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The renewable energies prediction and particularly global radiationforecasting is a challenge studied by a growing number of research teams. Thispaper proposes an original technique to model the insolation time series basedon combining Artificial Neural Network (ANN) and Auto-Regressive and MovingAverage (ARMA) model. While ANN by its non-linear nature is effective topredict cloudy days, ARMA techniques are more dedicated to sunny days withoutcloud occurrences. Thus, three hybrids models are suggested: the first proposessimply to use ARMA for 6 months in spring and summer and to use an optimizedANN for the other part of the year; the second model is equivalent to the firstbut with a seasonal learning; the last model depends on the error occurred theprevious hour. These models were used to forecast the hourly global radiationfor five places in Mediterranean area. The forecasting performance was comparedamong several models: the 3 above mentioned models, the best ANN and ARMA foreach location. In the best configuration, the coupling of ANN and ARMA allowsan improvement of more than 1%, with a maximum in autumn (3.4%) and a minimumin winter (0.9%) where ANN alone is the best.

Hybrid methodology for hourly global radiation forecasting in Mediterranean area