  Considering the grid manager's point of view, needs in terms of prediction of
intermittent energy like the photovoltaic resource can be distinguished
according to the considered horizon: following days (d+1, d+2 and d+3), next
day by hourly step (h+24), next hour (h+1) and next few minutes (m+5 e.g.).
Through this work, we have identified methodologies using time series models
for the prediction horizon of global radiation and photovoltaic power. What we
present here is a comparison of different predictors developed and tested to
propose a hierarchy. For horizons d+1 and h+1, without advanced ad hoc time
series pre-processing (stationarity) we find it is not easy to differentiate
between autoregressive moving average (ARMA) and multilayer perceptron (MLP).
However we observed that using exogenous variables improves significantly the
results for MLP . We have shown that the MLP were more adapted for horizons
h+24 and m+5. In summary, our results are complementary and improve the
existing prediction techniques with innovative tools: stationarity, numerical
weather prediction combination, MLP and ARMA hybridization, multivariate
analysis, time index, etc.
