IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v32y2010i2p450-457.html
   My bibliography  Save this article

Evaluating long term forecasts

Author

Listed:
  • Lady, George M.

Abstract

The U.S. Department of Energy's Energy Information Administration (EIA), and its predecessor organizations, has published projections of U.S. energy production, consumption, distribution and prices annually for over 30Â years. A natural issue to raise in evaluating the projections is an assessment of their accuracy compared to eventual outcomes. A related issue is the determination of the sources of "error" in the projections that are due to differences between the actual versus realized values of the associated assumptions. One way to do this would be to run the computer-based model from which the projections are derived at the time the projected values are realized, using actual rather than assumed values for model assumptions; and, compare these results to the original projections. For long term forecasts, this approach would require that the model's software and hardware configuration be archived and available for many years, possibly decades, into the future. Such archival creates many practical problems; and, in general, it is not being done. This paper reports on an alternative approach for evaluating the projections. In the alternative approach, the model is run many times for cases in which important assumptions are changed individually and in combinations. A database is assembled from the solutions and a regression analysis is conducted for each important projected variable with the associated assumptions chosen as exogenous variables. When actual data are eventually available, the regression results are then used to estimate the sources of the differences in the projections of the endogenous variables compared to their eventual outcomes. The results presented here are for residential and commercial sector natural gas and electricity consumption.

Suggested Citation

  • Lady, George M., 2010. "Evaluating long term forecasts," Energy Economics, Elsevier, vol. 32(2), pages 450-457, March.
    • Handle: RePEc:eee:eneeco:v:32:y:2010:i:2:p:450-457
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140-9883(09)00189-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. James M. Griffin, 1977. "Long-Run Production Modeling with Pseudo Data: Electric Power Generation," Bell Journal of Economics, The RAND Corporation, vol. 8(1), pages 112-127, Spring.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2022. "Asymmetry and Interdependence when Evaluating U.S. Energy Information Agency Forecasts," MPRA Paper 114325, University Library of Munich, Germany.
    4. 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.
    5. Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2023. "Asymmetry and interdependence when evaluating U.S. Energy Information Administration forecasts," Energy Economics, Elsevier, vol. 121(C).
    6. Lin, Boqiang & Wu, Ya & Zhang, Li, 2011. "Estimates of the potential for energy conservation in the Chinese steel industry," Energy Policy, Elsevier, vol. 39(6), pages 3680-3689, June.
    7. Rentizelas, Athanasios & Georgakellos, Dimitrios, 2014. "Incorporating life cycle external cost in optimization of the electricity generation mix," Energy Policy, Elsevier, vol. 65(C), pages 134-149.
    8. Hal T. Nelson & David von Hippel & Tom Peterson & Roman Garagulagian, 2016. "The Great Recession or progressive energy policies? Explaining the decline in US greenhouse gas emissions forecasts," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 59(3), pages 480-500, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Olivier Massol, 2011. "A Cost Function for the Natural Gas Transmission Industry: Further Considerations," The Engineering Economist, Taylor & Francis Journals, vol. 56(2), pages 95-122.
    2. 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.
    3. O' Mahony, Tadhg & Zhou, P. & Sweeney, John, 2013. "Integrated scenarios of energy-related CO2 emissions in Ireland: A multi-sectoral analysis to 2020," Ecological Economics, Elsevier, vol. 93(C), pages 385-397.
    4. J. Andrew Kelly & Herman R.J. Vollebergh, 2012. "Adaptive Policy Mechanisms for Transboundary Air Pollution Regulation: Reasons and Recommendations," Working Papers 2012.32, Fondazione Eni Enrico Mattei.
    5. Abada, Ibrahim & Briat, Vincent & Massol, Olivier, 2013. "Construction of a fuel demand function portraying interfuel substitution, a system dynamics approach," Energy, Elsevier, vol. 49(C), pages 240-251.
    6. Mazen Labban, 2012. "Preempting Possibility: Critical Assessment of the IEA's World Energy Outlook 2010," Development and Change, International Institute of Social Studies, vol. 43(1), pages 375-393, January.
    7. 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.
    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. Kialashaki, Arash & Reisel, John R., 2014. "Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States," Energy, Elsevier, vol. 76(C), pages 749-760.
    10. G. Thomas Sav, 1987. "Tax Incentives for Innovative Energy Sources: Extensions of E-K Complementarity," Public Finance Review, , vol. 15(4), pages 417-427, October.
    11. Strachan, Neil & Pye, Steve & Kannan, Ramachandran, 2009. "The iterative contribution and relevance of modelling to UK energy policy," Energy Policy, Elsevier, vol. 37(3), pages 850-860, March.
    12. Garratt, Anthony & Petrella, Ivan & Zhang, Yunyi, 2023. "Asymmetry and interdependence when evaluating U.S. Energy Information Administration forecasts," Energy Economics, Elsevier, vol. 121(C).
    13. repec:cty:dpaper:11/03 is not listed on IDEAS
    14. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2023. "Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning," Applied Energy, Elsevier, vol. 340(C).
    15. 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.
    16. Huntington, Hillard G., 2011. "Backcasting U.S. oil demand over a turbulent decade," Energy Policy, Elsevier, vol. 39(9), pages 5674-5680, September.
    17. Scheer, Dirk, 2017. "Communicating energy system modelling to the wider public: An analysis of German media coverage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1389-1398.
    18. 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.
    19. al Irsyad, Muhammad Indra & Halog, Anthony & Nepal, Rabindra, 2019. "Renewable energy projections for climate change mitigation: An analysis of uncertainty and errors," Renewable Energy, Elsevier, vol. 130(C), pages 536-546.
    20. Liu, Xiaoyu & Cui, Qingbin, 2018. "Value of performance baseline in voluntary carbon trading under uncertainty," Energy, Elsevier, vol. 145(C), pages 468-476.
    21. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:32:y:2010:i:2:p:450-457. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.