Application of Machine Learning in Renewable Energy: A Bibliometric Analysis of a Decade

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorFlores, Denis Dante Corilla
dc.contributor.authorAyaz, Muhammad
dc.contributor.authorDodo, Yakubu Aminu
dc.contributor.authorAreche, Franklin Ore
dc.contributor.authorAdediran, Anthonia Oluwatosin
dc.contributor.authorOyebode, Oluwadare Joshua
dc.contributor.authorDayupay, Johnry P.
dc.date.accessioned2023-11-10T13:51:00Z
dc.date.available2023-11-10T13:51:00Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractMachine learning studies in the field of renewable energy are analysed here (REML). So, from 2012 to 2021, we looked at the publication tendencies (PT) and bibliometric analysis (BA) of REML research that was indexed by Elsevier Scopus. Key insights into the research landscape, scientific discoveries, and technological advancement were revealed by BA, while PT highlighted REML's important players, top cited papers, and financing organisations. In total, the PT discovered 1,218 works, 397 of which were conference papers and 106 were reviews. Because it spans the disciplines of science, technology, engineering, and mathematics, REML research is exhaustive, varied, and consequential. The most productive researchers, countries, and sponsors include Ravinesh C. Deo, the United States' National Renewable Energy Laboratory, and China's National Natural Science Foundation. Journal prestige and open access are valued by contributors, as seen by the success of Applied Energy and Energies. Productivity among REML's key stakeholders is boosted by collaborations and research funding. Keyword co-occurrence analysis was used to categorise REML research into four broad topic areas: systems, technologies, tools/technologies, and socio-technical dynamics. According to the results, ML plays a crucial role in the prediction, operation, and optimisation of RET as well as the design and development of RE-related materials.en_US
dc.identifier.doi10.1109/I2CACIS57635.2023.10193231en_US
dc.identifier.endpage179en_US
dc.identifier.scopus2-s2.0-85168379731en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage173en_US
dc.identifier.urihttps://hdl.handle.net/11467/6996
dc.identifier.urihttps://doi.org/10.1109/I2CACIS57635.2023.10193231
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2023 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectRenewable energy, Machine learning application, Bibliometric analysis, Publication trends, technologiesen_US
dc.titleApplication of Machine Learning in Renewable Energy: A Bibliometric Analysis of a Decadeen_US
dc.typeConference Objecten_US

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