Photovoltaic power plant output estimation by neural networks and fuzzy inference

The stochastic production of renewable energy sources puts increased demands on power grids worldwide. Neurocomputing methods can be used for the forecast of electric energy production of renewable resources and contribute to the reliability of energy systems. This study compares two neurocomputing methods as predictors of a selected photovoltaic power plant in the Czech Republic that meets the real world criterion of high output variance and relatively large installed power.