Evaluation of Thermal Control Based on Spatial Thermal Comfort with Reconstructed Environmental Data

TL;DR

This work tackles spatial variability in thermal comfort by reconstructing indoor temperature fields from sparse sensors using Gappy POD, enabling location-specific PMV calculations. It integrates real-time personal-factor estimates from AI-based vision models to compute PMV for individual occupants and proposes three group PMV aggregation methods to guide multi-occupant control. In a living-lab, the method achieves sub-3% temperature reconstruction error and reveals PMV disparities up to 1.26 units across space, demonstrating the value of occupant-location-aware control. The study further compares group-PMV control strategies, finding that MAD-based aggregation offers robust subjective comfort while acknowledging trade-offs among other methods, underscoring the need for adaptive, spatially aware thermal management in shared environments.

Abstract

Achieving thermal comfort while maintaining energy efficiency is a critical objective in building system 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, which reduces the accuracy of occupant-specific comfort estimation. To address this limitation, this study proposes a new PMV estimation method that incorporates spatial environmental data reconstructed using the Gappy Proper Orthogonal Decomposition (Gappy POD) algorithm. In addition, a group PMV-based control framework is developed to account for 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.

Figures (15)

• Figure 1: Examples of PF model outputs for estimating personal factors.
• Figure 2: Overview of the living lab environment
• Figure 3: Layout of the living lab
• Figure 4: Thermal control sequence
• Figure 5: Thermal control algorithm