Personalized Thermal Comfort Modeling based on Support Vector Classification

Heating Ventilation and Air Conditioning (HVAC) systems are designed to provide a comfortable indoor thermal environment for the occupants. Conventionally, Predicted Mean Vote (PMV) model is used to represent thermal comfort which is only an average model and it cannot reflect the individual differences in thermal sensation. This paper proposes a personalized thermal comfort model based on a machine learning classification algorithm called Support Vector Classification (SVC). We designed an Android application to collect feedback from occupants about their thermal sensation of the indoor environment. The feedback data-set is then used to train the personalized thermal comfort model. The model accuracy has been verified by comparison between the predicted thermal sensation based on the proposed model and actual thermal sensation feedback. Accuracy of the proposed thermal comfort model shows that the proposed model has a potential to save energy as well as capturing the individual differences in thermal comfort.