Modelos para previsão de carga a curto prazo através de redes neurais artificiais com treinamento baseado na teoria da informação

Abstract

The previous knowledge of the load value is almighty important to the electric power system planning and operation. This paper presents results of an investigative study of application of Artificial Neural Networks as a Multilayer Perceptron with the training based on Information Theory to the problem of short term load forecasting. The learning based on Information Theory focuses on the use of the amount of information (Entropy) for the training of neural network. Two forecaster models are presented, and that they was developed using real data from an energy utility. To compare and verify the efficiency of the proposed systems, it was also developed a forecasting system using neural network trained based on the traditional criterion of mean square error (MSE). The results has showed the efficiency of proposed systems, which had better results when compared with the forecasting system based on neural network trained by criterion of MSE and with forecasting system already was presented in the literature.