Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012-2021)

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dc.authoridBekun, Festus Victor/0000-0003-4948-6905
dc.authoridAjibade, Samuel-Soma M./0000-0002-3452-1889
dc.authoridAdedoyin, Festus/0000-0002-3586-2570
dc.authoridGyamfi, Bright Akwasi/0000-0002-7567-9885
dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorBekun, Festus Victor
dc.contributor.authorAdedoyin, Festus Fatai
dc.contributor.authorGyamfi, Bright Akwasi
dc.contributor.authorAdediran, Anthonia Oluwatosin
dc.date.accessioned2024-09-11T19:53:09Z
dc.date.available2024-09-11T19:53:09Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractThis study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author's choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.en_US
dc.identifier.doi10.3390/cleantechnol5020026
dc.identifier.endpage517en_US
dc.identifier.issn2571-8797
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85163772967en_US
dc.identifier.startpage497en_US
dc.identifier.urihttps://doi.org/10.3390/cleantechnol5020026
dc.identifier.urihttps://hdl.handle.net/11363/8079
dc.identifier.volume5en_US
dc.identifier.wosWOS:001014157600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofClean Technologiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectmachine learningen_US
dc.subjectalgorithmsen_US
dc.subjectsupervised learningen_US
dc.subjectunsupervised learningen_US
dc.subjectdeep learningen_US
dc.subjectrenewable energyen_US
dc.subjectforecastingen_US
dc.subjectoptimizationen_US
dc.titleMachine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012-2021)en_US
dc.typeReview Articleen_US

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