Author :
Valenpa, M. ; Ludermir, Teresa ; Valenpa, A.
Abstract :
In this study, a constructive neural networks model (NSRBN) were used to forecast daily river flows for the Boa Esperanga hydroelectric power plant, part of the Chesf (Companhia Hidreletrica do Sao Francisco) system. This dam is located at Parnaiba River, in the borderline between Maranhdo and Piaui, two Brazilian States. Several studies have been dedicated to the prediction of river flows with no exogenous inputs that are with the only use of past flow observations. In the present work, constructive neural networks are first used without exogenous input that is without the use of rainfall observations. Only the last measured discharges are provided as input to the networks, analyzing the performance of the forecasts provided for the validation sets over the varying lead-times. In the second type of application, the same optimal number of past discharges is given as input to the ANN, along with exogenous inputs, that is past rainfall values, thus testing a rainfall-runoff modeling approach. The NSRBN model approach is shown to provide better representation of the daily average water inflow forecasting, than the models based on Box-Jenkins method, currently in use on the Brazilian Electrical Sector.
Keywords :
forecasting theory; geophysics computing; hydroelectric power; hydroelectric power stations; neural nets; power engineering computing; rain; Boa Esperanga hydroelectric power plant; Companhia Hidreletrica do Sao Francisco system; NSRBN model; artificial neural network; constructive neural network model; daily average water inflow forecasting; daily river flow forecasting; rainfall-runoff relationship modeling; river flow prediction; Artificial neural networks; Load forecasting; Neural networks; Performance analysis; Power generation; Power system modeling; Predictive models; Reservoirs; Rivers; Testing;
