Estimated electric power consumption by means of artificial neural networks and autoregressive models with exogenous input methods

The growth electric energy demand in the industrial and commercial sectors and in public and private buildings represents a problem to estimate electrical consumption in these sectors in order to avoid fines imposed by the respective companies supplying electricity. This study presents artificial neural networks (ANN) and autoregressive models with exogenous input (ARX) models to calculate and to predict the electrical consumption for public sector using heuristic procedures. This system allows estimating the electric power consumption of the next few months ahead, and therefore, a better management of electric energy. The model validation is performed by comparing the results with a nonlinear regression model, ANN and autoregressive models with exogenous input models and the real data with analysis of variance (ANOVA). The ANN models results are estimate confidence intervals of 95%. The variables used as inputs to the neural model estimated are temperature, relative humidity, power consumption and time (day and hour). The algorithm used to estimate is Levenberg-Marquardt.   Key words: Analysis of variance (ANOVA), artificial neural networks (ANN), autoregressive models with exogenous input (ARX), energy management, estimating power consumption.