Daily Reservoir Inflow Forecasting Using Fuzzy Inference Systems

dc.creatorHuamani I.R.L.
dc.creatorBallini R.
dc.creatorHidalgo I.G.
dc.creatorBarbosa P.S.F.
dc.creatorFrancato A.L.
dc.date2011
dc.date2015-06-30T20:43:26Z
dc.date2015-11-26T14:53:59Z
dc.date2015-06-30T20:43:26Z
dc.date2015-11-26T14:53:59Z
dc.date.accessioned2018-03-28T22:05:50Z
dc.date.available2018-03-28T22:05:50Z
dc.identifier9781424473175
dc.identifierIeee International Conference On Fuzzy Systems. , v. , n. , p. 2745 - 2751, 2011.
dc.identifier10987584
dc.identifier10.1109/FUZZY.2011.6007690
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-80053070771&partnerID=40&md5=681635d3d19336612a4c517662666248
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/108994
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/108994
dc.identifier2-s2.0-80053070771
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1255048
dc.descriptionThis paper presents the application of a methodology for daily reservoir inflow forecasting in Brazilian hydroelectric plants. The methodology is based on Fuzzy Inference Systems (FIS) and the technique used for adjusting of the model parameters is an offline version of the Expectation Maximization (EM) algorithm. In order to automate the application of the methodology and facilitate the analysis of the results, a tool that allows managing streamflow forecasting studies and visualizing their information in graphical form was developed. A case study was applied to the data from three Brazilian hydroelectric plants whose operation is under the coordination of the Electric System National Operator. They are located in the Grande basin, a part of the Parana basin with two main rivers: the Grande and the Pardo. The benefits of the model are analyzed using statistics calculations, such as: root mean square error, mean absolute percentage error, mean absolute error and mass curve coefficient. Besides that, graphics that compare the registered and predicted streamflow are presented. The results show an adequate performance of the model, leading to a promising alternative for daily streamflow forecasting. © 2011 IEEE.
dc.description
dc.description
dc.description2745
dc.description2751
dc.descriptionNational Science Council,Ministry of Education,Bureau of Foreign Trade
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dc.languageen
dc.publisher
dc.relationIEEE International Conference on Fuzzy Systems
dc.rightsfechado
dc.sourceScopus
dc.titleDaily Reservoir Inflow Forecasting Using Fuzzy Inference Systems
dc.typeActas de congresos


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