24-hours ahead global irradiation forecasting using Multi-Layer Perceptron

The grid integration of variable renewable energy sources implies that their effective production could be predicted, at different times ahead. In the case of solar plants, the driving factor is the global solar irradiation (sum of direct and diffuse solar radiation projected on a plane (Wh/m²)). This paper focuses on the 24-hours ahead forecast of global solar irradiation (i.e. hourly solar irradiation prediction for the day after). A method based on artificial intelligence using Artificial Neural Network (ANN) is reported. The ANN hereafter considered is a Multi-Layer Perceptron (MLP) applied to a pre-treated time series (TS). Two architectures are tested; it is shown that the most relevant is based on a multi-output MLP using endogenous and exogenous input data. A real case 2-years TS is computed and the MLP results are compared with both a statistical approach (AutoRegressive-Moving Average model; ARMA) and a reference persistent approach. Results show that the prediction error estimate (nRMSE) can be reduced by 1.3 points with an ANN compared to ARMA and by 7.8 points compared to the naive persistence.