Neural Networks in Forecasting Electrical Energy Consumption

G. E. Nasr, E. A. Badr, and M. R. Younes, Lebanese American University, Lebanon

This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting. In order to provide the forecasted energy consumption, the ANN interpolates between the EEC and its determinants in a training data set. In this study, two ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on EEC time series and a weather dependent variable, namely, degree days (DD). Forecasting performance measures such as mean square errors (MSE), mean absolute deviations (MAD), mean percentage square errors (MPSE) and mean absolute percentage errors (MAPE) are presented for both models.

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