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Benchmarking time series based forecasting models for electricity balancing market prices

Author

Listed:
  • Gro Klaeboe

    (Norwegian University of Science and Technology)

  • Anders Lund Eriksrud

    (Norwegian University of Science and Technology)

  • Stein-Erik Fleten

    (Norwegian University of Science and Technology)

Abstract

In the trade-off between bidding in the day-ahead electricity market and the real time balancing market, producers need good forecasts for balancing market prices to make informed decisions. A range of earlier published models for forecasting of balancing market prices, including a few extensions, is benchmarked. The models are benchmarked both for one hour-ahead and day-ahead forecast, and both point and interval forecasts are compared. None of the benchmarked models produce informative day-ahead point forecasts, suggesting that information available before the closing of the day-ahead market is effciently reflected in the day-ahead market price rather than the balancing market price. Evaluation of the interval forecasts reveals that models without balancing state information overestimate variance, making them unsuitable for scenario generation.

Suggested Citation

  • Gro Klaeboe & Anders Lund Eriksrud & Stein-Erik Fleten, 2013. "Benchmarking time series based forecasting models for electricity balancing market prices," Working Papers 2013-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    • Handle: RePEc:gwc:wpaper:2013-006
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    File URL: https://www2.gwu.edu/~forcpgm/2013-006.pdf
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    References listed on IDEAS

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    Cited by:

    1. Litjens, G.B.M.A. & Worrell, E. & van Sark, W.G.J.H.M., 2018. "Economic benefits of combining self-consumption enhancement with frequency restoration reserves provision by photovoltaic-battery systems," Applied Energy, Elsevier, vol. 223(C), pages 172-187.
    2. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    3. Boomsma, Trine Krogh & Juul, Nina & Fleten, Stein-Erik, 2014. "Bidding in sequential electricity markets: The Nordic case," European Journal of Operational Research, Elsevier, vol. 238(3), pages 797-809.
    4. Kiesel, Rüdiger & Paraschiv, Florentina, 2017. "Econometric analysis of 15-minute intraday electricity prices," Energy Economics, Elsevier, vol. 64(C), pages 77-90.
    5. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.

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    More about this item

    Keywords

    Federal Reserve; Forecast Evaluation; Survey of Professional Forecasts; Business Cycle; Mahalanobis Distance;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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