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Prediction of solar radiation with genetic approach combing multi-model framework

Author

Listed:
  • Wu, Ji
  • Chan, Chee Keong
  • Zhang, Yu
  • Xiong, Bin Yu
  • Zhang, Qing Hai

Abstract

In this paper, a genetic approach combing multi-model framework for solar radiation time series prediction is proposed. The framework starts with the assumption that there exists several different patterns in the stochastic component of the solar radiation series. To uncover the underlying pattern, a genetic algorithm is used to segment the solar series dynamically, and the subsequences are further grouped into different clusters. For each cluster, a prediction model is trained to represent that specific pattern. In the prediction phase, identifying the pattern for current period is of great importance. Thus a procedure for the pattern identification is performed to identify the proper pattern for the series belong to. The prediction result of the proposed framework is then compared to other algorithms. It shows that the proposed framework could provide superior performance compared to others.

Suggested Citation

  • Wu, Ji & Chan, Chee Keong & Zhang, Yu & Xiong, Bin Yu & Zhang, Qing Hai, 2014. "Prediction of solar radiation with genetic approach combing multi-model framework," Renewable Energy, Elsevier, vol. 66(C), pages 132-139.
    • Handle: RePEc:eee:renene:v:66:y:2014:i:c:p:132-139
    DOI: 10.1016/j.renene.2013.11.064
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    References listed on IDEAS

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    1. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    2. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    3. Kaplanis, S. & Kaplani, E., 2007. "A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values," Renewable Energy, Elsevier, vol. 32(8), pages 1414-1425.
    4. Will, A. & Bustos, J. & Bocco, M. & Gotay, J. & Lamelas, C., 2013. "On the use of niching genetic algorithms for variable selection in solar radiation estimation," Renewable Energy, Elsevier, vol. 50(C), pages 168-176.
    5. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    6. Benghanem, Mohamed & Mellit, Adel, 2010. "Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia," Energy, Elsevier, vol. 35(9), pages 3751-3762.
    7. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    Cited by:

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    5. Ramedani, Zeynab & Omid, Mahmoud & Keyhani, Alireza & Shamshirband, Shahaboddin & Khoshnevisan, Benyamin, 2014. "Potential of radial basis function based support vector regression for global solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1005-1011.
    6. Güçlü, Yavuz Selim & Dabanlı, İsmail & Şişman, Eyüp & Şen, Zekai, 2015. "HARmonic–LINear (HarLin) model for solar irradiation estimation," Renewable Energy, Elsevier, vol. 81(C), pages 209-218.
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    9. Hoyos-Gómez, Laura S. & Ruiz-Muñoz, Jose F. & Ruiz-Mendoza, Belizza J., 2022. "Short-term forecasting of global solar irradiance in tropical environments with incomplete data," Applied Energy, Elsevier, vol. 307(C).
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