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Type: Artigo de evento
Title: Streamflow Forecasting Using Neural Networks And Fuzzy Clustering Techniques
Author: Luna I.
Soares S.
Magalhaes M.H.
Ballini R.
Abstract: 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.
Rights: fechado
Identifier DOI: 10.1109/IJCNN.2005.1556318
Date Issue: 2005
Appears in Collections:Unicamp - Artigos e Outros Documentos

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