Flow estimation using an Elman networks

Author

Neto, Luiz Biondi ; Coelho, P.H.G. ; Soares de Mello, Joao Carlos C. B. ; Meza, L.A. ; Fernandes Velloso, Maria Luiza

Author_Institution

Dept. of Electron. & Telecommun., State Univ. of Rio de Janeiro, Brazil

Volume

2

fYear

2004

fDate

25-29 July 2004

Firstpage

831

Abstract

This paper investigates the application of partially recurrent artificial neural networks (ANN) in the flow estimation for Sao Francisco river that feeds the hydroelectric power plant of Sobradinho. An Elman neural network was used, suitably arranged to receive samples of the flow time series data available for Sao Francisco river shifted by one month. The data used in the application concern to the measured Sao Francisco river flow time series from 1931 to 1996, in a total of 65 years from what 60 were used for training and 5 for testing. The obtained results indicate that the Elman neural network is suitable to estimate the river flow for 5 year periods monthly. The average estimation error was less than 0.2%.

Keywords

flow measurement; hydroelectric power stations; learning (artificial intelligence); power engineering computing; recurrent neural nets; time series; Elman neural network; Sao Francisco river flow estimation; Sobradinho hydroelectric power plant; flow time series data; neural network training; recurrent ANN; recurrent artificial neural networks; Artificial neural networks; Electronic mail; Fluid flow measurement; Ice; Neural networks; Reservoirs; Rivers; Sea measurements; Uncertainty; Water resources;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1380037

Filename

1380037