A fast electric load forecasting using neural networks


Autoria(s): Lopes, Mara Lúcia M.; Minussi, Carlos R.; Lotufo, Anna Diva P.
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

27/05/2014

27/05/2014

01/12/2000

Resumo

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.

Formato

646-649

Identificador

http://dx.doi.org/10.1109/MWSCAS.2000.952840

Midwest Symposium on Circuits and Systems, v. 2, p. 646-649.

http://hdl.handle.net/11449/66342

10.1109/MWSCAS.2000.952840

WOS:000172099300150

2-s2.0-0034463498

Idioma(s)

eng

Relação

Midwest Symposium on Circuits and Systems

Direitos

closedAccess

Palavras-Chave #Backpropagation #Fuzzy control #Fuzzy sets #Gradient methods #Kalman filtering #Neural networks #Regression analysis #Binary systems #Linear regression #Electric load forecasting
Tipo

info:eu-repo/semantics/conferencePaper