iTCM: Toward Learning-Based Thermal Comfort Modeling via Pervasive Sensing for Smart Buildings

For decades, ASHRAE Standard 55 has been using the Fanger's predicted mean vote (PMV) model to evaluate the indoor thermal comfort satisfaction. However, this canonical model has drawbacks in both data inadequacy and lack of inputs from test subjects. In this paper, we propose a learning-based solution for thermal comfort modeling via the emerging machine learning techniques and Internet of Things-based pervasive sensing technologies. First, we build an intelligent thermal comfort management (iTCM) system. It adopts the wireless sensor network to collect environmental data and utilizes the wearable device for vital sign monitoring. In addition, a cloud-based back-end system, with cost efficient deployment fees, is developed for data management and analysis. Second, we implement a black-box neural network (NN), namely the intelligent thermal comfort NN (ITCNN). To evaluate the performance of ITCNN, we compare it with the PMV model, three traditional white-box machine learning approaches and three classical black-box machine learning methods. Our preliminary results show that four black-box methods achieve better performance than the PMV model and the three white-box approaches. The ITCNN achieves the best performance and outperforms the PMV model by on average 13.1% and up to 17.8%. Third, with the iTCM system, we demonstrate a novel deep reinforcement learning-based application by encouraging human behavioral changes to form energy-saving habits for greener, smarter, and healthier building. Finally, we discuss the limitations of this paper and present the plan for our future research.