Yield of the Hydroelectric Power Plant using Feed Forward and Recurrent Neural Networks: Hirfanlı Dam Application Example

The yield variety of the hydroelectric power plants according to the fall and flow changes depending on climatic conditions was investigated. For this purpose, Hirfanlı Hydroelectric Power Plant in Kaman where is the district of Kırşehir was selected as the sampling plane. The water consumption, decreased the flow of water to energy ratio, yield such data in 2008 from Hirfanlı Hydroelectric Power Plant and this data changes in the amount of precipitation, climate data were taken from the Kaman Weather Directorate and examination was performed about them. In this study, the methods to apply the feed forward and Elman’s recurrent neural networks to estimate the net head according to climatic data and power plant production yield relationship. According to this data, the yield data of the hydroelectric power plants have been estimated to the system. As a result, the yield variety of hydroelectric power plants was found to be effective from all these climatic factors by using neural network structures.