Electric load forecasting using a fuzzy ART&ARTMAP neural network

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Data

2005-01-01

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Editor

Elsevier B.V.

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.

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Palavras-chave

adaptive resonance theory, electric load forecasting, electric power systems, neural networks, fuzzy logic, fuzzy ART&ARTMAP neural network

Como citar

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