This paper describes an application of a neural network that is method forecasting to time variation of insulation intensity. In recent years, research and technological developments in the field of electrical energy have focused on photovoltaic. Therefore, the photovoltaic power generator is introduced in large quantities in the power system in the future is expected. However, despite the high expectations for renewable power generation technologies, it remains difficult to obtain stable power from such distributed sources, primarily because they depend on weather conditions and other variable factors. In order to apply to the supply and demand stable operation, we report a case of developing a method for predicting solar power generation using the gray theory and a neural network.
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