2014-07-292012-12-042012-09-05FERREIRA, Carlos da Costa. Daily natural incoming flow to the reservoir Tucuruí using the technique of artificial neural networks. 2012. 117 f. Dissertação (Mestrado em Engenharia) - Universidade Federal de Goiás, Goiânia, 2012.http://repositorio.bc.ufg.br/tede/handle/tde/970The forecast of natural flows to hydroelectric plant reservoirs is an essential input to the planning and programming of the SIN´s operation. Various computer models are used to determine these forecasts, including physical models, statistical models and the ones developed with the RNA´s techniques. Currently, the ONS performs daily forecasts of natural flows to the UHE Tucuruí based on the univariate stochastic model named PREVIVAZH, developed by Electric Energy Research Center - Eletrobras CEPEL. Throughout the last decade, several papers have shown evolution in the application of neural networks methodology in many areas, specially in the prediction of flows on a daily, weekly and monthly basis. The goal of this dissertation is to present and calibrate a model of natural flow forecast using the RNA´s methodology, more specifically the NSRBN (Non-Linear Sigmoidal Regression Blocks Networks) (VALENCA; LUDERMIR, 2001), on a time lapse from 1 to 12 days forward to the Tucuruí Hydroelectric Plant, considering the hydrometric stations data located upstream from it s reservoir. In addition, a comparative analysis of results found throughout the calibrated neural network and the ones released by ONS is performed. The results show the advantage of the methodology of artificial neural networks on autoregressive models. The Mean Absolute Percentage Error - MAPE values obtained were, on average, 48 % lower than those released by the ONS.application/pdfAcesso Abertoprevisão de vazões naturaisredes neurais artificiaisusinas hidrelétricasplanejamento da geraçãoredes neurais por combinação de blocos de regressões sigmoides não-linearforecastingartificial neural networkshydroelectric power generationpower generation planningnon-linear Sigmoidal regression blocks networksCNPQ::ENGENHARIASDissertação