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Title: Machine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singapore
Authors: Chaudhuri, Tanaya
Soh, Yeng Chai
Li, Hua
Xie, Lihua
Keywords: Thermal comfort prediction
Machine learning
Issue Date: 2017
Source: 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).
Conference: 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC 2017)
Abstract: 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.
Schools: School of Electrical and Electronic Engineering 
School of Mechanical and Aerospace Engineering 
Interdisciplinary Graduate School (IGS) 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
ERI@N Conference Papers
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MAE Conference Papers

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