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Bayesian rules and stochastic models for high accuracy prediction of solar radiation

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  • Voyant, Cyril
  • Darras, Christophe
  • Muselli, Marc
  • Paoli, Christophe
  • Nivet, Marie-Laure
  • Poggi, Philippe

Abstract

It is essential to find solar predictive methods to massively insert renewable energies on the electrical distribution grid. The goal of this study is to find the best methodology allowing predicting with high accuracy the hourly global radiation. The knowledge of this quantity is essential for the grid manager or the private PV producer in order to anticipate fluctuations related to clouds occurrences and to stabilize the injected PV power. In this paper, we test both methodologies: single and hybrid predictors. In the first class, we include the multi-layer perceptron (MLP), auto-regressive and moving average (ARMA), and persistence models. In the second class, we mix these predictors with Bayesian rules to obtain ad hoc models selections, and Bayesian averages of outputs related to single models. If MLP and ARMA are equivalent (nRMSE close to 40.5% for the both), this hybridization allows a nRMSE gain upper than 14% points compared to the persistence estimation (nRMSE=37% versus 51%).

Suggested Citation

  • Voyant, Cyril & Darras, Christophe & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure & Poggi, Philippe, 2014. "Bayesian rules and stochastic models for high accuracy prediction of solar radiation," Applied Energy, Elsevier, vol. 114(C), pages 218-226.
    • Handle: RePEc:eee:appene:v:114:y:2014:i:c:p:218-226
    DOI: 10.1016/j.apenergy.2013.09.051
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    7. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
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    9. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
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    11. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).
    12. Hassan, Muhammed A. & Al-Ghussain, Loiy & Khalil, Adel & Kaseb, Sayed A., 2022. "Self-calibrated hybrid weather forecasters for solar thermal and photovoltaic power plants," Renewable Energy, Elsevier, vol. 188(C), pages 1120-1140.
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    14. Akarslan, Emre & Hocaoglu, Fatih Onur, 2016. "A novel adaptive approach for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 87(P1), pages 628-633.
    15. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    16. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    17. Shireen, Tahasin & Shao, Chenhui & Wang, Hui & Li, Jingjing & Zhang, Xi & Li, Mingyang, 2018. "Iterative multi-task learning for time-series modeling of solar panel PV outputs," Applied Energy, Elsevier, vol. 212(C), pages 654-662.
    18. Hussain, Sajid & Al-Alili, Ali, 2016. "A new approach for model validation in solar radiation using wavelet, phase and frequency coherence analysis," Applied Energy, Elsevier, vol. 164(C), pages 639-649.
    19. Bouabdallah, A. & Olivier, J.C. & Bourguet, S. & Machmoum, M. & Schaeffer, E., 2015. "Safe sizing methodology applied to a standalone photovoltaic system," Renewable Energy, Elsevier, vol. 80(C), pages 266-274.
    20. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.
    21. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.

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