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Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms

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dc.citedby8
dc.contributor.authorMoayedi H.en_US
dc.contributor.authorMukhtar A.en_US
dc.contributor.authorBen Khedher N.en_US
dc.contributor.authorElbadawi I.en_US
dc.contributor.authorAmara M.B.en_US
dc.contributor.authorTT Q.en_US
dc.contributor.authorKhalilpoor N.en_US
dc.contributor.authorid55923628500en_US
dc.contributor.authorid57195426549en_US
dc.contributor.authorid35102548000en_US
dc.contributor.authorid56499091400en_US
dc.contributor.authorid57219371832en_US
dc.contributor.authorid58913717500en_US
dc.contributor.authorid56397128000en_US
dc.date.accessioned2025-03-03T07:48:08Z
dc.date.available2025-03-03T07:48:08Z
dc.date.issued2024
dc.description.abstractEnergy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set. ? 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2322509
dc.identifier.doi10.1080/19942060.2024.2322509
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85186453056
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85186453056&doi=10.1080%2f19942060.2024.2322509&partnerID=40&md5=705aab27f29e291de86157510dbbee5e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37162
dc.identifier.volume18
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleForecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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