Navegando por Assunto "Load forecasting"

Agora exibindo 1 - 2 de 2

Acesso aberto (Open Access)
Estratégia para predição de consumo de energia elétrica de curto prazo: uma abordagem baseada em densificação com MEAN SHIFT para tratamento de dias especiais
(Universidade Federal do Pará, 2016-11-04) RÊGO, Liviane Ponte; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858
The use of short-term prediction strategies is an important tool for planning and operation of electrical systems, playing a crucial part in aiding the decision support process for buying and selling of electricity in the future market. For the energy market, in particular, an important component to take into account for consumption forecasting are the special days (holidays or atypical days, for example). Given its unusual behavior, the estimation of such events can be a complex task, when compared to the forecasting of ordinary days. In addition, as they are often found with only a small number of samples, it is difficult to adequately train and validate prediction algorithms. To tackle these problems, this work presents a model for short-term load forecasting using the Information Theoretic Learning Mean-Shift model to clustering and densify the sample size of special days's events on a time series, there on followed by the prediction using statistical and/or machine learning algorithms; in this work represented by artificial neural network algorithms and multiple Linear regression. The model was applied in a load forecasting problem for the electric utility in the northern region of Brazil, providing an improvement in the accuracy of results.
Acesso aberto (Open Access)
Modelos para previsão de carga a curto prazo através de redes neurais artificiais com treinamento baseado na teoria da informação
(Universidade Federal do Pará, 2011-11-04) ALVES, Wesin Ribeiro; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
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.