  When a territory is poorly instrumented, geostationary satellites data can be
useful to predict global solar radiation. In this paper, we use geostationary
satellites data to generate 2-D time series of solar radiation for the next
hour. The results presented in this paper relate to a particular territory, the
Corsica Island, but as data used are available for the entire surface of the
globe, our method can be easily exploited to another place. Indeed 2-D hourly
time series are extracted from the HelioClim-3 surface solar irradiation
database treated by the Heliosat-2 model. Each point of the map have been used
as training data and inputs of artificial neural networks (ANN) and as inputs
for two persistence models (scaled or not). Comparisons between these models
and clear sky estimations were proceeded to evaluate the performances. We found
a normalized root mean square error (nRMSE) close to 16.5% for the two best
predictors (scaled persistence and ANN) equivalent to 35-45% related to ground
measurements. Finally in order to validate our 2-D predictions maps, we
introduce a new error metric called the gamma index which is a criterion for
comparing data from two matrixes in medical physics. As first results, we found
that in winter and spring, scaled persistence gives the best results (gamma
index test passing rate is respectively 67.7% and 86%), in autumn simple
persistence is the best predictor (95.3%) and ANN is the best in summer
(99.8%).
