Appraisal of energy loss reduction in green buildings using large-scale experiments compiled with swarm intelligent solutions

dc.authoridMoayedi, Hossein/0000-0002-5625-1437
dc.contributor.authorMoayedi, Hossein
dc.contributor.authorYildizhan, Hasan
dc.contributor.authorAl-Bahrani, Mohammed
dc.contributor.authorLe Van, Bao
dc.date.accessioned2025-01-06T17:36:46Z
dc.date.available2025-01-06T17:36:46Z
dc.date.issued2023
dc.description.abstractToday, the issue of energy efficiency is a major one in global politics. The external environment, particularly the wind speed and outside air temperature, determines the thermal burden the cold outside air places on a building's interior. The heat load of a building is influenced by several factors, including the wall's heat transfer coefficients (W/mK), the coating material (W/mK), the inside temperature (degrees C), and the outside temperature (degrees C), and the temperature of external surface (degrees C). In this investigation, we undertake a comprehensive assessment, evaluation, and comparing the performance of two unique artificial approaches (BSA and COA) utilized for anticipating heat loss in green buildings; the optimum way is then identified depending on the R-2 and RMSE criteria. The outcomes demonstrate that BSA and COA have R-2 values of (0.97038 and 0.90158) and (0.9919 and 0.94239) in the training and testing phases. Additionally, the RMSE values for BSA and COA in the training and testing stages are (0.02541 and 0.08616) and (0.01336 and 0.06662), correspondingly. Also, the estimated MAEs (0.019055 and 0.0097193) denote a low level of training error for both methods. Regarding R-2, RMSE and MAE values, the COA predicts energy loss more accurately.
dc.identifier.doi10.1016/j.seta.2023.103215
dc.identifier.issn2213-1388
dc.identifier.issn2213-1396
dc.identifier.scopus2-s2.0-85151632601
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.seta.2023.103215
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1995
dc.identifier.volume57
dc.identifier.wosWOS:001055348300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofSustainable Energy Technologies and Assessments
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectBuilding
dc.subjectResidential
dc.subjectSwarm intelligent
dc.subjectEnergy effeciency
dc.subjectEnergy loss
dc.titleAppraisal of energy loss reduction in green buildings using large-scale experiments compiled with swarm intelligent solutions
dc.typeArticle

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