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Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

Author

Listed:
  • Sameer Al-Dahidi

    (Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan)

  • Manoharan Madhiarasan

    (Department of Electronics and Computers, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, B-dul Eroilor 29, 500036 Brasov, Romania)

  • Loiy Al-Ghussain

    (Argonne National Laboratory, Energy Systems and Infrastructure Analysis Division, Lemont, IL 60439, USA)

  • Ahmad M. Abubaker

    (Institute of Research for Technology Development (IR4TD), University of Kentucky, Lexington, KY 40506, USA)

  • Adnan Darwish Ahmad

    (Institute of Research for Technology Development (IR4TD), University of Kentucky, Lexington, KY 40506, USA)

  • Mohammad Alrbai

    (Department of Mechanical Engineering, School of Engineering, University of Jordan, Amman 11942, Jordan)

  • Mohammadreza Aghaei

    (Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
    Department of Sustainable Systems Engineering (INATECH), University of Freiburg, 79110 Freiburg, Germany)

  • Hussein Alahmer

    (Department of Automated Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt 19117, Jordan)

  • Ali Alahmer

    (Department of Mechanical Engineering, Tuskegee University, Tuskegee, AL 36088, USA)

  • Piero Baraldi

    (Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy)

  • Enrico Zio

    (Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
    Mines Paris, Centre de Recherche sur les Risques et les Crises, Paris Sciences et Lettres University, 75006 Valbonne, France)

Abstract

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

Suggested Citation

  • Sameer Al-Dahidi & Manoharan Madhiarasan & Loiy Al-Ghussain & Ahmad M. Abubaker & Adnan Darwish Ahmad & Mohammad Alrbai & Mohammadreza Aghaei & Hussein Alahmer & Ali Alahmer & Piero Baraldi & Enrico Z, 2024. "Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework," Energies, MDPI, vol. 17(16), pages 1-38, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4145-:d:1460190
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    References listed on IDEAS

