Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore
Majority of energy consumption in Singapore buildings is due to air-conditioning, because of its hot and humid weather. Besides attaining a healthy indoor environment, a prior knowledge about the occupant’s thermal comfort can be beneficial in reducing energy consumption, as it can save energy which...
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sg-ntu-dr.10356-821402021-01-10T11:04:40Z Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore Chaudhuri, Tanaya Soh, Yeng Chai Li, Hua Xie, Lihua School of Electrical and Electronic Engineering School of Mechanical and Aerospace Engineering Interdisciplinary Graduate School (IGS) 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC 2017) Energy Research Institute @ NTU (ERI@N) Thermal comfort prediction Machine learning Majority of energy consumption in Singapore buildings is due to air-conditioning, because of its hot and humid weather. Besides attaining a healthy indoor environment, a prior knowledge about the occupant’s thermal comfort can be beneficial in reducing energy consumption, as it can save energy which is otherwise spent in extra cooling. This paper proposes a data-driven approach to predict individual thermal comfort level (‘cool-discomfort’, ‘comfort’, ‘warm-discomfort’) using environmental and human factors as input. Six types of classifiers have been implemented- Support Vector Machine (SVM), Artificial Neural Network (ANN), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Classification Trees (CT), on a publicly available database of 817 occupants for air-conditioned and free-running buildings separately. Results show that our approach achieves prediction accuracies of 73.14-81.2%, outperforming the traditional Fanger’s PMV (Predicted Mean Vote) model, which has accuracies of only 41.68-65.5%. Age, gender, and outdoor effective temperature, which are not included in the PMV model, are found to be important factors for thermal comfort. The proposed approach also outperforms modified PMV models- the extended PMV model and the adaptive PMV model which attain accuracies of 61.75% and 35.51% respectively. NRF (Natl Research Foundation, S’pore) Accepted version 2017-07-21T05:02:42Z 2019-12-06T14:47:29Z 2017-07-21T05:02:42Z 2019-12-06T14:47:29Z 2017 Conference Paper Chaudhuri, T., Soh, Y. C., Li, H., & Xie, L. (2017). Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore. 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC 2017). https://hdl.handle.net/10356/82140 http://hdl.handle.net/10220/42964 en © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 6 p. application/pdf |
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Thermal comfort prediction Machine learning Chaudhuri, Tanaya Soh, Yeng Chai Li, Hua Xie, Lihua Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore |
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Majority of energy consumption in Singapore buildings is due to air-conditioning, because of its hot and humid weather. Besides attaining a healthy indoor environment, a prior knowledge about the occupant’s thermal comfort can be beneficial in reducing energy consumption, as it can save energy which is otherwise spent in extra cooling. This paper proposes a data-driven approach to predict individual thermal comfort level (‘cool-discomfort’, ‘comfort’, ‘warm-discomfort’) using environmental and human factors as input. Six types of classifiers have been implemented- Support Vector Machine (SVM), Artificial Neural Network (ANN), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Classification Trees (CT), on a publicly available database of 817 occupants for air-conditioned and free-running buildings separately. Results show that our approach achieves prediction accuracies of 73.14-81.2%, outperforming the traditional Fanger’s PMV (Predicted Mean Vote) model, which has accuracies of only 41.68-65.5%. Age, gender, and outdoor effective temperature, which are not included in the PMV model, are found to be important factors for thermal comfort. The proposed approach also outperforms modified PMV models- the extended PMV model and the adaptive PMV model which attain accuracies of 61.75% and 35.51% respectively. |
author2 |
School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Chaudhuri, Tanaya Soh, Yeng Chai Li, Hua Xie, Lihua |
format |
Conference or Workshop Item |
author |
Chaudhuri, Tanaya Soh, Yeng Chai Li, Hua Xie, Lihua |
author_sort |
Chaudhuri, Tanaya |
title |
Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore |
title_short |
Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore |
title_full |
Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore |
title_fullStr |
Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore |
title_full_unstemmed |
Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore |
title_sort |
machine learning based prediction of thermal comfort in buildings of equatorial singapore |
publishDate |
2017 |
url |
https://hdl.handle.net/10356/82140 http://hdl.handle.net/10220/42964 |
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1690658343391068160 |