Electricity consumption is currently an issue of great interest for power companies that need an as much as accurate profile for controlling the installed systems but also for designing future expansions and alterations. Detailed monitoring has proved to be valuable for both power companies and consumers. Further, as smart grid technology is bound to result to increasingly flexible rates, an accurate forecast is bound to prove valuable in the future. In this paper, a monitoring and forecasting system is investigated. The monitoring system was installed in an actual building and the recordings were used to design and evaluate the forecasting system, based on an artificial neural network. Results show that the system can provide detailed monitoring and also an accurate forecast for a building’s consumption.
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.
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