Producción Científica

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.

Today, assumptions about probable future developments (at least as far as they make use of quantifiable scientific methods and are not pure speculation) are generally based on data from the past. An interesting way to analyze the future through this type of data is text mining or individual methods out of the spectrum of text mining, such as topic modeling. Topic Modeling itself is a combination of quantitative and qualitative methodology and is based on the full spectrum of social science methodology. Therefore, the method is an interesting way for futures research to analyze futures. This publication addresses the question of how a combination of different methods can contribute to trend monitoring or trend mining. For this purpose, a set of scientific publications was first generated with the help of a search query in the Web of Science (WoS), which is the basis for all evaluations and statements and topics. In essence, the method considered here should be more fully integrated into the scientific practice of futures research because it can make a valuable contribution to estimating future development based on past development.