Forecasting United States' industrial sector energy demand using artificial neural networks

In the United States, the industrial sector is the driving engine of economic development, and the energy consumption in this sector may be considered as the fuel for this engine. In order to keep this sector sustainable (diverse and productive over time), energy planning should be carried out comprehensively and precisely. In this study, an ANN model was applied to forecast the industrial energy demand and perform future projections for the period 2013-2030. Among all effective independent parameters on energy demand in the industrial sector, energy costs and GDP growth have been considered in this study based on correlation coefficient analysis. For the future trend of GDP, a second order polynomial equation is fitted to the GDP growth curve. For the other independent variables, we define three scenarios for potential future changes: Constant Price Scenario, Ascending Price Scenario, and Descending Price Scenario. The Constant Price and Descending Price scenarios show increases in energy demand, while results show that along with an increase in energy prices, the demand may decrease slightly. For comparison purposes, the results of the three scenarios are presented along with the predictions from the EIA presented in the Annual Energy Outlook 2013.