Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors

Barium 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 fa...

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Main Authors:	Mohamed Salleh F.H., Saripuddin M.B., Bin Omar R.
Other Authors:	26423229000
Format:	Conference Paper
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
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Institution:	Universiti Tenaga Nasional

id	my.uniten.dspace-25335
record_format	dspace
spelling	my.uniten.dspace-253352023-05-29T16:08:16Z Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors Mohamed Salleh F.H. Saripuddin M.B. Bin Omar R. 26423229000 57220806580 57220803886 Barium 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; HVAC Predicting 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. Final 2023-05-29T08:08:16Z 2023-05-29T08:08:16Z 2020 Conference Paper 10.1109/ICIMU49871.2020.9243466 2-s2.0-85097644407 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097644407&doi=10.1109%2fICIMU49871.2020.9243466&partnerID=40&md5=875e33d53f9506949ca6f0fa1ae52e92 https://irepository.uniten.edu.my/handle/123456789/25335 9243466 170 176 Institute of Electrical and Electronics Engineers Inc. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Barium 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; HVAC
author2	26423229000
author_facet	26423229000 Mohamed Salleh F.H. Saripuddin M.B. Bin Omar R.
format	Conference Paper
author	Mohamed Salleh F.H. Saripuddin M.B. Bin Omar R.
spellingShingle	Mohamed Salleh F.H. Saripuddin M.B. Bin Omar R. Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors
author_sort	Mohamed Salleh F.H.
title	Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors
title_short	Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors
title_full	Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors
title_fullStr	Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors
title_full_unstemmed	Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors
title_sort	predicting thermal comfort of hvac building using 6 thermal factors
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2023
_version_	1806427740701720576

Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors

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