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

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dc.citedby4
dc.contributor.authorBen Khedher N.en_US
dc.contributor.authorMukhtar A.en_US
dc.contributor.authorMd Yasir A.S.H.en_US
dc.contributor.authorKhalilpoor N.en_US
dc.contributor.authorFoong L.K.en_US
dc.contributor.authorNguyen Le B.en_US
dc.contributor.authorYildizhan H.en_US
dc.contributor.authorid35102548000en_US
dc.contributor.authorid57195426549en_US
dc.contributor.authorid58518504200en_US
dc.contributor.authorid56397128000en_US
dc.contributor.authorid57210822623en_US
dc.contributor.authorid57972795900en_US
dc.contributor.authorid57195605597en_US
dc.date.accessioned2024-10-14T03:20:38Z
dc.date.available2024-10-14T03:20:38Z
dc.date.issued2023
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2226725
dc.identifier.doi10.1080/19942060.2023.2226725
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85163833469
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85163833469&doi=10.1080%2f19942060.2023.2226725&partnerID=40&md5=d356e67319b93376383ae169e2365164
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34558
dc.identifier.volume17
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.subjectartificial neural network
dc.subjectGreen buildings
dc.subjectharmony search
dc.subjectheat loss
dc.subjectparticle swarm optimisation
dc.titleApproximating heat loss in smart buildings through large scale experimental and computational intelligence solutionsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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