Artificial neural network based daily local forecasting for global solar radiation

When a part of the power is generated by grid connected photovoltaic installations, an effective global solar irradiation (GSI) forecasting tool becomes a must to ensure the quality and the security of the electrical grid. GSI forecasts allow the quantification of generated photovoltaic (PV) power and helps electrical grid operators anticipate problems related to the nature of PV power and the planning for adequate solutions and decisions. In this study, a new methodology for local forecasting of daily global horizontal irradiance (GHI) is proposed. This methodology is a combination of spatial modelling and artificial neural networks (ANNs) techniques. An ANN based model is developed to predict the local GHI based on daily weather forecasts provided by the US National Oceanic and Atmospheric Administration (NOAA) for four neighbouring locations. The methodology was tested for two locations; Le Bourget du Lac (45°38′44″N, 5°51′33″E), which is located in the French Alps and Cadarache (43°42′28″N, 05°46′31″E), which is located in the south of France. The model's forecasts were compared to measured data for the two locations and validation results indicate that the ANN-based method presented in this study can estimate daily GHI with satisfactory accuracy.