Environmental pollution has become a significant concern of nations. International organizations, local authorities, and social activists try to achieve sustainable development goals (SDGs) to protect the environment. However, this cannot be achieved without acknowledging the role of advanced technology applications. Previous studies found a significant relationship between technology and energy resources. But the need to highlight the significance of artificial intelligence (AI) in dealing with inevitable environmental issues still requires more attention. This study aims to analyze the application of AI applications in predicting, developing, and implementing wind and solar energy resources through a bibliometric analysis from 1991 to 2022. It uses bilioshiny of the "bibliometrix 3.0" package of R-programming for influential core aspects and keyword analysis and VOSviewer for co-occurrence analysis. The study provides significant implications for core authors, documents, sources, affiliations, and countries. It also provides keyword analysis and a co-occurrence network to cope with the conceptual integration of the literature. It reports three significant streams of literature in clusters: AI optimization and renewable energy resources; smart renewable energy resource challenges and opportunities; deep learning and machine learning forecasting; and energy efficiency. The findings will uncover the strategic perspective of AI technology for wind and solar energy generation projects.
Keywords: AI capabilities; Artificial intelligence; Bibliometric analysis; Deep learning; Environmental pollution; Machine learning; Solar energy; Wind energy.
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.