This study aims to present a bibliometric analysis of research on artificial intelligence (AI) applications in renewable energy (RE) technologies and the clean energy transition, highlighting both established and emerging research themes.
Data were extracted from Scopus and Web of Science using clearly documented search queries, with search dates and exported fields (CSV/BibTeX) reported to ensure full reproducibility. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework guided publication selection. Bibliometric analyses were conducted using VOSviewer and the R Bibliometrix package to identify key trends, leading authors, influential institutions and research clusters.
This study maps the evolving research landscape, highlighting emerging themes, highly cited sources and potential avenues for collaboration and technological development in AI-enabled RE. Recent developments, such as the application of ChatGPT and large language models, are emerging topics, with evidence based on the number of publications mentioning these terms in abstracts or keywords since 2022, though their long-term impact remains preliminary.
The findings offer actionable insights for policymakers, researchers and industry practitioners. They can guide evidence-based policy decisions, strategic research funding, international collaborations and innovation in AI-driven energy technologies while helping prioritize areas with the greatest potential to accelerate the clean energy transition.
This work addresses a critical gap by quantifying and visualizing the AI–RE research landscape, providing a foundation for targeted future studies and evidence-based energy strategies while acknowledging emerging AI technologies that may shape the field in the coming years.
