Previsão de curto prazo de cargas elétricas usando combinação de redes neurais artificiais e delineamento de experimentos

Abstract

This work proposes a new approach to short-term load forecast for power distribution substations using the artificial neural network ensemble. In this sense, the main objective of this approach is to make predictions for the same time series using different tools that are competent for this type of problem and then combine the solutions, obtaining a better solution compared to those tools used individually. For the construction of the ensemble, the experimental planning methodology (DOE) was used initially to identify the influence of 6 factors related the parameterization of the ANN and from the desirability optimization method to obtain a parameterization to determine the architecture of the neural networks that formed the ensemble . Then, the normal intersection optimization method (NBI) combined with the technique of exploratory factor analysis (based on mixture design of experiments) was used to establish a set of optimal Pareto solutions for combining the outputs produced by neural networks, forming the output of the ensemble. As a criterion of choice, the maximum ratio between Shannon’s entropy and global percentage error was used and based on the 72 out of sample data the ensemble of artificial neural networks presented better results compared to each individual network.