ThermCont: A machine Learning enabled Thermal Comfort Control Tool in a real time

Abstract

Occupants’ thermal comfort assessment is becoming a crucial research topic since it aims not only at improving indoor thermal comfort but also to save energy in both commercial and residential buildings. Hence, it makes buildings more sustainable. Predicted Mean Vote (PMV) model is considered as the most recognized in thermal comfort standards and was widely used to estimate thermal sensation of occupants. However, few works are dealing with the assessment and control of occupants’ thermal comfort in real time and most of them do not provide mechanisms to improve occupants’ comfort in case of detecting indoor thermal discomfort. In this paper, we propose ThermCont a novel machine learning based tool to predict and control occupants’ thermal comfort through the PMV model, in real time. Our tool uses multiple linear regression algorithm and is based on findings from a one-year longitudinal case study of occupants’ thermal comfort in office building. Moreover, we also propose a new genetic algorithm based scheme to optimize parameters values of thermal comfort, when observing occupants’ thermal discomfort, and hence to improve the indoor thermal comfort. The experimental results show the efficiency of ThermCont in terms of prediction accuracy and time complexity when compared to other machine learning algorithms, in addition to its ability to control and improve occupants’ thermal comfort in real time.