The smart green tide: A bibliometric analysis of AI and renewable energy transition

With the growing demand for renewable energy worldwide, the application of artificial intelligence (AI) has become a driving force in promoting sustainable development. This study conducts a bibliometric analysis of 1054 articles published between 2000 and 2024 from the Web of Science Core Collection Database, applying CiteSpace, VOS Viewer, and Bibliometrix tools to construct a knowledge map of AI’s application in renewable energy transition. The results reveal an exponential growth in related academic literature, with the number of publications increasing by 650 % from 2019 to 2024 alone, indicating the intensifying interest in this field. Chinese scholars have emerged as leaders in this research area, contributing 935 of the total 1054 publications, and forming extensive international collaborations. Keyword co-occurrence analysis shows that AI technologies, such as machine learning, smart grids, and blockchain, are becoming crucial in enhancing renewable energy systems. Specifically, “renewable energy” and “artificial intelligence” appear in over 40 % of the literature, with high-frequency terms including “sustainability,” “energy storage,” and “forecasting.” Notably, AI has been applied to improve solar and wind energy systems, with citation analysis revealing that works on AI-based solar forecasting methods and energy optimization have received more than 500 citations. This paper not only assesses the current application of AI in renewable energy but also anticipates future trends, identifying emerging topics like AI-driven blockchain integration and smart grids as key areas for future exploration.