Water Inflow Forecasting using the Echo State Network: a Brazilian Case Study
A type of recurrent neural network has been proposed by H. Jaeger. This model, called Echo State Network (ESN), possesses a highly interconnected and recurrent topology of nonlinear processing elements, which constitutes a "reservoir of rich dynamics" and contains information about the history of input or/and output patterns. The interesting property of ESN is that only the memoryless readout is trained, whereas the recurrent topology has fixed connection weights. This reduces the complexity of recurrent neural network training to simple linear regression while preserving a recurrent topology. In this paper, the ESN is used to forecast hydropower plant reservoir water inflow, which is a fundamental information to the hydrothermal power system operation planning. A database of average monthly water inflows of Furnas plant, one of the Brazilian hydropower plants, was used as source of training and test data. The performance of the ESN is compared with SONARX network, RBF network and ANFIS model. The results show that the Echo State Network provides pretty good results for one-step ahead water inflow forecasting, providing a valuable information for the system operator.