New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorZaidi, Abdelhamid
dc.contributor.authorAl Luhayb, Asamh Saleh M.
dc.contributor.authorAdediran, Anthonia Oluwatosin
dc.contributor.authorVoumik, Liton Chandra
dc.contributor.authorRabbi, Fazle
dc.date.accessioned2023-11-13T11:30:58Z
dc.date.available2023-11-13T11:30:58Z
dc.date.issued2023en_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 and deep learning in energy storage (MDLES) 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 MDLES 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.doi10.32479/ijeep.14832en_US
dc.identifier.endpage314en_US
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85172359728en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage303en_US
dc.identifier.urihttps://hdl.handle.net/11467/7021
dc.identifier.urihttps://doi.org/10.32479/ijeep.14832
dc.identifier.volume13en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEconjournalsen_US
dc.relation.ispartofInternational Journal of Energy Economics and Policyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine Learning, Deep Learning, Artificial Intelligence, Energy Storage, Renewable Energy Technologies, Bibliometric Analysisen_US
dc.titleNew Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trendsen_US
dc.typeArticleen_US

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