A Method for Estimating Instantaneous Predicted Mean Vote Under Dynamic Conditions by Accounting for Thermal Inertia

Abstract

Researchers have increasingly focused on thermal comfort, examining both individuals’ thermal sensations and the percentage of people dissatisfied with the thermal environment. Most studies rely on the widely used PMV (Predicted Mean Vote) model and the PPD (Predicted Percentage of Dissatisfied) value derived from it, both defined by the ISO 7730:2005 standard. However, previous studies have shown that this standardized method only applies under steady-state conditions, which do not reflect the dynamic nature of everyday environments. As closed-loop control technologies gain prominence in building services, the need to evaluate thermal comfort under time-varying conditions has grown. The standard method does not account for the thermal inertia of the human body, which limits its applicability in such dynamic contexts. In this study, we develop a method to estimate instantaneous thermal sensation under non-stationary conditions by incorporating thermal inertia through signal processing techniques. This approach addresses a well-recognized limitation of the standard PMV–PPD model and provides a way to assess thermal comfort in real time. We collected experimental data using a thermal comfort measurement station, a thermal manikin, and human subjects in a controlled climate chamber. The proposed method enables real-time evaluation of thermal comfort in dynamic environments and offers a foundation for integration into HVAC control and comfort optimization systems.