Application of Machine Learning in Energy Storage: A Scientometric Research of a Decade

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorBashir, Faizah Mohammed
dc.contributor.authorDodo, Yakubu Aminu
dc.contributor.authorDayupay, Johnry P.
dc.contributor.authorDe La Calzada, Limic M.
dc.contributor.authorAdediran, Anthonia Oluwatosin
dc.date.accessioned2024-01-31T07:45:07Z
dc.date.available2024-01-31T07:45:07Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe publication trends and bibliometric analysis of the research landscape on the applications of machine/deep learning in energy storage (MES) research were examined in this study based on published documents in the Elsevier Scopus database between 2012 and 2022. The PRISMA technique employed to identify, screen, and filter related publications on MES research recovered 969 documents comprising articles, conference papers, and reviews published in English. The results showed that the publications count on the topic increased from 3 to 385 (or a 12,733.3% increase) along with citations between 2012 and 2022. The high publications and citations rate was ascribed to the MDLES research impact, co-authorships/collaborations, as well as the source title/journals’ reputation, multidisciplinary nature, and research funding. The top/most prolific researcher, institution, country, and funding body on MDLES research are; is Yan Xu, Tsinghua University, China, and the National Natural Science Foundation of China, respectively. Keywords occurrence analysis revealed three clusters or hotspots based on machine learning, digital storage, and Energy Storage. Further analysis of the research landscape showed that MDLES research is currently and largely focused on the application of machine/deep learning for predicting, operating, and optimising energy storage as well as the design of energy storage materials for renewable energy technologies such as wind, and PV solar. However, future research will presumably include a focus on advanced energy materials development, operational systems monitoring and control as well as techno-economic analysis to address challenges associated with energy efficiency analysis, costing of renewable energy electricity pricing, trading, and revenue prediction.en_US
dc.identifier.doi10.1007/978-3-031-48981-5_10en_US
dc.identifier.endpage135en_US
dc.identifier.scopus2-s2.0-85182515982en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage124en_US
dc.identifier.urihttps://hdl.handle.net/11467/7129
dc.identifier.urihttps://doi.org/10.1007/978-3-031-48981-5_10
dc.identifier.volume179en_US
dc.identifier.wosWOS:001264477500010en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofCommunications in Computer and Information Science / 29th International Conference on Information and Software Technologies, ICIST 2023Kaunas12 October 2023through 14 October 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectartificial intelligence; energy storage; machine learning; scientometric analysisen_US
dc.titleApplication of Machine Learning in Energy Storage: A Scientometric Research of a Decadeen_US
dc.typeConference Objecten_US

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