Bibliographic Exploration of Application of Machine Learning and Artificial Intelligence in Solar Energy

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dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorBashir, Faizah Mohammed
dc.contributor.authorDodo, Yakubu Aminu
dc.contributor.authorOyebode, Oluwadare Joshua
dc.contributor.authorCulpable, Rex V.
dc.contributor.authorDe La Calzada, Limic M.
dc.contributor.authorAdediran, Anthonia Oluwatosin
dc.date.accessioned2024-10-12T19:47:12Z
dc.date.available2024-10-12T19:47:12Z
dc.date.issued2024
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024 -- 2 April 2024 through 4 April 2024 -- Omu-Aran -- 201895en_US
dc.description.abstractSolar energy could mitigate global warming and climate change. Solar energy faces economic, environmental, and technical challenges. Machine learning solves these technical issues. Despite several studies, machine learning in photovoltaics and solar energy is understudied. This study examines publishing patterns and bibliometrics to critically evaluate machine learning applications in photovoltaics and solar energy research. Scopus uses PRISMA. International publishing, citations, and collaboration are high. The Chinese Ministry of Education employs famous scholars like G. E. Georghiou and Haibo Ma. China is most active due to funding schemes like the National Natural Science Foundation and the National Key Research and Development Programme. This study examines publication patterns by country, institution, and funding organisation from 2014 to 2022, spanning topic categories and indicators. Examining author-keyword data to group publishing themes and identify influential journals. Increasing understanding of machine learning applications in photovoltaics and solar energy research. This project will examine the potential for significant development and the hurdles that must be overcome to leverage Cognitive Computing's benefits in cancer and tumour research. In response to the rising amount of malware, phishing, and intrusion attacks on global energy and grid infrastructure, photovoltaic and solar energy system cybersecurity may be studied. © 2024 IEEE.en_US
dc.identifier.doi10.1109/SEB4SDG60871.2024.10629967
dc.identifier.isbn979-835035815-5
dc.identifier.scopus2-s2.0-85202983132en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/SEB4SDG60871.2024.10629967
dc.identifier.urihttps://hdl.handle.net/11467/8794
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofInternational Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzScopus_20241012en_US
dc.subjectartificial intelligenceen_US
dc.subjectmachine learningen_US
dc.subjectscientometric analysisen_US
dc.subjectsolar energyen_US
dc.titleBibliographic Exploration of Application of Machine Learning and Artificial Intelligence in Solar Energyen_US
dc.typeConference Objecten_US

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