Artificial Neural Networks Modeling of the Electricity Demands in the Jordanian Industrial Sector.

Since the beginning of the 21st century, it has been noted that the rate of energy consumption has increased globally due to the technological progress that has taken place. The industrial sector occupies one of the first sectors in the consumption of electrical energy. Therefore, this paper aims to use the method of artificial neural networks (ANN) to model, analyze, and forecast electricity consumption in Jordan's industrial sector. In the present analysis, several factors affecting energy consumption in this sector are studied and verified, namely: the number of the industrial establishments (ES), the number of employees (EM), the electricity price (E$), the price of fuel (F$), gross output (G), structural effect (GI/GN) and the capacity utilization (CU). Several networks are executed and tested in terms of the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The results of this study implicated that the electricity consumption levels in the Jordanian industrial segment are highly driven by the number of industrial establishments and employees followed by the gross output, among others. Additionally, the present ANN-predicted electricity consumption results are compared with literature and showed superior accuracy. Finally, this ANN model is combined with the time series analysis approach to forecast the electricity needs of the Jordanian industrial sector for the next decade. [ABSTRACT FROM AUTHOR]