Evaluation of thermal control based on spatial thermal comfort with reconstructed environmental data

Abstract

Achieving thermal comfort while maintaining energy efficiency is a critical objective in building control. Conventional thermal comfort models, such as the Predicted Mean Vote (PMV), rely on both environmental and personal variables. However, the use of fixed-location sensors limits the ability to capture spatial variability, thereby reducing the accuracy of occupant-specific comfort estimation. To address this limitation, this study proposes a new PMV estimation method that utilizes spatial environmental data reconstructed using the Gappy Proper Orthogonal Decomposition (Gappy POD) algorithm. In addition, a novel control framework based on group PMV is developed to represent the thermal comfort of multiple occupants. The Gappy POD method enables fast and accurate reconstruction of indoor temperature fields from sparse sensor measurements. Using these reconstructed fields and occupant location data, spatially resolved PMV values are calculated. Group-level thermal conditions are then derived through statistical aggregation methods and used to control indoor temperature in a multi-occupant living lab environment. Experimental results show that the Gappy POD algorithm achieves an average relative error below 3% in temperature reconstruction. PMV distributions varied by up to 1.26 scale units depending on occupant location. Moreover, thermal satisfaction outcomes varied depending on the group PMV method employed. These findings underscore the importance for adaptive thermal control strategies that incorporate both spatial and individual variability, offering valuable insights for future occupant-centric building operations.