Streamflow Forecasting Using Neural Networks And Fuzzy Clustering Techniques

dc.creatorLuna I.
dc.creatorSoares S.
dc.creatorMagalhaes M.H.
dc.creatorBallini R.
dc.date2005
dc.date2015-06-26T14:10:03Z
dc.date2015-11-26T14:10:09Z
dc.date2015-06-26T14:10:03Z
dc.date2015-11-26T14:10:09Z
dc.date.accessioned2018-03-28T21:10:48Z
dc.date.available2018-03-28T21:10:48Z
dc.identifier0780390482; 9780780390485
dc.identifierProceedings Of The International Joint Conference On Neural Networks. , v. 4, n. , p. 2631 - 2636, 2005.
dc.identifier
dc.identifier10.1109/IJCNN.2005.1556318
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33750127770&partnerID=40&md5=235c7231504fd5542cf4dd505132221b
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/93948
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/93948
dc.identifier2-s2.0-33750127770
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1241438
dc.descriptionPlanning 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.
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dc.description2631
dc.description2636
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dc.languageen
dc.publisher
dc.relationProceedings of the International Joint Conference on Neural Networks
dc.rightsfechado
dc.sourceScopus
dc.titleStreamflow Forecasting Using Neural Networks And Fuzzy Clustering Techniques
dc.typeActas de congresos


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