Electric load forecasting using a fuzzy ART&ARTMAP neural network


Autoria(s): Lopes, MLM; Minussi, C. R.; Lotufo, ADP
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

20/05/2014

20/05/2014

01/01/2005

Resumo

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Formato

235-244

Identificador

http://dx.doi.org/10.1016/j.asoc.2004.07.003

Applied Soft Computing. Amsterdam: Elsevier B.V., v. 5, n. 2, p. 235-244, 2005.

1568-4946

http://hdl.handle.net/11449/9740

10.1016/j.asoc.2004.07.003

WOS:000227208700008

Idioma(s)

eng

Publicador

Elsevier B.V.

Relação

Applied Soft Computing

Direitos

closedAccess

Palavras-Chave #adaptive resonance theory #electric load forecasting #electric power systems #neural networks #fuzzy logic #fuzzy ART&ARTMAP neural network
Tipo

info:eu-repo/semantics/article