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Metropolitan Econometric Electric Utility Forecast Accuracy

Author

Listed:
  • Thomas M. Fullerton

    (Department of Economics & Finance, University of Texas at El Paso, El Paso, TX 79968-0543, USA,)

  • George Novela

    (Load Forecast and Planning Unit, El Paso Electric Company, P.O. Box 982, El Paso, TX 79960 USA,)

  • David Torres

    (Department of Research and Planning, El Paso Water Utilities, 1154 Hawkins Boulevard, El Paso, TX 79925, USA,)

  • Adam G. Walke

    (Department of Economics & Finance, University of Texas at El Paso, El Paso, TX 79968-0543, USA.)

Abstract

El Paso Electric Company (EPEC) is the sole commercial electricity provider for two metropolitan economies in the southwestern desert region of the United States: El Paso, Texas and Las Cruces, New Mexico. A publicly traded corporation, EPEC employs a structural econometric system of equations model to forecast energy sales for various customer classes. Although the modeling system has provided reliable inputs to annual corporate planning efforts at EPEC, its historical track record has not previously been formally assessed for forecast accuracy. Both descriptive and inferential statistics are used to evaluate the EPEC model s forecasting performance. Results indicate that accurate prediction of electricity usage in this service area is an elusive target. Those results are similar to what has been documented for other regional economic variables.

Suggested Citation

  • Thomas M. Fullerton & George Novela & David Torres & Adam G. Walke, 2015. "Metropolitan Econometric Electric Utility Forecast Accuracy," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 738-745.
    • Handle: RePEc:eco:journ2:2015-03-13
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    References listed on IDEAS

    as
    1. Contreras, Sergio & Smith, Wm. Doyle & Roth, Timothy P. & Fullerton, Thomas M., Jr., 2009. "Regional Evidence regarding U.S. Residential Electricity Consumption," MPRA Paper 29093, University Library of Munich, Germany, revised 2009.
    2. Athukorala, P.P.A Wasantha & Wilson, Clevo, 2010. "Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka," Energy Economics, Elsevier, vol. 32(Supplemen), pages 34-40, September.
    3. Bogetic, Zeljko & Fedderke, Johannes W., 2006. "Forecasting investment needs in South Africa's electricity and telecommunications sectors," Policy Research Working Paper Series 3829, The World Bank.
    4. Badri, Masood A., 1992. "Analysis of demand for electricity in the United States," Energy, Elsevier, vol. 17(7), pages 725-733.
    5. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    6. Chern, Wen S. & Just, Richard E., 1980. "Regional analysis of electricity demand growth," Energy, Elsevier, vol. 5(1), pages 35-46.
    7. Arsenault, E. & Bernard, J. -T. & Carr, C. W. & Genest-Laplante, E., 1995. "A total energy demand model of Quebec : Forecasting properties," Energy Economics, Elsevier, vol. 17(2), pages 163-171, April.
    8. Brown, Richard E. & Koomey, Jonathan G., 2003. "Electricity use in California: past trends and present usage patterns," Energy Policy, Elsevier, vol. 31(9), pages 849-864, July.
    9. żeljko Bogetić & Johannes W. Fedderke, 2006. "Forecasting Investment Needs In South Africa'S Electricity And Telecom Sectors," South African Journal of Economics, Economic Society of South Africa, vol. 74(3), pages 557-574, September.
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    Cited by:

    1. Iswan Iswan & Iwa Garniwa & Isti Surjandari, 2021. "Data Optimization on the Accuracy of Forecasting Electricity Energy Sales Using Principal Component Analysis Based on Spatial," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 215-220.
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    3. F. C. Susila Adiyanta, 2020. "Urban Space Governance and Sustainable Green Development in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 1-6.

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    More about this item

    Keywords

    Energy Forecasting; Statistical Tests; Forecast Accuracy Evaluation;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

    Statistics

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