Thermal comfort modeling for smart buildings : a fine-grained deep learning approach

Abstract

The emerging Internet of Things (IoT) technology enables smart building management and operation to improve building energy efficiency and occupant thermal comfort. In this paper, we perform data analysis using the IoT generated building data to derive accurate thermal comfort model for smart building control. Deep neural network (DNN) is used to model the relationship between the controllable building operations and thermal comfort. As thermal comfort is determined by multiple comfort factors, a fine-grained architecture is proposed, where an exclusive model is trained for each factor and accordingly the corresponding thermal comfort can be evaluated. The experimental results show that the proposed fine-grained DNN outperforms its coarse-grained counterpart by 3.5× and is 1.7×, 2.5×, 2.4×, and 1.9× more accurate compared to four popular machine learning algorithms. Besides, DNN's performance promotes with deeper network topology and more neurons, and a simple topology with the same number of neurons per network hidden layer is sufficient to achieve high modeling accuracy. Finally, the derived thermal comfort model reveals a linear relationship between comfort and air conditioning setpoint. The linear property helps quickly and accurately search for the optimal controllable setpoint with the desired comfort.