Preliminary study of learning individual thermal complaint behavior using one-class classifier for indoor environment control

This paper proposes a data-driven learning method to describe the personal thermal complaint behavior in a complaint-driven environment control system. The complaint-driven system only uses personal human complaints to control the personal office environment. It avoids the user's direct control on the set-point of the room, which usually results in unreasonable and uncomfortable set-point. A two-stage classifier model is proposed, using personal thermal compliant data with respect to the transient and steady complaint behaviors. The classifier structure is developed based on the properties of human thermal perception with parameters to learn for different users. Quantitative results using experimental data show that the model has lower false negative rate than traditional data-driven classification model and acceptable false detection rate. Practical implementation and subjects' questionnaire evaluation demonstrate the satisfying performance of the model in real environment control. We also discuss the limitations and potential extensions of the model at the end of this paper.