Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition
https://orcid.org/0000-0001-5848-0826
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.
Highlights Achieves non-intrusive measurement and modeling of personal thermal comfort. The key parameters influencing the prediction of personal thermal preferences were identified. Broad learning (BL) model with the best performance was determined for future personal thermal preference prediction. The optimal prediction model suitable for different distances are provided to adapt to the dynamic changes in the user's spatial location.
Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition
https://orcid.org/0000-0001-5848-0826
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.
Highlights Achieves non-intrusive measurement and modeling of personal thermal comfort. The key parameters influencing the prediction of personal thermal preferences were identified. Broad learning (BL) model with the best performance was determined for future personal thermal preference prediction. The optimal prediction model suitable for different distances are provided to adapt to the dynamic changes in the user's spatial location.
Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition
https://orcid.org/0000-0001-5848-0826
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.
Highlights Achieves non-intrusive measurement and modeling of personal thermal comfort. The key parameters influencing the prediction of personal thermal preferences were identified. Broad learning (BL) model with the best performance was determined for future personal thermal preference prediction. The optimal prediction model suitable for different distances are provided to adapt to the dynamic changes in the user's spatial location.
Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition
Bai, Yan (Autor:in) / Liu, Liang (Autor:in) / Liu, Kai (Autor:in) / Yu, Shuai (Autor:in) / Shen, Yifan (Autor:in) / Sun, Di (Autor:in)
Building and Environment ; 247
13.11.2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| British Library Online Contents | 2016