Publication:
Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors

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dc.citedby3
dc.contributor.authorMohamed Salleh F.H.en_US
dc.contributor.authorSaripuddin M.B.en_US
dc.contributor.authorBin Omar R.en_US
dc.contributor.authorid26423229000en_US
dc.contributor.authorid57220806580en_US
dc.contributor.authorid57220803886en_US
dc.date.accessioned2023-05-29T08:08:16Z
dc.date.available2023-05-29T08:08:16Z
dc.date.issued2020
dc.descriptionBarium compounds; Forecasting; Nearest neighbor search; Office buildings; Sodium compounds; Support vector machines; Thermal comfort; Accurate prediction; Classification trees; Commercial building; Environmental conditions; K-nearest neighbors; Machine learning models; Prediction process; Thermal factors; HVACen_US
dc.description.abstractPredicting thermal comfort requires a set of reliable thermal factors for an accurate prediction. The effectiveness of using thermal factors varies depending on the environmental conditions and occupants' characteristics. Identifying thermal comfort in a commercial building is important for better management of the building's facilities. The objective of this research is to compare the performance of the six established thermal factors with actual users' responses in predicting thermal comfort, focusing on buildings operating with HVAC system. This research applies six machine-learning models for prediction process; and, one general method widely use to generate thermal comfort known as the PMV method. The experimental results prove that subspace K-Nearest Neighbor (s-KNN) can reach up to 80.41% of accuracy, and then followed by Begged Trees (BT) model (76.30%), Classification Tree (CT) (66%), Classification Neural Network (CNN) (55.67%), Support Vector Machine (SVM) (50.51%) and Kernel Na�ve Bayes (KNB) (43.30%). Whilst, PMV method achieves the lowest result, with 22.68% accuracy only. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243466
dc.identifier.doi10.1109/ICIMU49871.2020.9243466
dc.identifier.epage176
dc.identifier.scopus2-s2.0-85097644407
dc.identifier.spage170
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097644407&doi=10.1109%2fICIMU49871.2020.9243466&partnerID=40&md5=875e33d53f9506949ca6f0fa1ae52e92
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25335
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titlePredicting Thermal Comfort of HVAC Building Using 6 Thermal Factorsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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