  The renewable energies prediction and particularly global radiation
forecasting is a challenge studied by a growing number of research teams. This
paper proposes an original technique to model the insolation time series based
on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving
Average (ARMA) model. While ANN by its non-linear nature is effective to
predict cloudy days, ARMA techniques are more dedicated to sunny days without
cloud occurrences. Thus, three hybrids models are suggested: the first proposes
simply to use ARMA for 6 months in spring and summer and to use an optimized
ANN for the other part of the year; the second model is equivalent to the first
but with a seasonal learning; the last model depends on the error occurred the
previous hour. These models were used to forecast the hourly global radiation
for five places in Mediterranean area. The forecasting performance was compared
among several models: the 3 above mentioned models, the best ANN and ARMA for
each location. In the best configuration, the coupling of ANN and ARMA allows
an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum
in winter (0.9%) where ANN alone is the best.
