Artificially evolved soft computing models for photovoltaic power plant output estimation

Renewable energy sources are becoming a significant part of todays energy mix. The unstable production of many renewable energy sources including photovoltaic and wind power plants puts increased demands on power transmission systems and on the power grid as a whole. Soft computing methods can contribute to the prediction of electric energy production of renewable resources and therefore to the reliability of the power transmission networks. This work compares two soft computing methods that utilize genetic programming to evolve predictors of a selected renewable energy resource that meets the real world criterion of high output variance and relatively large installed power (in context of the power distribution system of the Czech Republic).