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

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Title: Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors.
Authors: Deka, Angshuman1 angshuma@buffalo.edu, Hamta, Nima2 nimahamt@buffalo.edu, Esmaeilian, Behzad3 besmaeilian@niu.edu, Behdad, Sara1,2 sarabehd@buffalo.edu
Source: Journal of Energy Resources Technology. Mar2016, Vol. 138 Issue 2, p022001-1-022001-9. 9p.
Subject Terms: *Power resources forecasting, *Energy policy, *Prediction models, *Decision making in political science, *Artificial neural networks, *Energy consumption
Geographic Terms: United States
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 the historical data from 1950 to 2013. While ANN model I and regression model I 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 US. Energy Information Administration (EIA) for the period of 2014-2019. The forecasted results of ANN models and regression model 1 are dose to those of the US. El A; however, the results of regression model 2 and ARIMA model are significantly different from the forecast made by the US. El A. 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. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
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  Data: Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors.
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  Data: <searchLink fieldCode="AR" term="%22Deka%2C+Angshuman%22">Deka, Angshuman</searchLink><relatesTo>1</relatesTo><i> angshuma@buffalo.edu</i><br /><searchLink fieldCode="AR" term="%22Hamta%2C+Nima%22">Hamta, Nima</searchLink><relatesTo>2</relatesTo><i> nimahamt@buffalo.edu</i><br /><searchLink fieldCode="AR" term="%22Esmaeilian%2C+Behzad%22">Esmaeilian, Behzad</searchLink><relatesTo>3</relatesTo><i> besmaeilian@niu.edu</i><br /><searchLink fieldCode="AR" term="%22Behdad%2C+Sara%22">Behdad, Sara</searchLink><relatesTo>1,2</relatesTo><i> sarabehd@buffalo.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Energy+Resources+Technology%22">Journal of Energy Resources Technology</searchLink>. Mar2016, Vol. 138 Issue 2, p022001-1-022001-9. 9p.
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  Data: *<searchLink fieldCode="DE" term="%22Power+resources+forecasting%22">Power resources forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+policy%22">Energy policy</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Decision+making+in+political+science%22">Decision making in political science</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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 I and regression model I 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 US. Energy Information Administration (EIA) for the period of 2014-2019. The forecasted results of ANN models and regression model 1 are dose to those of the US. El A; however, the results of regression model 2 and ARIMA model are significantly different from the forecast made by the US. El A. 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. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=115067060
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1115/1.4031632
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 022001-1
    Subjects:
      – SubjectFull: United States
        Type: general
      – SubjectFull: Power resources forecasting
        Type: general
      – SubjectFull: Energy policy
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Decision making in political science
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Energy consumption
        Type: general
    Titles:
      – TitleFull: Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors.
        Type: main
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          Name:
            NameFull: Deka, Angshuman
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          Name:
            NameFull: Hamta, Nima
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            NameFull: Esmaeilian, Behzad
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            NameFull: Behdad, Sara
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            – D: 01
              M: 03
              Text: Mar2016
              Type: published
              Y: 2016
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              Value: 138
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            – TitleFull: Journal of Energy Resources Technology
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