Thermal Comfort

Thermal comfort research aims to understand and optimize the thermal environment for human well-being and energy efficiency. Current research focuses on personalized models using machine learning (including deep learning, support vector machines, and random forests), often incorporating data from various sensors and leveraging techniques like active learning to reduce data collection needs and improve model accuracy. This field is crucial for improving building design, HVAC control systems, and urban planning, ultimately impacting energy consumption, occupant health, and overall societal sustainability. Furthermore, research is exploring the integration of thermal comfort with other factors like occupancy patterns and user preferences to create more holistic and effective solutions.