An indoor thermal comfort model for group thermal comfort prediction based on K-means++ algorithm
•K-means++ algorithm was first used to build a thermal comfort model without occupants’ feedback.•Even with small dataset, the proposed model achieved high accurancy.•Mahalanobis distance algorithm was used for outlier test, reducing errors and avoiding excessive reduction of data set.•The model cou...
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| Published in: | Energy and buildings Vol. 327; p. 115000 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.01.2025
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| ISSN: | 0378-7788 |
| Online Access: | Get full text |
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| Abstract | •K-means++ algorithm was first used to build a thermal comfort model without occupants’ feedback.•Even with small dataset, the proposed model achieved high accurancy.•Mahalanobis distance algorithm was used for outlier test, reducing errors and avoiding excessive reduction of data set.•The model could effectively predict group thermal comfort by using only three parameters: Clo, Ta, and RH.
Predicting indoor thermal comfort plays an essential role in controlling energy consumption in buildings. Existing studies have used supervised machine learning to predict thermal comfort, which were more accurate than traditional models. However, these models required occupants’ subjective feedback for model training, which reduced the accuracy of the model. In this study, a prediction model that didn’t require feedback was proposed for the first time using the K-means++ algorithm based on the ASHRAE Global Thermal Comfort Database II. Firstly, the data quality was improved through feature selection, dimensional processing, and feature weighting. Then the influence of different outlier judgment methods, feature weight and data set size on model accuracy were compared. Finally, the K-means++ algorithm was applied for thermal comfort clustering analysis. The result showed that the model with an accuracy higher than 90 % could be constructed using only three factors (CLO, TA, RH), and the proposed model could predict indoor group thermal comfort reliably, and provide a foundation for the indoor thermal sensation evaluation. |
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| AbstractList | •K-means++ algorithm was first used to build a thermal comfort model without occupants’ feedback.•Even with small dataset, the proposed model achieved high accurancy.•Mahalanobis distance algorithm was used for outlier test, reducing errors and avoiding excessive reduction of data set.•The model could effectively predict group thermal comfort by using only three parameters: Clo, Ta, and RH.
Predicting indoor thermal comfort plays an essential role in controlling energy consumption in buildings. Existing studies have used supervised machine learning to predict thermal comfort, which were more accurate than traditional models. However, these models required occupants’ subjective feedback for model training, which reduced the accuracy of the model. In this study, a prediction model that didn’t require feedback was proposed for the first time using the K-means++ algorithm based on the ASHRAE Global Thermal Comfort Database II. Firstly, the data quality was improved through feature selection, dimensional processing, and feature weighting. Then the influence of different outlier judgment methods, feature weight and data set size on model accuracy were compared. Finally, the K-means++ algorithm was applied for thermal comfort clustering analysis. The result showed that the model with an accuracy higher than 90 % could be constructed using only three factors (CLO, TA, RH), and the proposed model could predict indoor group thermal comfort reliably, and provide a foundation for the indoor thermal sensation evaluation. |
| ArticleNumber | 115000 |
| Author | Dong, Qi Yan, Bin Yin, Qing Liu, Ying Li, Xiangru Sun, Cheng |
| Author_xml | – sequence: 1 givenname: Ying orcidid: 0000-0002-5175-0202 surname: Liu fullname: Liu, Ying email: liuying8361@163.com organization: School of Architecture and Design, Harbin Institute of Technology, Harbin 150006, Heilongjiang, People's Republic of China – sequence: 2 givenname: Xiangru surname: Li fullname: Li, Xiangru email: lixiangru620237@163.com organization: School of Architecture and Design, Harbin Institute of Technology, Harbin 150006, Heilongjiang, People's Republic of China – sequence: 3 givenname: Cheng orcidid: 0000-0003-1365-2780 surname: Sun fullname: Sun, Cheng email: suncheng@hit.edu.cn organization: School of Architecture and Design, Harbin Institute of Technology, Harbin 150006, Heilongjiang, People's Republic of China – sequence: 4 givenname: Qi orcidid: 0000-0001-6587-8074 surname: Dong fullname: Dong, Qi email: dongqi@hit.edu.cn organization: School of Architecture and Design, Harbin Institute of Technology, Harbin 150006, Heilongjiang, People's Republic of China – sequence: 5 givenname: Qing surname: Yin fullname: Yin, Qing email: hityin@126.com organization: School of Architecture and Design, Harbin Institute of Technology, Harbin 150006, Heilongjiang, People's Republic of China – sequence: 6 givenname: Bin surname: Yan fullname: Yan, Bin email: byan@gsd.Harvard.edu organization: Graduate School of Design, Harvard University, Cambridge 021382, MA, United States of America |
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| Keywords | ANN K-means++ clustering algorithm CLO Prediction model KNN WGBT HR DBI SVM DT SC Indoor thermal comfort CC SSE TOA IQR EEM TA TSK TC NB RF RH RI Machine learning VEL TP MET TR TS |
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| Title | An indoor thermal comfort model for group thermal comfort prediction based on K-means++ algorithm |
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