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

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dc.authoridMD YASIR, AHMAD SHAH HIZAM/0000-0003-0253-0796
dc.authoridBen Khedher, Nidhal/0000-0003-1274-6443
dc.authoridMukhtar, Azfarizal/0000-0002-7792-0767
dc.contributor.authorBen Khedher, Nidhal
dc.contributor.authorMukhtar, Azfarizal
dc.contributor.authorYasir, Ahmad Shah Hizam Md
dc.contributor.authorKhalilpoor, Nima
dc.contributor.authorFoong, Loke Kok
dc.contributor.authorLe, Binh Nguyen
dc.contributor.authorYildizhan, Hasan
dc.date.accessioned2025-01-06T17:38:18Z
dc.date.available2025-01-06T17:38:18Z
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.
dc.identifier.doi10.1080/19942060.2023.2226725
dc.identifier.issn1994-2060
dc.identifier.issn1997-003X
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85163833469
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1080/19942060.2023.2226725
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2520
dc.identifier.volume17
dc.identifier.wosWOS:001017972900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofEngineering Applications of Computational Fluid Mechanics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectGreen buildings
dc.subjectheat loss
dc.subjectharmony search
dc.subjectparticle swarm optimisation
dc.subjectartificial neural network
dc.titleApproximating heat loss in smart buildings through large scale experimental and computational intelligence solutions
dc.typeArticle

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