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

Solar 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 publis...

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Bibliographic Details
Published in2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) pp. 1 - 5
Main Authors Ajibade, Samuel-Soma M., Bashir, Faizah Mohammed, Dodo, Yakubu Aminu, Oyebode, Oluwadare Joshua, Culpable, Rex V., De La Calzada, Limic M., Adediran, Anthonia Oluwatosin, Ajibade, Temiloluwa Iyanuoluwa
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.04.2024
Subjects
artificial intelligence
Machine learning
Malware
Phishing
Photovoltaic systems
scientometric analysis
Solar energy
Sustainable development
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Summary:Solar 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.
DOI:10.1109/SEB4SDG60871.2024.10629967