Occupant centered thermal comfort modeling and recognition

Conventional strategies for indoor environmental control in buildings predominantly target aggregate thermal comfort metrics, often overlooking inter-individual variations in thermal perception. This oversight constitutes a key driver of thermal dissatisfaction and elevated energy consumption in HVAC systems. Accurate monitoring and real-time feedback of occupants' thermal comfort states hold substantial practical significance, enabling optimized environmental regulation that simultaneously enhances thermal satisfaction and curtails building energy use. In this book, the correlation between thermal sensation and multivariate physiological information is investigated. Various data-driven models are established for thermal sensation prediction. The focus is put on the correlation analysis between facial temperature / human posture and thermal sensation recognition. Non-invasive object detection technology and prediction method of human facial features and thermal adaptive action recognition are investigated. Finally, system design of non-invasive personal thermal comfort recognition is realized, which will help achieve efficient and balanced indoor thermal regulation.