Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82140
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dc.contributor.authorChaudhuri, Tanayaen
dc.contributor.authorSoh, Yeng Chaien
dc.contributor.authorLi, Huaen
dc.contributor.authorXie, Lihuaen
dc.date.accessioned2017-07-21T05:02:42Zen
dc.date.accessioned2019-12-06T14:47:29Z-
dc.date.available2017-07-21T05:02:42Zen
dc.date.available2019-12-06T14:47:29Z-
dc.date.issued2017en
dc.identifier.citationChaudhuri, 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).en
dc.identifier.urihttps://hdl.handle.net/10356/82140-
dc.description.abstractMajority 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.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.format.extent6 p.en
dc.language.isoenen
dc.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.en
dc.subjectThermal comfort predictionen
dc.subjectMachine learningen
dc.titleMachine Learning based Prediction of Thermal Comfort in Buildings of Equatorial Singaporeen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en
dc.contributor.conference2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC 2017)en
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en
dc.description.versionAccepted versionen
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Appears in Collections:EEE Conference Papers
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MAE Conference Papers
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