Staff View: Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions

Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions

The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many...

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Main Authors:	Ben Khedher N., Mukhtar A., Md Yasir A.S.H., Khalilpoor N., Foong L.K., Nguyen Le B., Yildizhan H.
Other Authors:	35102548000
Format:	Article
Published:	Taylor and Francis Ltd. 2024
Subjects:	artificial neural network Green buildings harmony search heat loss particle swarm optimisation
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id	my.uniten.dspace-34558
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spelling	my.uniten.dspace-345582024-10-14T11:20:39Z Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions Ben Khedher N. Mukhtar A. Md Yasir A.S.H. Khalilpoor N. Foong L.K. Nguyen Le B. Yildizhan H. 35102548000 57195426549 58518504200 56397128000 57210822623 57972795900 57195605597 artificial neural network Green buildings harmony search heat loss particle swarm optimisation The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings� heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R 2 and RMSE). Model performance of PSO-MLP is shown by R 2 amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R 2 amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance. � 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Final 2024-10-14T03:20:38Z 2024-10-14T03:20:38Z 2023 Article 10.1080/19942060.2023.2226725 2-s2.0-85163833469 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163833469&doi=10.1080%2f19942060.2023.2226725&partnerID=40&md5=d356e67319b93376383ae169e2365164 https://irepository.uniten.edu.my/handle/123456789/34558 17 1 2226725 All Open Access Gold Open Access Taylor and Francis Ltd. 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/
topic	artificial neural network Green buildings harmony search heat loss particle swarm optimisation
spellingShingle	artificial neural network Green buildings harmony search heat loss particle swarm optimisation Ben Khedher N. Mukhtar A. Md Yasir A.S.H. Khalilpoor N. Foong L.K. Nguyen Le B. Yildizhan H. Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
description	The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings� heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R 2 and RMSE). Model performance of PSO-MLP is shown by R 2 amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R 2 amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance. � 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
author2	35102548000
author_facet	35102548000 Ben Khedher N. Mukhtar A. Md Yasir A.S.H. Khalilpoor N. Foong L.K. Nguyen Le B. Yildizhan H.
format	Article
author	Ben Khedher N. Mukhtar A. Md Yasir A.S.H. Khalilpoor N. Foong L.K. Nguyen Le B. Yildizhan H.
author_sort	Ben Khedher N.
title	Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
title_short	Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
title_full	Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
title_fullStr	Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
title_full_unstemmed	Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
title_sort	approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
publisher	Taylor and Francis Ltd.
publishDate	2024
_version_	1814061061671747584
score	13.222552

Approximating heat loss in smart buildings through large scale experimental and computational intelligence solutions

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