Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors

Abstract

Effective energy planning and governmental decision making policies heavily rely on accurate forecast of energy demand. This paper discusses and compares five different forecasting techniques to model energy demand in the United States using economic and demographic factors. Two Artificial Neural Network (ANN) models, two regression analysis models and one autoregressive integrated moving average (ARIMA) model are developed based on historical data from 1950–2013. While ANN model 1 and regression model 1 use Gross Domestic Product (GDP), Gross National Product (GNP) and per capita personal income as independent input factors, ANN model 2 and regression model 2 employ GDP, GNP and population (POP) as the predictive factors. The forecasted values resulted from these models are compared with the forecast made by the U.S. Energy Information Administration (EIA) for the period of 2014–2019. The forecasted results of ANN models and regression model 1 are close to those of the U.S. EIA, however the results of regression model 2 and ARIMA model are significantly different from the forecast made by the U.S. EIA. Finally, a comparison of the forecasted values resulted from three efficient models showed the energy demand would vary between 95.51 and 100.08 quadrillion British thermal unit for the period of 2014–2019.