Previsão de Vazões Naturais Diárias Afluentes ao Reservatório da UHE Tucuruí Utilizando a Técnica de Redes Neurais Artificiais
Carregando...
Data
2012-09-05
Autores
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Goiás
Resumo
The 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.
Descrição
Palavras-chave
previsão de vazões naturais , redes neurais artificiais , usinas hidrelétricas , planejamento da geração , redes neurais por combinação de blocos de regressões sigmoides não-linear , forecasting , artificial neural networks , hydroelectric power
generation , power generation planning , non-linear Sigmoidal
regression blocks networks
Citação
FERREIRA, 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.