Modelo Neurofuzzy Eficiente Para Previsão de Carga no Curtíssimo Prazo (Efficient Neurofuzzy Model to Very Short-Term Load Forecasting)

Ivan Nunes da Silva (insilva@sc.usp.br)1, Luciano Carli Moreira de Andrade (lucarli@sc.usp.br)1


1Universidade de São Paulo

This paper appears in: Revista IEEE América Latina

Publication Date: Feb. 2016
Volume: 14,   Issue: 2 
ISSN: 1548-0992


Abstract:
Since adaptive neurofuzzy inference systems are universal approximators that can be used in prediction applications, this study aims to determine the best parameters and their best architectures for the purpose of performing very short-term load demand forecasting in power distribution substations. The system inputs are load demand time series, consisting of data measured at five-minute intervals over seven days. Several input configurations and different architectures were examined to make the forecasting a step forward. The results provided by the adaptive neurofuzzy inference system in relation to the approaches found in the literature are promising.

Index Terms:
load forecasting, intelligent systems, power system parameter estimation, fuzzy neural networks, decision support systems   


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