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From Trends to Insights: A Text Mining Analysis of Solar Energy Forecasting (2017–2023)

Author

Listed:
  • Mohammed Asloune

    (SPE Laboratory, UMR CNRS 6134, University of Corsica Pasquale Paoli, Route des Sanguinaires, 20000 Ajaccio, France
    Mines Paris, PSL University, Centre for Observation, Impacts, Energy (O.I.E.), Sophia-Antipolis, 06904 Antibes, France)

  • Gilles Notton

    (SPE Laboratory, UMR CNRS 6134, University of Corsica Pasquale Paoli, Route des Sanguinaires, 20000 Ajaccio, France)

  • Cyril Voyant

    (Mines Paris, PSL University, Centre for Observation, Impacts, Energy (O.I.E.), Sophia-Antipolis, 06904 Antibes, France)

Abstract

This study aims to highlight key figures and organizations in solar energy forecasting research, including the most prominent authors, journals, and countries. It also clarifies commonly used abbreviations in the field, with a focus on forecasting methods and techniques, the form and type of solar energy forecasting outputs, and the associated error metrics. Building on previous research that analyzed data up to 2017, the study updates findings to include information through 2023, incorporating metadata from 500 articles to identify key figures and organizations, along with 276 full-text articles analyzed for abbreviations. The application of text mining offers a concise yet comprehensive overview of the latest trends and insights in solar energy forecasting. The key findings of this study are threefold: First, China, followed by the United States of America and India, is the leading country in solar energy forecasting research, with shifts observed compared to the pre-2017 period. Second, numerous new abbreviations related to machine learning, particularly deep learning, have emerged in solar energy forecasting since before 2017, with Long Short-Term Memory, Convolutional Neural Networks, and Recurrent Neural Networks the most prominent. Finally, deterministic error metrics are mentioned nearly 11 times more frequently than probabilistic ones. Furthermore, perspectives on the practices and approaches of solar energy forecasting companies are also examined.

Suggested Citation

  • Mohammed Asloune & Gilles Notton & Cyril Voyant, 2025. "From Trends to Insights: A Text Mining Analysis of Solar Energy Forecasting (2017–2023)," Energies, MDPI, vol. 18(19), pages 1-30, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:19:p:5231-:d:1763259
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    References listed on IDEAS

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