  In this paper, we present an application of neural networks in the renewable
energy domain. We have developed a methodology for the daily prediction of
global solar radiation on a horizontal surface. We use an ad-hoc time series
preprocessing and a Multi-Layer Perceptron (MLP) in order to predict solar
radiation at daily horizon. First results are promising with nRMSE < 21% and
RMSE < 998 Wh/m2. Our optimized MLP presents prediction similar to or even
better than conventional methods such as ARIMA techniques, Bayesian inference,
Markov chains and k-Nearest-Neighbors approximators. Moreover we found that our
data preprocessing approach can reduce significantly forecasting errors.
