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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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id my.uniten.dspace-25335
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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
score 13.222552

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