Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||Streamflow Forecasting Using Neural Networks And Fuzzy Clustering Techniques|
|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.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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