PV power forecasting using different Artificial Neural Networks strategies

The integration of photovoltaic (PV), intermittent and uncontrollable power, into the electrical grid has become one of the major challenges for power system operators. Therefore the PV power forecasting can be beneficial in system planning and balancing energies. In this paper the PV power forecasting of a real generator [1] is presented. Different Artificial Neural Networks (ANN) strategies are used to forecast the PV power from meteorological variables, the radiation and the temperature. Simulation results corresponding to each ANN strategy are presented, discussed and compared. The dynamic ANN chosen in this work is the Nonlinear Auto Regressive models with eXogenous input (NARX model). Its performances have proved in the different time frame PV power forecasting. The impact of season's type on the efficiency of PV power forecasting is presented in the second part of this paper.