Neural Networks in Forecasting Electrical Energy Consumption

Abstract

This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting. In order 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) mean absolute percentage errors (MAPE) are presented for both models. Moreover, autoregressive and ARIMA modeling was applied (Saab, Badr and Nasr 2001). In this paper, two models were built to forecast electrical energy consumption (EEC) in Lebanon using artificial neural networks (ANN). The first model is univariate and fully connected model based on past EEC values. The second model is a multivariate not fully connected model based on past degree days (DD) and EEC time series. The monthly DD data is calculated from the daily mean temperatures obtained from climatological monthly bulletins (Ghaddar, 1995-1999). DD is used to indicate the days requiring energy usage for comfortable indoor living.