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

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 devel...
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Autor*in:	Bai, Yan [verfasserIn] Liu, Liang [verfasserIn] Liu, Kai [verfasserIn] Yu, Shuai [verfasserIn] Shen, Yifan [verfasserIn] Sun, Di [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Non-intrusive measurement Infrared facial recognition Skin temperature feature extraction Thermal preference prediction Machine learning

Übergeordnetes Werk:	Enthalten in: Building and environment - New York, NY [u.a.] : Elsevier, 1976, 247
Übergeordnetes Werk:	volume:247

DOI / URN:	10.1016/j.buildenv.2023.111033

Katalog-ID:	ELV065981677

Internformat


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520			\|a 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.
650		4	\|a Non-intrusive measurement
650		4	\|a Infrared facial recognition
650		4	\|a Skin temperature feature extraction
650		4	\|a Thermal preference prediction
650		4	\|a Machine learning
700	1		\|a Liu, Liang \|e verfasserin \|4 aut
700	1		\|a Liu, Kai \|e verfasserin \|4 aut
700	1		\|a Yu, Shuai \|e verfasserin \|4 aut
700	1		\|a Shen, Yifan \|e verfasserin \|4 aut
700	1		\|a Sun, Di \|e verfasserin \|4 aut
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allfields	10.1016/j.buildenv.2023.111033 doi (DE-627)ELV065981677 (ELSEVIER)S0360-1323(23)01060-0 DE-627 ger DE-627 rda eng 690 VZ 56.00 bkl Bai, Yan verfasserin (orcid)0000-0001-5848-0826 aut Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Non-intrusive measurement Infrared facial recognition Skin temperature feature extraction Thermal preference prediction Machine learning Liu, Liang verfasserin aut Liu, Kai verfasserin aut Yu, Shuai verfasserin aut Shen, Yifan verfasserin aut Sun, Di verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 247 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:247 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.00 Bauwesen: Allgemeines VZ AR 247
spelling	10.1016/j.buildenv.2023.111033 doi (DE-627)ELV065981677 (ELSEVIER)S0360-1323(23)01060-0 DE-627 ger DE-627 rda eng 690 VZ 56.00 bkl Bai, Yan verfasserin (orcid)0000-0001-5848-0826 aut Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Non-intrusive measurement Infrared facial recognition Skin temperature feature extraction Thermal preference prediction Machine learning Liu, Liang verfasserin aut Liu, Kai verfasserin aut Yu, Shuai verfasserin aut Shen, Yifan verfasserin aut Sun, Di verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 247 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:247 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.00 Bauwesen: Allgemeines VZ AR 247
allfields_unstemmed	10.1016/j.buildenv.2023.111033 doi (DE-627)ELV065981677 (ELSEVIER)S0360-1323(23)01060-0 DE-627 ger DE-627 rda eng 690 VZ 56.00 bkl Bai, Yan verfasserin (orcid)0000-0001-5848-0826 aut Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Non-intrusive measurement Infrared facial recognition Skin temperature feature extraction Thermal preference prediction Machine learning Liu, Liang verfasserin aut Liu, Kai verfasserin aut Yu, Shuai verfasserin aut Shen, Yifan verfasserin aut Sun, Di verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 247 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:247 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.00 Bauwesen: Allgemeines VZ AR 247
allfieldsGer	10.1016/j.buildenv.2023.111033 doi (DE-627)ELV065981677 (ELSEVIER)S0360-1323(23)01060-0 DE-627 ger DE-627 rda eng 690 VZ 56.00 bkl Bai, Yan verfasserin (orcid)0000-0001-5848-0826 aut Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Non-intrusive measurement Infrared facial recognition Skin temperature feature extraction Thermal preference prediction Machine learning Liu, Liang verfasserin aut Liu, Kai verfasserin aut Yu, Shuai verfasserin aut Shen, Yifan verfasserin aut Sun, Di verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 247 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:247 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.00 Bauwesen: Allgemeines VZ AR 247
allfieldsSound	10.1016/j.buildenv.2023.111033 doi (DE-627)ELV065981677 (ELSEVIER)S0360-1323(23)01060-0 DE-627 ger DE-627 rda eng 690 VZ 56.00 bkl Bai, Yan verfasserin (orcid)0000-0001-5848-0826 aut Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Non-intrusive measurement Infrared facial recognition Skin temperature feature extraction Thermal preference prediction Machine learning Liu, Liang verfasserin aut Liu, Kai verfasserin aut Yu, Shuai verfasserin aut Shen, Yifan verfasserin aut Sun, Di verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 247 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:247 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 56.00 Bauwesen: Allgemeines VZ AR 247
language	English
source	Enthalten in Building and environment 247 volume:247
sourceStr	Enthalten in Building and environment 247 volume:247
format_phy_str_mv	Article
bklname	Bauwesen: Allgemeines
institution	findex.gbv.de
topic_facet	Non-intrusive measurement Infrared facial recognition Skin temperature feature extraction Thermal preference prediction Machine learning
dewey-raw	690
isfreeaccess_bool	false
container_title	Building and environment
authorswithroles_txt_mv	Bai, Yan @@aut@@ Liu, Liang @@aut@@ Liu, Kai @@aut@@ Yu, Shuai @@aut@@ Shen, Yifan @@aut@@ Sun, Di @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	300188773
dewey-sort	3690
id	ELV065981677
language_de	englisch
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author	Bai, Yan
spellingShingle	Bai, Yan ddc 690 bkl 56.00 misc Non-intrusive measurement misc Infrared facial recognition misc Skin temperature feature extraction misc Thermal preference prediction misc Machine learning Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition
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topic_title	690 VZ 56.00 bkl Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition Non-intrusive measurement Infrared facial recognition Skin temperature feature extraction Thermal preference prediction Machine learning
topic	ddc 690 bkl 56.00 misc Non-intrusive measurement misc Infrared facial recognition misc Skin temperature feature extraction misc Thermal preference prediction misc Machine learning
topic_unstemmed	ddc 690 bkl 56.00 misc Non-intrusive measurement misc Infrared facial recognition misc Skin temperature feature extraction misc Thermal preference prediction misc Machine learning
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title	Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition
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title_full	Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition
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title_sort	non-intrusive personal thermal comfort modeling: a machine learning approach using infrared face recognition
title_auth	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition
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author2	Liu, Liang Liu, Kai Yu, Shuai Shen, Yifan Sun, Di
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Non-intrusive personal thermal comfort modeling: A machine learning approach using infrared face recognition

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