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

Outlines Motivation Objectives Methodology Experiments Conclusions Comments 2

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

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

Methodology 5

ARIMABayesian Markov chains KNN 6

Methodology ARIMABayesian Markov chains KNN 7

Methodology ARIMABayesian Markov chains KNN 8

Methodology ARIMABayesian Markov chains KNN 9

Experiments 10

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

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

Experiments 13

Experiments 14

Experiments 15

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

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