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Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector

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dc.citedby5
dc.contributor.authorWang G.en_US
dc.contributor.authorMukhtar A.en_US
dc.contributor.authorMoayedi H.en_US
dc.contributor.authorKhalilpoor N.en_US
dc.contributor.authorTt Q.en_US
dc.contributor.authorid57770281700en_US
dc.contributor.authorid57195426549en_US
dc.contributor.authorid55923628500en_US
dc.contributor.authorid56397128000en_US
dc.contributor.authorid58913717500en_US
dc.date.accessioned2025-03-03T07:42:56Z
dc.date.available2025-03-03T07:42:56Z
dc.date.issued2024
dc.description.abstractResidential uses a significant amount of energy; hence, encouraging sustainability and lessening environmental effects requires minimizing energy consumption in this sector. This study focuses on applying and evaluating evolutionary algorithms combined with conventional neural networks to predict building energy consumption in the residential sector. The primary objectives were to assess the performance of three evolutionary algorithms ? Heap-Based Optimizer (HBO), Multiverse Optimizer (MVO), and Whale Optimization Algorithm (WOA) ? in comparison to each other and to determine their effectiveness in predicting energy consumption. Each algorithm was integrated into the neural network framework to optimize the prediction model. Training and testing datasets were employed to evaluate the performance of the models. Two key statistical indices, Root Mean Square Error (RMSE) and R-squared (R2), were utilized to assess the accuracy of the predictions. The results of the evaluation demonstrated varying performances among the three evolutionary algorithms. MVO achieved the highest scores for both RMSE (48.55082 in training and 68.44517 in testing) and R2 (0.99184 in training and 0.98236 in testing) on both training and testing datasets, indicating superior predictive accuracy compared to HBO and WOA. These findings underscore the importance of algorithm selection in optimizing predictive models for energy consumption forecasting. Further research may explore hybrid approaches or parameter tuning to enhance the performance of evolutionary algorithms in this domain. Overall, this study contributes to advancing energy forecasting techniques, with potential implications for energy management and conservation efforts in the residential sector. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo131312
dc.identifier.doi10.1016/j.energy.2024.131312
dc.identifier.scopus2-s2.0-85190819582
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85190819582&doi=10.1016%2fj.energy.2024.131312&partnerID=40&md5=b65a2379b0f52ade63264eb8c2e2aff1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36535
dc.identifier.volume298
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy
dc.subjectBiomimetics
dc.subjectEnergy utilization
dc.subjectForecasting
dc.subjectHousing
dc.subjectMean square error
dc.subjectSustainable development
dc.subjectBuilding energy consumption
dc.subjectEnergy
dc.subjectEnergy-consumption
dc.subjectNature inspired optimization
dc.subjectNeural-networks
dc.subjectOptimisations
dc.subjectOptimization algorithms
dc.subjectOptimizers
dc.subjectPerformance
dc.subjectResidential sectors
dc.subjectartificial neural network
dc.subjectenergy management
dc.subjectspatiotemporal analysis
dc.subjectsustainability
dc.subjecttraining
dc.subjectEvolutionary algorithms
dc.titleApplication and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sectoren_US
dc.typeArticleen_US
dspace.entity.typePublication
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