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Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)

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dc.citedby10
dc.contributor.authorHosseini E.en_US
dc.contributor.authorAl-Ghaili A.M.en_US
dc.contributor.authorKadir D.H.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorDeveci M.en_US
dc.contributor.authorRazali R.A.en_US
dc.contributor.authorid57212521533en_US
dc.contributor.authorid26664381500en_US
dc.contributor.authorid57211243421en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid55734383000en_US
dc.contributor.authorid35146685400en_US
dc.date.accessioned2025-03-03T07:43:13Z
dc.date.available2025-03-03T07:43:13Z
dc.date.issued2024
dc.description.abstractThe synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear nature of energy data. Conversely, meta-heuristic algorithms offer optimization capabilities but suffer from computational burdens, especially with high-dimensional data. This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. We analyze state-of-the-art techniques, innovations, and recent advancements, identifying open research challenges. Additionally, we propose a novel framework that seamlessly merges meta-heuristic algorithms into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The contributions of the paper include: 1. Overview of recent advancements in MHs, DL, and integration. 2. Coverage of trends from 2018 to 2023. 3. Introduction of Alpha metric for performance evaluation. 4. Innovative framework harmonizing MHs with DL for energy problems. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101409
dc.identifier.doi10.1016/j.esr.2024.101409
dc.identifier.scopus2-s2.0-85193900930
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85193900930&doi=10.1016%2fj.esr.2024.101409&partnerID=40&md5=00ee3b19e8f7eb43dcdfe6e9105a2011
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36584
dc.identifier.volume53
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleEnergy Strategy Reviews
dc.subjectClustering algorithms
dc.subjectHeuristic algorithms
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectOptimization
dc.subjectDeep learning
dc.subjectEnergy
dc.subjectEnergy applications
dc.subjectExcel
dc.subjectMeta-heuristics algorithms
dc.subjectMetaheuristic
dc.subjectModeling and forecasting
dc.subjectOptimality
dc.subjectRenewable energies
dc.subjectResearch challenges
dc.subjectDeep learning
dc.titleMeta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)en_US
dc.typeReviewen_US
dspace.entity.typePublication
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