SOLAR RADIATION FORECAST USING NEURAL NETWORKS FOR THE PREDICTION OF GRIDCONNECTED PV PLANTS ENERGY PRODUCTION (DSP PROJECT)

摘要

The work presented in this paper is part of a project aimed to develop a prototype device (DSP) able toforecast with a day in advance the energy produced by PV plants. The energy forecast is required by the NationalAuthority for the electricity in order to control the high instabilities of the electric grid induced by unpredictableenergy sources such as photovoltaic. In the paper several models to forecast the hourly solar irradiance with a day inadvance using Artificial Neural Network (ANN) techniques are described. Statistical (ST) models that use only localmeasured data and Hybrid model (HY) that also use Numerical Weather Prediction (NWP) data are tested for theUniversity of Rome “Tor Vergata” site. The performance of ST, NWP and HY models, together with the Persistencemodel (PM), are compared. The ST models and the NWP model exhibit similar results improving the performance ofthe PM of around 20%. Nevertheless different sources of forecast errors between ST and NWP models are identified.The Hybrid models give the better performance, improving the forecast of approximately 39% with respect to thePersistence model.