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Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

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dc.citedby16
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorMiah M.S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorSarker M.R.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorSaad M.H.M.en_US
dc.contributor.authorMahmud M.S.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid57226266149en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid57537703000en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid57220492528en_US
dc.date.accessioned2023-05-29T09:11:26Z
dc.date.available2023-05-29T09:11:26Z
dc.date.issued2021
dc.descriptionArtificial intelligence; Electric power generation; Stochastic systems; Weather forecasting; Wind power; Activation functions; Generalization performance; Influential factors; Learning capabilities; Performance analysis; Stochastic characteristic; Training algorithms; Wind power forecasting; Electric power transmission networksen_US
dc.description.abstractGlobally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9483904
dc.identifier.doi10.1109/ACCESS.2021.3097102
dc.identifier.epage102489
dc.identifier.scopus2-s2.0-85110853330
dc.identifier.spage102460
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85110853330&doi=10.1109%2fACCESS.2021.3097102&partnerID=40&md5=b08744e41dfa01fa4c059d215402ca28
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26518
dc.identifier.volume9
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleArtificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospectsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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