Application of neural networks for hydro power plant water inflow forecasting

Water inflow forecasting is usually based on precipitation data collected by the ombrometer stations in the river basin. The solution to this problem is rather complex due to the highly nonlinear relation between the amount of precipitation at different locations and water inflow. In the paper, a new approach to forecasting water inflow into the head of the hydro power plant reservoir based on neural networks is described. First, the selection of input parameters is discussed. Next, the most appropriate architecture of the neural network is chosen. Finally, efficacy of the proposed method is tested for a practical case and some results are presented.