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A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting

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  • Hocaoglu, Fatih Onur
  • Serttas, Fatih

Abstract

Short term solar radiation forecasting is significant for the operation of big installations and the electrical grid. There are many short term forecasting techniques in the literature. In this study, short term predictions of solar radiation are reviewed. An alternative approach and model is proposed. The approach assumes that solar radiation data repeats itself in the history. According to this initial assumption, a novel Mycielski based model is proposed. This model considers the recorded hourly solar radiation data as an array and starting from the last record value, it tries to find most similar sub-array pattern in the history. This sub-array pattern corresponds to the longest matching solar radiation data array in the history. The data observed after this longest array in history is considered as the prediction. In case several sub-arrays are obtained, the model decides the selection according to the probabilistic relations of the sub-patterns last values to the next value. To model the probabilistic relations of the data, a Markov chain model is adopted and used. By this way historical search model is strengthened. Thanks to the proposed model, accurate predictions are obtained. To show the effectiveness, the results are compared with the models of others in the literature as a final task.

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  • Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    • Handle: RePEc:eee:renene:v:108:y:2017:i:c:p:635-643
    DOI: 10.1016/j.renene.2016.08.058
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    1. Furlan, Claudia & de Oliveira, Amauri Pereira & Soares, Jacyra & Codato, Georgia & Escobedo, João Francisco, 2012. "The role of clouds in improving the regression model for hourly values of diffuse solar radiation," Applied Energy, Elsevier, vol. 92(C), pages 240-254.
    2. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    3. Zjavka, Ladislav, 2015. "Wind speed forecast correction models using polynomial neural networks," Renewable Energy, Elsevier, vol. 83(C), pages 998-1006.
    4. Hervás Soriano, Fernando & Mulatero, Fulvio, 2011. "EU Research and Innovation (R&I) in renewable energies: The role of the Strategic Energy Technology Plan (SET-Plan)," Energy Policy, Elsevier, vol. 39(6), pages 3582-3590, June.
    5. Kashyap, Yashwant & Bansal, Ankit & Sao, Anil K., 2015. "Solar radiation forecasting with multiple parameters neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 825-835.
    6. Zhang, Xiongwen, 2014. "A statistical approach for sub-hourly solar radiation reconstruction," Renewable Energy, Elsevier, vol. 71(C), pages 307-314.
    7. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    8. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    9. Evans, S.P. & Clausen, P.D., 2015. "Modelling of turbulent wind flow using the embedded Markov chain method," Renewable Energy, Elsevier, vol. 81(C), pages 671-678.
    10. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    11. Akarslan, Emre & Hocaoğlu, Fatih Onur & Edizkan, Rifat, 2014. "A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting," Energy, Elsevier, vol. 73(C), pages 978-986.
    12. Batlles, F.J. & Rubio, M.A. & Tovar, J. & Olmo, F.J. & Alados-Arboledas, L., 2000. "Empirical modeling of hourly direct irradiance by means of hourly global irradiance," Energy, Elsevier, vol. 25(7), pages 675-688.
    13. Akarslan, Emre & Hocaoglu, Fatih Onur, 2016. "A novel adaptive approach for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 87(P1), pages 628-633.
    14. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
    Full references (including those not matched with items on IDEAS)

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