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Understanding Errors in EIA Projections of Energy Demand

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  • Fischer, Carolyn

    (Resources for the Future)

  • Herrnstadt, Evan
  • Morgenstern, Richard D.

Abstract

This paper investigates the potential for systematic errors in the Energy Information Administration’s (EIA) widely used Annual Energy Outlook, focusing on the near- to midterm projections of energy demand as measured in physical quantities. Overall, based on an analysis of the EIA’s 22-year projection record, we find a fairly modest but persistent tendency to underestimate total energy demand by an average of 2 percent per year over the one- to five-year projection horizon after controlling for projection errors in gross domestic product, oil prices, and heating/cooling degree days. For the 14 individual fuels/consuming sectors routinely reported by the EIA, we observe a great deal of directional consistency in the error patterns over time, ranging up to 7 percent per year. Electric utility renewables, electric utility natural gas, transportation distillate, and residential electricity all show significant biases, on average, across the full five year projection horizon examined. Projections for certain other fuels/consuming sectors have significant unexplained errors for selected time horizons. Independent evaluation of this type can be useful for validating ongoing analytic efforts and for prioritizing future model revisions.

Suggested Citation

  • Fischer, Carolyn & Herrnstadt, Evan & Morgenstern, Richard D., 2008. "Understanding Errors in EIA Projections of Energy Demand," RFF Working Paper Series dp-08-54, Resources for the Future.
    • Handle: RePEc:rff:dpaper:dp-08-54
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    File URL: http://www.rff.org/RFF/documents/RFF-DP-07-54.pdf
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    References listed on IDEAS

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    1. Auffhammer, Maximilian, 2007. "The rationality of EIA forecasts under symmetric and asymmetric loss," Resource and Energy Economics, Elsevier, vol. 29(2), pages 102-121, May.
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    Cited by:

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    2. Bernard, Jean-Thomas & Khalaf, Lynda & Kichian, Maral & Yelou, Clement, 2018. "Oil Price Forecasts For The Long Term: Expert Outlooks, Models, Or Both?," Macroeconomic Dynamics, Cambridge University Press, vol. 22(3), pages 581-599, April.
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    4. Gilbert, Alexander Q. & Sovacool, Benjamin K., 2016. "Looking the wrong way: Bias, renewable electricity, and energy modelling in the United States," Energy, Elsevier, vol. 94(C), pages 533-541.
    5. James G. Baldwin & Ian Sue Wing, 2013. "The Spatiotemporal Evolution Of U.S. Carbon Dioxide Emissions: Stylized Facts And Implications For Climate Policy," Journal of Regional Science, Wiley Blackwell, vol. 53(4), pages 672-689, October.
    6. Wilkerson, Jordan T. & Cullenward, Danny & Davidian, Danielle & Weyant, John P., 2013. "End use technology choice in the National Energy Modeling System (NEMS): An analysis of the residential and commercial building sectors," Energy Economics, Elsevier, vol. 40(C), pages 773-784.
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    8. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2022. "Accuracy indicators for evaluating retrospective performance of energy system models," Applied Energy, Elsevier, vol. 325(C).
    9. Moret, Stefano & Codina Gironès, Víctor & Bierlaire, Michel & Maréchal, François, 2017. "Characterization of input uncertainties in strategic energy planning models," Applied Energy, Elsevier, vol. 202(C), pages 597-617.
    10. Huntington, Hillard G., 2011. "Backcasting U.S. oil demand over a turbulent decade," Energy Policy, Elsevier, vol. 39(9), pages 5674-5680, September.
    11. Millard-Ball, Adam, 2013. "The trouble with voluntary emissions trading: Uncertainty and adverse selection in sectoral crediting programs☆☆Special thanks to Suzi Kerr, Lawrence Goulder, Michael Wara, Arthur van Benthem, Lee Sch," Journal of Environmental Economics and Management, Elsevier, vol. 65(1), pages 40-55.
    12. Wachtmeister, Henrik & Henke, Petter & Höök, Mikael, 2018. "Oil projections in retrospect: Revisions, accuracy and current uncertainty," Applied Energy, Elsevier, vol. 220(C), pages 138-153.

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    More about this item

    Keywords

    EIA; energy forecasting; bias;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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