Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation
This work addresses forecasting global radiation for renewable energy applications in the Mediterranean area, representing an incremental improvement over existing models.
The paper tackles the problem of predicting global radiation by proposing a hybrid ARMA/ANN model that uses data from a numerical weather prediction model, achieving an nRMSE of 14.9% compared to 26.2% for a naive persistence predictor.
We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/ARMA is 14.9% compared to 26.2% for the naïve persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposed