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Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression

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  • Tryggvi Jónsson

    (Department of Applied Mathematics, Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark
    ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark)

  • Pierre Pinson

    (Department of Electrical Engineering, Technical University of Denmark, Elektrovej 325, 2800 Kgs. Lyngby, Denmark)

  • Henrik Madsen

    (Department of Applied Mathematics, Technical University of Denmark, Matematiktorvet 303, 2800 Kgs. Lyngby, Denmark)

  • Henrik Aalborg Nielsen

    (ENFOR A/S, Lyngsø Allé 3, 2970 Hørsholm, Denmark)

Abstract

A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for such prices, then enhanced with a temporal dependence structure. A semi-parametric methodology for generating such densities is presented: it includes: (i) a time-adaptive quantile regression model for the 5%–95% quantiles; and (ii) a description of the distribution tails with exponential distributions. The forecasting skill of the proposed model is compared to that of four benchmark approaches and the well-known the generalist autoregressive conditional heteroskedasticity (GARCH) model over a three-year evaluation period. While all benchmarks are outperformed in terms of forecasting skill overall, the superiority of the semi-parametric model over the GARCH model lies in the former’s ability to generate reliable quantile estimates.

Suggested Citation

  • Tryggvi Jónsson & Pierre Pinson & Henrik Madsen & Henrik Aalborg Nielsen, 2014. "Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression," Energies, MDPI, vol. 7(9), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:9:p:5523-5547:d:39593
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    References listed on IDEAS

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    3. 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.
    4. Brenda López Cabrera & Franziska Schulz, 2016. "Time-Adaptive Probabilistic Forecasts of Electricity Spot Prices with Application to Risk Management," SFB 649 Discussion Papers SFB649DP2016-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Ekaterina Abramova & Derek Bunn, 2020. "Forecasting the Intra-Day Spread Densities of Electricity Prices," Energies, MDPI, vol. 13(3), pages 1-31, February.
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    7. 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.
    8. Smith, Michael Stanley & Shively, Thomas S., 2018. "Econometric modeling of regional electricity spot prices in the Australian market," Energy Economics, Elsevier, vol. 74(C), pages 886-903.
    9. Christopher Koch & Philipp Maskos, 2020. "Passive Balancing Through Intraday Trading: Whether Interactions Between Short-term Trading and Balancing Stabilize Germany s Electricity System," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 101-112.
    10. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    11. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado, 2015. "Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market," Energies, MDPI, vol. 8(9), pages 1-23, September.
    12. Ekaterina Abramova & Derek Bunn, 2020. "Forecasting the Intra-Day Spread Densities of Electricity Prices," Papers 2002.10566, arXiv.org.
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    14. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
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    17. Derek Bunn, Arne Andresen, Dipeng Chen, Sjur Westgaard, 2016. "Analysis and Forecasting of Electricty Price Risks with Quantile Factor Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    18. Gunnhildur H. Steinbakk & Alex Lenkoski & Ragnar Bang Huseby & Anders L{o}land & Tor Arne {O}ig{aa}rd, 2018. "Using published bid/ask curves to error dress spot electricity price forecasts," Papers 1812.02433, arXiv.org.
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    20. Cramer, Eike & Witthaut, Dirk & Mitsos, Alexander & Dahmen, Manuel, 2023. "Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 346(C).

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