, and 20 % for validation data. The model is further validated by a subset of the ASHRAE Global Thermal Comfort Database II. The mPMV model demonstrated improved prediction quality over the original PMV model, reducing mean mean absolute error (MAE), standard deviation (SD), and coefficient of variation of root mean square error (CVRMSE) by 0.67, 0.36, and 1.26, respectively, in the experimental data. However, due to various scene classifications in the ASHRAE data, the prediction quality of the mPMV model remains basically unchanged compared with the PMV model, with minor increases in MAE and CVRMSE and a decrease in SD. With the elimination of the climate differences, the mean MAE, SD and CVRMSE of the mPMV model reduces by 0.19, 0.16 and 0.29 compared with the PMV model. In addition, the mPMV model outperforms the other three modified models across all three age groups of the experimental data, but this advantage is only observed in the younger elderly group of the ASHRAE data. This study can offer methodological assistance in predicting the thermal sensation for elderly people and contribute to creating a thermal environment accordant to their thermal preferences.