Optimal design of neural tree for solar power prediction
Sebastián Basterrech, Lukáš Prokop, Tomáš Buriánek, Stanislav Mišák · 2014
Today renewable energy sources are integral part of energy mix in most of countries in the world. Carbon reduction issues and other ecological activity provide a wide possibility to progressive increase of installed capacity of renewable energy sources. Huge distribution of instable renewable energy sources like wind and photovoltaic plants brings new tasks in power system control and power system reliability. Prediction of power production is one of the ways to mitigate negative impact of operation of instable energy sources. This work presents application of a neural tree method from the group of soft computing method for renewable energy prediction. In this work we focus on photovoltaic power plant production prediction.