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 the historical data from 1950 to 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 that the energy demand would vary between 95.51 and 100.08 quadrillion British thermal unit (btu) for the period of 2014–2019. In addition, we have discussed the possibility of self-sufficiency of the United States in terms of energy generation based on the information of current available technologies nationwide.

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

Angshuman Deka

,
Angshuman Deka

Department of Mechanical

and Aerospace Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: angshuma@buffalo.edu

and Aerospace Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: angshuma@buffalo.edu

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Nima Hamta

,
Nima Hamta

Department of Industrial

and Systems Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: nimahamt@buffalo.edu

and Systems Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: nimahamt@buffalo.edu

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Behzad Esmaeilian

,
Behzad Esmaeilian

Department of Industrial

and Systems Engineering,

Northern Illinois University,

DeKalb, IL 60115

e-mail: besmaeilian@niu.edu

and Systems Engineering,

Northern Illinois University,

DeKalb, IL 60115

e-mail: besmaeilian@niu.edu

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Sara Behdad

Sara Behdad

Department of Mechanical

and Aerospace Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260;

and Aerospace Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260;

Department of Industrial

and Systems Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: sarabehd@buffalo.edu

and Systems Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: sarabehd@buffalo.edu

Search for other works by this author on:

Angshuman Deka

and Aerospace Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: angshuma@buffalo.edu

Nima Hamta

and Systems Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: nimahamt@buffalo.edu

Behzad Esmaeilian

and Systems Engineering,

Northern Illinois University,

DeKalb, IL 60115

e-mail: besmaeilian@niu.edu

Sara Behdad

Department of Mechanical

and Aerospace Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260;

and Aerospace Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260;

Department of Industrial

and Systems Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: sarabehd@buffalo.edu

and Systems Engineering,

University at Buffalo-SUNY,

Buffalo, NY 14260

e-mail: sarabehd@buffalo.edu

1

Corresponding author.
Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received May 24, 2015; final manuscript received September 13, 2015; published online October 15, 2015. Editor: Hameed Metghalchi.

*J. Energy Resour. Technol*. Mar 2016, 138(2): 022001 (9 pages)

**Published Online:**October 15, 2015

Article history

Received:

May 24, 2015

Revised:

September 13, 2015

Citation

Deka, A., Hamta, N., Esmaeilian, B., and Behdad, S. (October 15, 2015). "Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors." ASME. *J. Energy Resour. Technol*. March 2016; 138(2): 022001. https://doi.org/10.1115/1.4031632

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