Heterogeneous Transfer Learning for Thermal Comfort Modeling

Abstract

For decades, the Predicted Mean Vote (PMV) model has been adopted to evaluate building occupants' thermal comfort. However, recent studies argue that the PMV model is inaccurate and suffers from two major issues: thermal comfort parameter inadequacy and modeling data inadequacy. To overcome these issues, in this paper, we propose a learning-based approach for thermal comfort modeling, named as Heterogeneous Transfer Learning (HTL) based Intelligent Thermal Comfort Neural Network (HTL-ITCNN). First, to address the parameter inadequacy issue, we add more relevant factors as the modeling features except for the six PMV parameters. Due to the flexibility of learning-based approaches, newly found thermal comfort parameters can be appended to extend the number of modeling features. Second, to mitigate the impact of the data inadequacy issue, we adopt the deep transfer learning techniques to train the thermal comfort model, where the model training would benefit from the transferred knowledge from the existing datasets. Due to the heterogeneity of the features among different datasets, we follow the HTL concept to conducting effective knowledge transfer among heterogeneous domains, which are the different but related datasets with varied features. To validate our solution, we conduct five-month data collection experiments and build our datasets. With the HTL-based two-stage learning paradigm, the experimental results show that the accuracy of HTL-ITCNN outperforms the PMV model by on average 73.9%. Besides, we verify the impacts of newly added features and knowledge transfer on model performance. Moreover, we demonstrate the enormous potential of personal thermal comfort modeling research.