A Contactless Non-Intrusive Approach for Machine Learning-Based Personalized Thermal Comfort Prediction

Indoor environmental conditions play a significant role in protecting occupants’ well-being. The thermal characteristics are one of the primary factors of Indoor Environmental Qualities (IEQ) that can influence occupants’ health. In this regard, schedule-based and predefined environmental control is one of the main reasons for the current discomfort and dissatisfaction with the thermal environment. Recent research is attempting to leverage occupants’ demand in the control loop of the buildings to consider the well-being of each individual based on their own physiological properties. These thermal comfort models are called "personalized comfort models". In this regard, studies are trying to utilize skin temperature recorded by infrared thermal cameras for developing personal comfort models through machine learning prediction algorithms. However, some critical gaps in the current methods have limited the application of this platform in real buildings. The contribution of this dissertation is in the three main aspects of literature review, data collection, and model development. This study presents a comprehensive and systematic review of the current machine learning-based personalized thermal comfort studies. In addition, we introduce "Charlotte-ThermalFace", our recently developed dataset, and how it addresses some of the existing gaps in the subject. Charlotte-ThermalFace contains more than 10,000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. Using this dataset, we have developed a personalized comfort model for subjects farther away in a completely non-intrusive method.