  The official meteorological network is poor on the island of Corsica: only
three sites being about 50 km apart are equipped with pyranometers which enable
measurements by hourly and daily step. These sites are Ajaccio (seaside),
Bastia (seaside) and Corte (average altitude of 486 meters). This lack of
weather station makes difficult the predictability of PV power grid
performance. This work intends to study a methodology which can predict global
solar irradiation using data available from another location for daily and
hourly horizon. In order to achieve this prediction, we have used Artificial
Neural Network which is a popular artificial intelligence technique in the
forecasting domain. A simulator has been obtained using data available for the
station of Ajaccio that is the only station for which we have a lot of data: 16
years from 1972 to 1987. Then we have tested the efficiency of this simulator
in two places with different geographical features: Corte, a mountainous region
and Bastia, a coastal region. On daily horizon, the relocation has implied
fewer errors than a naive prediction method based on the persistence (RMSE=1468
Vs 1383Wh/m2 to Bastia and 1325 Vs 1213Wh/m2 to Corte). On hourly case, the
results were still satisfactory, and widely better than persistence (RMSE=138.8
Vs 109.3 Wh/m2 to Bastia and 135.1 Vs 114.7 Wh/m2 to Corte). The last
experiment was to evaluate the accuracy of our simulator on a PV power grid
localized at 10 km from the station of Ajaccio. We got errors very suitable
(nRMSE=27.9%, RMSE=99.0 W.h) compared to those obtained with the persistence
(nRMSE=42.2%, RMSE=149.7 W.h).
