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Master's Dissertation
DOI
10.11606/D.18.2010.tde-15092010-102430
Document
Author
Full name
Luciano Carli Moreira de Andrade
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2010
Supervisor
Committee
Silva, Ivan Nunes da (President)
Asada, Eduardo Nobuhiro
Feltrin, Antonio Padilha
Title in Portuguese
Abordagem neurofuzzy para previsão de demanda de energia elétrica no curtíssimo prazo
Keywords in Portuguese
Demanda de eletricidade
Previsão de curtíssimo prazo
Sistemas neuro-fuzzy
Abstract in Portuguese
Uma vez que sistemas de inferência neuro-fuzzy adaptativos são aproximadores universais que podem ser usados em aplicações de aproximação de funções e de previsão, este trabalho tem por objetivo determinar seus melhores parâmetros e suas melhores arquiteturas com o propósito de se executar previsão de demanda de energia elétrica no curtíssimo prazo em subestações de distribuição. Isto pode possibilitar o desenvolvimento de controles automáticos de carga mais eficientes para sistemas elétricos de potência. As entradas do sistema são séries temporais de demanda de energia elétrica, compostas por dados mensurados em intervalos de cinco minutos ao longo de sete dias em subestações localizadas em cidades do interior do estado de São Paulo. Diversas configurações de entrada e diferentes arquiteturas foram examinadas para se fazer a previsão de um passo a frente. Os resultados do sistema de inferência neuro-fuzzy adaptativo frente às abordagens encontradas na literatura foram promissores.
Title in English
Neurofuzzy approach for very-short term load demand forecasting
Keywords in English
Load demand
Neuro-fuzzy system
Very short-term forecasting
Abstract in English
Since adaptive neuro-fuzzy inference systems are universal approximators that can be used in functions approximation and forecasting applications, this work has the objective to determine their best parameters and best architectures with the purpose to execute very short term load forecasting in distribution substations. This can allow the development of more efficient load automatic control for power systems. The system inputs are load demand time series, which are composed of data measured at each five minutes interval, during seven days, from substations located in cities from São Paulo state countryside. Several input configurations and different architectures were examined to make a prediction aiming one step forecasting. The adaptive neuro-fuzzy inference system results in comparison with other approaches found in literature were promising.
 
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Luciano.pdf (1.03 Mbytes)
Publishing Date
2010-09-17
 
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