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Research Papers: Energy Systems Analysis

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

[+] Author and Article Information
Angshuman Deka

Department of Mechanical
and Aerospace Engineering,
University at Buffalo-SUNY,
Buffalo, NY 14260
e-mail: angshuma@buffalo.edu

Nima Hamta

Department of Industrial
and Systems Engineering,
University at Buffalo-SUNY,
Buffalo, NY 14260
e-mail: nimahamt@buffalo.edu

Behzad Esmaeilian

Department of Industrial
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;
Department of Industrial
and Systems Engineering,
University at Buffalo-SUNY,
Buffalo, NY 14260
e-mail: sarabehd@buffalo.edu

1Corresponding 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 138(2), 022001 (Oct 15, 2015) (9 pages) Paper No: JERT-15-1194; doi: 10.1115/1.4031632 History: Received May 24, 2015; Revised September 13, 2015

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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Figures

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Fig. 1

A typical architecture of a neural network

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Fig. 2

Process flow of the method used

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Fig. 6

Graphical comparison of the forecasted values from different models with U.S. EIA data

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Fig. 5

Historical and forecasted values using ARIMA

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Fig. 4

(a) Actual data versus predicted values of regression model 1 and (b) actual data versus predicted values of regression model 2

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Fig. 3

(a) Actual data versus predicted values of ANN model 1 and (b) actual data versus predicted values of ANN model 2

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