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Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends

Author

Listed:
  • Ewa Chodakowska

    (Faculty of Engineering Management, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland)

  • Joanicjusz Nazarko

    (Faculty of Engineering Management, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland)

  • Łukasz Nazarko

    (Faculty of Engineering Management, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland)

  • Hesham S. Rabayah

    (Department of Civil and Infrastructure Engineering, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan)

Abstract

Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. The need to optimise energy systems, ensure power continuity, and balance energy supply and demand is driving the continuous development of forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasises the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. The article’s relevance consists of providing up-to-date knowledge on solar forecasting, along with insights on emerging trends, future research directions, and anticipating implications for theory and practice.

Suggested Citation

  • Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko & Hesham S. Rabayah, 2024. "Solar Radiation Forecasting: A Systematic Meta-Review of Current Methods and Emerging Trends," Energies, MDPI, vol. 17(13), pages 1-27, June.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3156-:d:1422744
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    References listed on IDEAS

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    1. Zwane, N. & Tazvinga, H. & Botai, C. & Murambadoro, M. & Botai, J. & de Wit, J. & Mabasa, B. & Daniel, S. & Mabhaudhi, Tafadzwanashe, 2022. "A bibliometric analysis of solar energy forecasting studies in Africa," Papers published in Journals (Open Access), International Water Management Institute, pages 1-15(15):55.
    2. André, Maïna & Dabo-Niang, Sophie & Soubdhan, Ted & Ould-Baba, Hanany, 2016. "Predictive spatio-temporal model for spatially sparse global solar radiation data," Energy, Elsevier, vol. 111(C), pages 599-608.
    3. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    4. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
    5. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko & Hesham S. Rabayah & Raed M. Abendeh & Rami Alawneh, 2023. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations," Energies, MDPI, vol. 16(13), pages 1-24, June.
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