Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements
In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time change...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shan, Xin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives - Plonowska, Karolina A. ELSEVIER, 2018, an international journal of research applied to energy efficiency in the built environment, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:225 ; year:2020 ; day:15 ; month:10 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.enbuild.2020.110305 |
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ELV051455420 |
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| 520 | |a In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. | ||
| 520 | |a In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. | ||
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10.1016/j.enbuild.2020.110305 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001138.pica (DE-627)ELV051455420 (ELSEVIER)S0378-7788(20)31199-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Shan, Xin verfasserin aut Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. Thermal comfort Elsevier Supervised learning Elsevier Machine learning Elsevier Human sensing Elsevier electroencephalogram (EEG) Elsevier Yang, En-Hua oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:225 year:2020 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2020.110305 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 225 2020 15 1015 0 |
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10.1016/j.enbuild.2020.110305 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001138.pica (DE-627)ELV051455420 (ELSEVIER)S0378-7788(20)31199-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Shan, Xin verfasserin aut Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. Thermal comfort Elsevier Supervised learning Elsevier Machine learning Elsevier Human sensing Elsevier electroencephalogram (EEG) Elsevier Yang, En-Hua oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:225 year:2020 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2020.110305 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 225 2020 15 1015 0 |
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10.1016/j.enbuild.2020.110305 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001138.pica (DE-627)ELV051455420 (ELSEVIER)S0378-7788(20)31199-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Shan, Xin verfasserin aut Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. Thermal comfort Elsevier Supervised learning Elsevier Machine learning Elsevier Human sensing Elsevier electroencephalogram (EEG) Elsevier Yang, En-Hua oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:225 year:2020 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2020.110305 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 225 2020 15 1015 0 |
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10.1016/j.enbuild.2020.110305 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001138.pica (DE-627)ELV051455420 (ELSEVIER)S0378-7788(20)31199-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Shan, Xin verfasserin aut Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. Thermal comfort Elsevier Supervised learning Elsevier Machine learning Elsevier Human sensing Elsevier electroencephalogram (EEG) Elsevier Yang, En-Hua oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:225 year:2020 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2020.110305 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 225 2020 15 1015 0 |
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10.1016/j.enbuild.2020.110305 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001138.pica (DE-627)ELV051455420 (ELSEVIER)S0378-7788(20)31199-3 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Shan, Xin verfasserin aut Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. Thermal comfort Elsevier Supervised learning Elsevier Machine learning Elsevier Human sensing Elsevier electroencephalogram (EEG) Elsevier Yang, En-Hua oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:225 year:2020 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2020.110305 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 225 2020 15 1015 0 |
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610 VZ 44.94 bkl Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements Thermal comfort Elsevier Supervised learning Elsevier Machine learning Elsevier Human sensing Elsevier electroencephalogram (EEG) Elsevier |
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Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives |
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10.1016/j.enbuild.2020.110305 |
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supervised machine learning of thermal comfort under different indoor temperatures using eeg measurements |
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Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements |
| abstract |
In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. |
| abstractGer |
In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. |
| abstract_unstemmed |
In this paper, machine learning techniques in conjunction with passive EEG (electroencephalogram) measurement were explored to classify occupants’ real-time thermal comfort states, which have the potential in the future for energy saving through adopting time varying set points when real-time changes in thermal comfort can require less energy input. The performances of different machine learning techniques were compared, and the method to select linear continuous features for class interpolation was also explored. For the full-set features, the performances of different classifiers were satisfactory, with classification rates all above 90%. The LDA classifier had the best performance. The second best was the NB classifier, and the relatively worst was the KNN classifier. The linear continuous EEG features were selected by interpolation and can be found for all human subjects. Higher selection threshold led to less selected features but higher average performance of these features. In general, the EEG based machine learning methods can classify occupants’ real-time thermal comfort states, and could potentially lead to more building energy saving through comfort-driven time varying set points. |
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Supervised machine learning of thermal comfort under different indoor temperatures using EEG measurements |
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