Actas de congresos
Streamflow Forecasting Using Neural Networks And Fuzzy Clustering Techniques
Registro en:
0780390482; 9780780390485
Proceedings Of The International Joint Conference On Neural Networks. , v. 4, n. , p. 2631 - 2636, 2005.
10.1109/IJCNN.2005.1556318
2-s2.0-33750127770
Autor
Luna I.
Soares S.
Magalhaes M.H.
Ballini R.
Institución
Resumen
Planning of hydroelectric systems is a complex and difficult task once it involves non-linear production characteristics and depends on numerous variables. A key variable is the streamflow. Streamflow values covering the entire planning period must be accurately forecasted because they strongly influence energy production. This paper suggests an application of a FIR neural network and a fuzzy clustering-based model to evaluate one-step and multi-step ahead predictions. Results are compared to the ones obtained by a periodic autoregressive model (PAR). It is interesting to apply a recurrent neural network for prediction task due to its ability for temporal processing and efficiency to solve nonlinear problems. The results show a generally better performance of the FIR neural network for the case studied. © 2005 IEEE. 4
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