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Forecasting of preprocessed daily solar radiation time series using neural networks
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  1. Presenter : Cheng-Han Tsai Authors : Christophe Paoli, Cyril Voyant, Marc Muselli, Marie-Laure Nivet SOLAR ENERGY, 2010 Forecasting of preprocessed daily solar radiation time series using neural networks

  2. Outlines Motivation Objectives Methodology Experiments Conclusions Comments

  3. Motivation A lot of methods’ performance be affected by disruptors such as diffuse, ground-reflected and seasonal climate.

  4. Objectives This paper has used a MLP and pre-processing for the daily prediction of global solar radiation to deal with the above problems.

  5. Methodology

  6. Methodology

  7. Methodology

  8. Methodology

  9. Methodology

  10. Experiments

  11. Experiments Cleaning the measure errors Ad-hoc time series preprocessing Corrected time series Forecasting methods & Predicted irradiation

  12. Experiments Ad-hoc time series preprocessing Clearness index Clear sky index

  13. Experiments

  14. Experiments

  15. Experiments

  16. Conclusions This prediction model has been compared to other prediction methods These simulation tools have been successfully validated on the DC energy prediction

  17. Comments • Advantages • This paper considers seasonal factors • Applications • Solar radiation prediction