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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/66342
Title: 
A fast electric load forecasting using neural networks
Author(s): 
Institution: 
Universidade Estadual Paulista (UNESP)
Abstract: 
The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.
Issue Date: 
1-Dec-2000
Citation: 
Midwest Symposium on Circuits and Systems, v. 2, p. 646-649.
Time Duration: 
646-649
Keywords: 
  • Backpropagation
  • Fuzzy control
  • Fuzzy sets
  • Gradient methods
  • Kalman filtering
  • Neural networks
  • Regression analysis
  • Binary systems
  • Linear regression
  • Electric load forecasting
Source: 
http://dx.doi.org/10.1109/MWSCAS.2000.952840
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/66342
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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