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Data-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestions

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dc.citedby12
dc.contributor.authorHossain Lipu M.S.en_US
dc.contributor.authorMiah M.S.en_US
dc.contributor.authorAnsari S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHasan K.en_US
dc.contributor.authorSarker M.R.en_US
dc.contributor.authorMahmud M.S.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid57226266149en_US
dc.contributor.authorid57218906707en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57205215021en_US
dc.contributor.authorid57537703000en_US
dc.contributor.authorid57220492528en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid6701749037en_US
dc.date.accessioned2023-05-29T09:05:15Z
dc.date.available2023-05-29T09:05:15Z
dc.date.issued2021
dc.descriptionDecarbonization; Electric power transmission networks; Forecasting; Fossil fuels; Global warming; Solar energy; Data driven; Data-driven algorithm; Data-driven approach; Data-driven methods; Decarbonisation; Hybrid approach; Hybrid datum; Optimisations; Power predictions; Renewable Power; Wind poweren_US
dc.description.abstractGlobal warming and climate change are serious problems that need urgent action and replacement. Renewable power could be the promising alternative solution to fossil fuel-based electricity generation in minimizing carbon intensity and achieving the global decarbonization target by 2050. However, intermittent characteristics of renewables such as solar and wind have resulted in negative effects on the operation, reliability, and stability of the power grid. To address these concerns, the hybridization of data-driven algorithms has achieved substantial contributions in renewable power prediction with regard to efficiency, precision and robustness. The main contribution of this study is to provide a detailed explanation of the recent progress of hybrid data-driven algorithms for renewable power prediction including solar, wind, ocean, hydro, and geothermal highlighting their variables, forecasting horizons, performance indexes, contributions and limitations. Besides, the impact of grid decarbonization in connection with renewable power is analyzed rigorously. Furthermore, this review explores the key issues and challenges of hybrid data-driven approaches in renewable power prediction to identify existing research gaps and limitations. Finally, this paper delivers selective suggestions that will support academic researchers and power engineers to develop advanced hybrid data-driven approaches for future renewable power prediction toward achieving the decarbonization goal. � 2021 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo129476
dc.identifier.doi10.1016/j.jclepro.2021.129476
dc.identifier.scopus2-s2.0-85119970556
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85119970556&doi=10.1016%2fj.jclepro.2021.129476&partnerID=40&md5=772c82c212716ec20a8f95b117f42e3b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25846
dc.identifier.volume328
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Cleaner Production
dc.titleData-driven hybrid approaches for renewable power prediction toward grid decarbonization: Applications, issues and suggestionsen_US
dc.typeReviewen_US
dspace.entity.typePublication
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