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The article presents the results of a bibliometric analysis of scientific publications on the application of artificial intelligence (AI) in forecasting for renewable energy sources. The analysis covered 2659 publications from 2000–2024 available in the Web of Science database, and the data were processed using the CiteSpace software. The aim of the study was to identify key research trends, authors, institutions and countries that have the greatest impact on the development of this field. The results indicate a growing interest in AI in the energy sector, with an intensive increase in the number of publications after 2010. Key research topics include photovoltaic power forecasting, solar radiation intensity and short-term wind forecasting, and the dominant methods are hybrid models and machine learning algorithms. The analysis highlights countries such as China, the USA and Germany as research leaders. The conclusions emphasize the importance of AI in improving the precision of forecasts, which are crucial for the stability of energy systems in the context of global climate transformation.

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Bibliometric Analysis of Research on AI-Based Forecasting in Renewable Energy

  • 会议论文

  • 发表日期：

    2025年

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  • Paweł Kut,
  • Katarzyna Pietrucha-Urbanik,
  • Martina Zelenakova,
  • Hany F. Abd-Elhamid

摘要

The article presents the results of a bibliometric analysis of scientific publications on the application of artificial intelligence (AI) in forecasting for renewable energy sources. The analysis covered 2659 publications from 2000–2024 available in the Web of Science database, and the data were processed using the CiteSpace software. The aim of the study was to identify key research trends, authors, institutions and countries that have the greatest impact on the development of this field. The results indicate a growing interest in AI in the energy sector, with an intensive increase in the number of publications after 2010. Key research topics include photovoltaic power forecasting, solar radiation intensity and short-term wind forecasting, and the dominant methods are hybrid models and machine learning algorithms. The analysis highlights countries such as China, the USA and Germany as research leaders. The conclusions emphasize the importance of AI in improving the precision of forecasts, which are crucial for the stability of energy systems in the context of global climate transformation.