Forecasting of preprocessed daily solar radiation time series using neural networks

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