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Why did the historical energy forecasting succeed or fail? A case study on IEA's projection

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  • Liao, Hua
  • Cai, Jia-Wei
  • Yang, Dong-Wei
  • Wei, Yi-Ming

Abstract

Medium-to-long term energy prediction plays a widely-acknowledged role in guiding national energy strategy and policy but could also lead to serious economic and social chaos when poorly executed. A consequent issue may be the effectiveness of these predictions, and sources that errors can be traced back to. The International Energy Agency (IEA) has published its annual World Energy Outlook (WEO) concerning energy demand based on its long term world energy model (WEM) under specific assumptions towards uncertainties such as population, macroeconomy, energy price and technology. Unfortunately, some of its predictions succeeded while others failed. We in this paper attempt to decompose the leading source of these errors quantitatively. Results suggest that GDP acts as the leading source of demand forecasting errors while fuel price comes thereafter, which requires extra attention in forecasting. Gas, among all fuel types witness the most biased projections. Ignoring the catch-up effect of acquiring rapid economic growth in developing countries such as China will lead to huge mistake in predicting global energy demand. Finally, asymmetric cost of under- and over-estimation of GDP suggests a potentially less conservative stance in the future.

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  • Liao, Hua & Cai, Jia-Wei & Yang, Dong-Wei & Wei, Yi-Ming, 2016. "Why did the historical energy forecasting succeed or fail? A case study on IEA's projection," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 90-96.
    • Handle: RePEc:eee:tefoso:v:107:y:2016:i:c:p:90-96
    DOI: 10.1016/j.techfore.2016.03.026
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    1. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Forecasting the oil–gasoline price relationship: Do asymmetries help?," Energy Economics, Elsevier, vol. 46(S1), pages 44-56.
    2. Simoes, Sofia & Fortes, Patrícia & Seixas, Júlia & Huppes, Gjalt, 2015. "Assessing effects of exogenous assumptions in GHG emissions forecasts – a 2020 scenario study for Portugal using the Times energy technology model," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 221-235.
    3. Sanders, Dwight R. & Manfredo, Mark R. & Boris, Keith, 2008. "Accuracy and efficiency in the U.S. Department of Energy's short-term supply forecasts," Energy Economics, Elsevier, vol. 30(3), pages 1192-1207, May.
    4. Jonathan Koomey & Paul Craig & Ashok Gadgil & David Lorenzetti, 2003. "Improving Long-Range Energy Modeling: A Plea for Historical Retrospectives," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 75-92.
    5. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    6. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    7. Huntington, Hillard G., 2011. "Backcasting U.S. oil demand over a turbulent decade," Energy Policy, Elsevier, vol. 39(9), pages 5674-5680, September.
    8. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    9. 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.
    10. O'Neill, Brian C. & Desai, Mausami, 2005. "Accuracy of past projections of US energy consumption," Energy Policy, Elsevier, vol. 33(8), pages 979-993, May.
    11. Fischer, Carolyn & Herrnstadt, Evan & Morgenstern, Richard, 2009. "Understanding errors in EIA projections of energy demand," Resource and Energy Economics, Elsevier, vol. 31(3), pages 198-209, August.
    12. Linderoth, Hans, 2002. "Forecast errors in IEA-countries' energy consumption," Energy Policy, Elsevier, vol. 30(1), pages 53-61, January.
    13. Utgikar, V.P. & Scott, J.P., 2006. "Energy forecasting: Predictions, reality and analysis of causes of error," Energy Policy, Elsevier, vol. 34(17), pages 3087-3092, November.
    14. Baghestani, Hamid, 2015. "Predicting gasoline prices using Michigan survey data," Energy Economics, Elsevier, vol. 50(C), pages 27-32.
    15. Chang, Yusang & Lee, Jinsoo & Yoon, Hyerim, 2012. "Alternative projection of the world energy consumption-in comparison with the 2010 international energy outlook," Energy Policy, Elsevier, vol. 50(C), pages 154-160.
    16. Laitner, J. A. & DeCanio, S. J. & Koomey, J. G. & Sanstad, A. H., 2003. "Room for improvement: increasing the value of energy modeling for policy analysis," Utilities Policy, Elsevier, vol. 11(2), pages 87-94, June.
    17. Winebrake, James J. & Sakva, Denys, 2006. "An evaluation of errors in US energy forecasts: 1982-2003," Energy Policy, Elsevier, vol. 34(18), pages 3475-3483, December.
    18. Lady, George M., 2010. "Evaluating long term forecasts," Energy Economics, Elsevier, vol. 32(2), pages 450-457, March.
    19. Fye, Shannon R. & Charbonneau, Steven M. & Hay, Jason W. & Mullins, Carie A., 2013. "An examination of factors affecting accuracy in technology forecasts," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1222-1231.
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    6. 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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