Evaluation of thermal sensation models for predicting thermal comfort in dynamic outdoor and indoor environments

Abstract

Thermal sensation models are commonly used to assess thermal perception in various indoor environments. Our previous work developed a new model to predict thermal sensation in cars that uses gradual change in thermal load on the face, sudden change in solar radiation on the face, mean skin temperature and outdoor air temperature as predictors. The present investigation selected 11 outdoor scenarios and 20 indoor scenarios from the literature to further verify the accuracy of the thermal sensation model. Four other thermal models, the predicted mean vote (PMV) model, the dynamic thermal sensation (DTS) model, a model from the University of California, Berkeley (UCB), and a transient outdoor thermal comfort model (Lai's) were compared with the new model for the 32 scenarios. The results confirmed the validity of the new model in an outdoor environment with sudden change in solar radiation. The new model was able to predict the trend of thermal changes, but the accuracy was not as good as that of the PMV model in an environment with indoor temperature gradient/sudden changes.