A Low-Cost and Scalable Personalized Thermal Comfort Estimation System in Indoor Environments

Abstract

In commercial buildings, occupant thermal comfort is a key factor that must be optimized to provide a comfortable and productive work environment. However, current methods largely estimate thermal comfort based on preset models which do not incorporate real-time measurements or individual thermal preferences. In this work, we present a scalable system for estimating personalized thermal comfort using low-cost thermal camera based sensor nodes. This system extracts non-intrusive thermal measurements, is robust to different perspectives and environments, is easily deployable and low-cost, and can incorporate individual thermal feedback for more personalized thermal comfort estimates. In comparison with baseline methods, our system is able to improve thermal comfort estimates on the ASHRAE 7-point thermal sensation scale by 64% over baseline methods.