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Transport Energy Demand Modeling of the United States Using Artificial Neural Networks and Multiple Linear Regressions

[+] Author Affiliations
Arash Kialashaki, John Reisel

University of Wisconsin-Milwaukee, Milwaukee, WI

Paper No. ES2014-6447, pp. V002T11A004; 10 pages
doi:10.1115/ES2014-6447
From:
  • ASME 2014 8th International Conference on Energy Sustainability collocated with the ASME 2014 12th International Conference on Fuel Cell Science, Engineering and Technology
  • Volume 2: Economic, Environmental, and Policy Aspects of Alternate Energy; Fuels and Infrastructure, Biofuels and Energy Storage; High Performance Buildings; Solar Buildings, Including Solar Climate Control/Heating/Cooling; Sustainable Cities and Communities, Including Transportation; Thermofluid Analysis of Energy Systems, Including Exergy and Thermoeconomics
  • Boston, Massachusetts, USA, June 30–July 2, 2014
  • Conference Sponsors: Advanced Energy Systems Division
  • ISBN: 978-0-7918-4587-5
  • Copyright © 2014 by ASME

abstract

In 2009, the transportation sector was the second largest consumer of primary energy in the United States, following the electric power sector and followed by the industrial, residential, and commercial sectors. The pattern of energy use varies by sector. For example, petroleum provides 96% of the energy used for transportation but its share is much less in other sectors. While the United States consumes vast quantities of energy, it has also pledged to cut its greenhouse gas emissions by 2050. In order to assist in planning for future energy needs, the purpose of this study is to develop a model for transport energy demand that incorporates past trends.

This paper describes the development of two types of transportation energy models which are able to predict the United States’ future transportation energy-demand. One model uses an artificial neural network technique (a feed-forward multilayer perceptron neural network coupled with back-propagation technique), and the other model uses a multiple linear regression technique. Various independent variables (including GDP, population, oil price, and number of vehicles) are tested.

The future transport energy demand can then be forecast based on the application of the growth rate of effective parameters on the models. The future trends of independent variables have been predicted based on the historical data from 1980 using a regression method. Using the forecast of independent variables, the energy demand has been forecasted for period of 2010 to 2030.

In terms of the forecasts generated, the models show two different trends despite their performances being at the same level during the model-test period. Although, the results from the regression models show a uniform increase with different slopes corresponding to different models for energy demand in the near future, the results from ANN express no significant change in demand in same time frame. Increased sensitivity of the ANN models to the recent fluctuations caused by the economic recession may be the reason for the differences with the regression models which predict based on the total long-term trends.

Although a small increase in the energy demand in the transportation sector of the United States has been predicted by the models, additional factors need to be considered regarding future energy policy. For example, the United States may choose to reduce energy consumption in order to reduce CO2 emissions and meet its national and international commitments, or large increases in fuel efficiency may reduce petroleum demand.

Copyright © 2014 by ASME

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