A bibliometric analysis of machine learning techniques in photovoltaic cells and solar energy (2014-2022)

Solar energy presents a promising solution to replace fossil-based energy sources, mitigating global warming and climate change. However, solar energy faces socio-economic, environmental, and technical challenges. Computational tools like machine learning offer solutions to these technical challenges. Despite numerous studies, there's a lack of comprehensive research on ML applications in Photovoltaics and Solar Energy. This study conducts a critical analysis of ML applications in Photovoltaics and Solar Energy research using publication trends and bibliometric analysis, employing the PRISMA approach on Scopus database. Results reveal a high publication output, citations, and international collaboration. Notable researchers include G. E. Georghiou and Haibo Ma, with the Ministry of Education (China) being a prolific affiliation. China emerges as the most active nation due to funding programs like the National Natural Science Foundation and the National Key Research and Development Program. This research contributes in terms of providing an analysis of publication patterns from 2014 to 2022, including topic categories and important metrics, at the levels of country, institution, and funding organisation. Analysing author-keyword data to aggregate publishing themes and identify the most influential journals. Enhancing comprehension of hotspots and focal points in machine learning applications in Photovoltaics and Solar Energy research. This research also aims to discuss the role of Cognitive Computing in cancer/ tumor and oncological research, emphasising the potential for significant advancements and the obstacles that need to be overcome in order to fully utilise its advantages. Future studies on the topic could include extensive research into the cybersecurity of Photovoltaics and solar energy systems particularly in the wake of numerous malware, phishing, and other intrusion attacks on the energy and grid infrastructure worldwide.