  This paper proposes to use a rather new modelling approach in the realm of
solar radiation forecasting. In this work, two forecasting models:
Autoregressive Moving Average (ARMA) and Neural Network (NN) models are
combined to form a model committee. The Bayesian inference is used to affect a
probability to each model in the committee. Hence, each model's predictions are
weighted by their respective probability. The models are fitted to one year of
hourly Global Horizontal Irradiance (GHI) measurements. Another year (the test
set) is used for making genuine one hour ahead (h+1) out-of-sample forecast
comparisons. The proposed approach is benchmarked against the persistence
model. The very first results show an improvement brought by this approach.