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    1. Liu, Xiangjie & Liu, Yuanyan & Kong, Xiaobing & Ma, Lele & Besheer, Ahmad H. & Lee, Kwang Y., 2023. "Deep neural network for forecasting of photovoltaic power based on wavelet packet decomposition with similar day analysis," Energy, Elsevier, vol. 271(C).
    2. Hosenuzzaman, M. & Rahim, N.A. & Selvaraj, J. & Hasanuzzaman, M. & Malek, A.B.M.A. & Nahar, A., 2015. "Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 284-297.
    3. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
    4. Touati, Farid & Chowdhury, Noor Alam & Benhmed, Kamel & San Pedro Gonzales, Antonio J.R. & Al-Hitmi, Mohammed A. & Benammar, Mohieddine & Gastli, Adel & Ben-Brahim, Lazhar, 2017. "Long-term performance analysis and power prediction of PV technology in the State of Qatar," Renewable Energy, Elsevier, vol. 113(C), pages 952-965.
    5. 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).
    6. Gürtler, Marc & Paulsen, Thomas, 2018. "The effect of wind and solar power forecasts on day-ahead and intraday electricity prices in Germany," Energy Economics, Elsevier, vol. 75(C), pages 150-162.
    7. Taeseop Park & Keunju Song & Jaeik Jeong & Hongseok Kim, 2023. "Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants," Energies, MDPI, vol. 16(14), pages 1-20, July.
    8. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    9. Wang, Guochang & Su, Yan & Shu, Lianjie, 2016. "One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models," Renewable Energy, Elsevier, vol. 96(PA), pages 469-478.
    10. Heo, Jae & Song, Kwonsik & Han, SangUk & Lee, Dong-Eun, 2021. "Multi-channel convolutional neural network for integration of meteorological and geographical features in solar power forecasting," Applied Energy, Elsevier, vol. 295(C).
    11. Kosmopoulos, P.G. & Kazadzis, S. & Lagouvardos, K. & Kotroni, V. & Bais, A., 2015. "Solar energy prediction and verification using operational model forecasts and ground-based solar measurements," Energy, Elsevier, vol. 93(P2), pages 1918-1930.
    12. Elsinga, Boudewijn & van Sark, Wilfried G.J.H.M., 2017. "Short-term peer-to-peer solar forecasting in a network of photovoltaic systems," Applied Energy, Elsevier, vol. 206(C), pages 1464-1483.
    13. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    14. Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
    15. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    16. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    17. Feng, Cong & Zhang, Jie & Zhang, Wenqi & Hodge, Bri-Mathias, 2022. "Convolutional neural networks for intra-hour solar forecasting based on sky image sequences," Applied Energy, Elsevier, vol. 310(C).
    18. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    19. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
    20. Zhang, Peng & Li, Wenyuan & Li, Sherwin & Wang, Yang & Xiao, Weidong, 2013. "Reliability assessment of photovoltaic power systems: Review of current status and future perspectives," Applied Energy, Elsevier, vol. 104(C), pages 822-833.
    21. Mellit, A. & Pavan, A. Massi & Lughi, V., 2021. "Deep learning neural networks for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 172(C), pages 276-288.
    22. Jung, Jaehoon & Han, SangUk & Kim, Byungil, 2019. "Digital numerical map-oriented estimation of solar energy potential for site selection of photovoltaic solar panels on national highway slopes," Applied Energy, Elsevier, vol. 242(C), pages 57-68.
    23. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    24. Rizzi, Francesco & van Eck, Nees Jan & Frey, Marco, 2014. "The production of scientific knowledge on renewable energies: Worldwide trends, dynamics and challenges and implications for management," Renewable Energy, Elsevier, vol. 62(C), pages 657-671.
    25. Sameer Al-Dahidi & Francesco Di Maio & Piero Baraldi & Enrico Zio, 2017. "A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets," Post-Print hal-01652222, HAL.
    26. Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
    27. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    28. Aguiar, L. Mazorra & Pereira, B. & Lauret, P. & Díaz, F. & David, M., 2016. "Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting," Renewable Energy, Elsevier, vol. 97(C), pages 599-610.
    29. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    30. Amith Khandakar & Muhammad E. H. Chowdhury & Monzure- Khoda Kazi & Kamel Benhmed & Farid Touati & Mohammed Al-Hitmi & Antonio Jr S. P. Gonzales, 2019. "Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar," Energies, MDPI, vol. 12(14), pages 1-19, July.
    31. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
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    2. Younjeong Lee & Jongpil Jeong, 2025. "TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems," Energies, MDPI, vol. 18(4), pages 1-21, February.
    3. Paolo Di Leo & Alessandro Ciocia & Gabriele Malgaroli & Filippo Spertino, 2025. "Advancements and Challenges in Photovoltaic Power Forecasting: A Comprehensive Review," Energies, MDPI, vol. 18(8), pages 1-28, April.
    4. Balakrishnan, P., 2026. "Artificial intelligence in heliostat control and optimization for CSP plants: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 229(C).
    5. Bashir, Faizah Mohammed & Mahyuddin, Norhayati & Alsadun, Ibtihaj Saad Rashed & Andleeb, Zahra & Rassas, Nadia Hasen & Hamdoun, Haifa Youssef & Dodo, Yakubu Aminu, 2025. "Physics-informed Bayesian telemetry for dust-aware urban BIPV fleets: Calibrated forecasting, resource-constrained operations and maintenance, and event-triggered cleaning decisions," Energy, Elsevier, vol. 340(C).
    6. Masalha, Ismail & Alahmer, Ali & Alsabagh, Abdel Salam & Badran, Omar & Masuri, Siti Ujila, 2026. "Predictive analysis of porous media–cooled photovoltaic panels using gradient-boosting machine learning models," Renewable Energy, Elsevier, vol. 260(C).
    7. Zhichao Qiu & Ye Tian & Yanhong Luo & Taiyu Gu & Hengyu Liu, 2024. "Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning," Sustainability, MDPI, vol. 16(23), pages 1-24, December.

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