Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition

Abstract

At present, non-intrusive personal thermal comfort models are receiving more and more attention. Non-intrusive sensing technology is used to accurately capture the real-time thermal state of occupants indoors so as to construct personal thermal comfort models by data-driven methods. This study developed a non-intrusive personal thermal comfort model using machine learning techniques combined with infrared facial recognition. Firstly, the Charlotte-ThermalFace database was used to extract the temperatures from six regions of interest on the face using infrared face recognition and key point extraction algorithms. Subsequently, the feature importance of the variables was calculated by random forest (RF) and gradient boosting decision tree (GBDT) respectively to explore the key parameters influencing the prediction performance of personal thermal preferences. Finally, the performance of 12 machine learning models was systematically compared, including 6 traditional models, 5 ensemble models, and 1 broad model, based on precision, recall, F1 score and macro-F1 score. The results show that the ensemble learning models and the broad learning (BL) model perform better than the traditional models by using the full training dataset size. Secondly, the BL model is applied for the first time as an alternative to deep network models for thermal preference prediction, with a prediction precision of 90.44%. Compared with traditional deep neural networks (DNN) model, it has lower computational complexity and faster training speed. Furthermore, BL and deep cascade forest (DCF) have significant advantages over other models in predicting thermal preference with different data subsets. Overall, the results of this study provide a reference for non-intrusive personal thermal comfort modeling that can be used to optimize building thermal environments.