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A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market

Author

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  • Derek W. Bunn

    (Management Science and Operations, London Business School, London NW1 4SA, UK)

  • Angelica Gianfreda

    (Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano 39100, Italy)

  • Stefan Kermer

    (Institute of Energy Systems and Electrical Drives-Energy Economics Group, Technical University of Vienna, Vienna 1040, Austria)

Abstract

This paper applies a multi-factor, stochastic latent moment model to predicting the imbalance volumes in the Austrian zone of the German/Austrian electricity market. This provides a density forecast whose shape is determined by the flexible skew-t distribution, the first three moments of which are estimated as linear functions of lagged imbalance and forecast errors for load, wind and solar production. The evaluation of this density predictor is compared to an expected value obtained from OLS regression model, using the same regressors, through an out-of-sample backtest of a flexible generator seeking to optimize its imbalance positions on the intraday market. This research contributes to forecasting methodology and imbalance prediction, and most significantly it provides a case study in the evaluation of density forecasts through decision-making performance. The main finding is that the use of the density forecasts substantially increased trading profitability and reduced risk compared to the more conventional use of mean value regressions.

Suggested Citation

  • Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2658-:d:173889
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    References listed on IDEAS

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    Cited by:

    1. Grzegorz Marcjasz & Bartosz Uniejewski & Rafał Weron, 2020. "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts," Energies, MDPI, vol. 13(7), pages 1-16, April.
    2. Arne Vogler & Florian Ziel, "undated". "On The Evaluation Of Binary Event Probability Predictions In Electricity Price Forecasting," EWL Working Papers 1911, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
    3. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    4. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    5. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    6. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado, 2020. "Predictive Trading Strategy for Physical Electricity Futures," Energies, MDPI, vol. 13(14), pages 1-24, July.
    7. Anna Brdulak & Grażyna Chaberek & Jacek Jagodziński, 2020. "Determination of Electricity Demand by Personal Light Electric Vehicles (PLEVs): An Example of e-Motor Scooters in the Context of Large City Management in Poland," Energies, MDPI, vol. 13(1), pages 1-18, January.
    8. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    9. Sinan Deng & John Inekwe & Vladimir Smirnov & Andrew Wait & Chao Wang, 2023. "Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices," Working Papers 2023-03, University of Sydney, School of Economics.
    10. Simon Hirsch & Florian Ziel, 2022. "Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution," Papers 2211.13002, arXiv.org.
    11. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits," Energies, MDPI, vol. 12(4), pages 1-15, February.
    12. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    13. Lago, Jesus & Poplavskaya, Ksenia & Suryanarayana, Gowri & De Schutter, Bart, 2021. "A market framework for grid balancing support through imbalances trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    14. Štefan Bojnec & Alan Križaj, 2021. "Electricity Markets during the Liberalization: The Case of a European Union Country," Energies, MDPI, vol. 14(14), pages 1-21, July.
    15. Micha{l} Narajewski, 2022. "Probabilistic forecasting of German electricity imbalance prices," Papers 2205.11439, arXiv.org.
    16. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    17. Mihail Busu, 2020. "Analyzing the Impact of the Renewable Energy Sources on Economic Growth at the EU Level Using an ARDL Model," Mathematics, MDPI, vol. 8(8), pages 1-18, August.

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