Predicted Mean Vote with skin wettedness from standard effective temperature model

Abstract

Predicted Mean Vote (PMV) predicts thermal sensation for a group of people from the human thermal load, and is well recognized by thermal comfort standards for energy-efficient design of thermally comfortable buildings. However, the oversimplified skin evaporative heat loss in the thermal load calculation contributes to the frequently reported discrepancies between the PMV and the real thermal sensation vote. This study modifies the PMV by using the skin wettedness from the standard effective temperature model. The standard effective temperature model, with advanced human thermoregulation, reasonably predicts the skin wettedness based on the core temperature, skin temperature, and peripheral blood flow. The skin wettedness is used to calculate the skin evaporative heat loss to replace the oversimplified one. The modified PMV with the improved skin evaporative heat loss is validated by the largest thermal comfort database, i.e., ASHRAE Global Thermal Comfort Database II, for different types of climate, buildings, and HVAC systems. Compared with the original PMV, the modified PMV improves the overall accuracy and robustness in thermal sensation prediction by 64% and 32% respectively. This study contributes to improving the PMV for the updating of thermal comfort standards.