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Forecasting electricity spot prices using time-series models with a double temporal segmentation

Author

Listed:
  • Marie Bessec

    (LEDA-CGEMP - Centre de Géopolitique de l’Energie et des Matières Premières - LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Julien Fouquau

    (ESCP Europe - Ecole Supérieure de Commerce de Paris)

  • Sophie Meritet

    (LEDA-CGEMP - Centre de Géopolitique de l’Energie et des Matières Premières - LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

The French wholesale market is set to expand in the next few years under European pressure and national decisions. In this article, we assess the forecasting ability of several classes of time-series models for electricity wholesale spot prices at a day-ahead horizon in France. Electricity spot prices display a strong seasonal pattern, particularly in France, given the high share of electric heating in housing during winter time. To deal with this pattern, we implement a double temporal segmentation of the data. For each trading period and season, we use a large number of specifications based on market fundamentals: linear regressions, Markov-switching (MS) models and threshold models with a smooth transition. An extensive evaluation on French data shows that modelling each season independently leads to better results. Among nonlinear models, MS models designed to capture the sudden and fast-reverting spikes in the price dynamics yield more accurate forecasts. Finally, pooling forecasts give more reliable results.

Suggested Citation

  • Marie Bessec & Julien Fouquau & Sophie Meritet, 2016. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Post-Print hal-01276807, HAL.
    • Handle: RePEc:hal:journl:hal-01276807
    DOI: 10.1080/00036846.2015.1080801
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    1. repec:ipg:wpaper:2014-518 is not listed on IDEAS
    2. repec:ipg:wpaper:2014-546 is not listed on IDEAS
    3. repec:ipg:wpaper:2014-535 is not listed on IDEAS
    4. repec:ipg:wpaper:2014-547 is not listed on IDEAS
    5. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    6. repec:ipg:wpaper:2014-514 is not listed on IDEAS
    7. repec:ipg:wpaper:2014-545 is not listed on IDEAS
    8. Hryshchuk, Antanina & Lessmann, Stefan, 2018. "Deregulated day-ahead electricity markets in Southeast Europe: Price forecasting and comparative structural analysis," IRTG 1792 Discussion Papers 2018-009, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
    10. repec:ipg:wpaper:2014-495 is not listed on IDEAS
    11. repec:ipg:wpaper:2014-481 is not listed on IDEAS
    12. repec:ipg:wpaper:2014-502 is not listed on IDEAS
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    14. repec:ipg:wpaper:2014-549 is not listed on IDEAS
    15. repec:ipg:wpaper:2014-486 is not listed on IDEAS
    16. Mohammad Nure Alam, 2021. "Accessing the Effect of Renewables on the Wholesale Power Market," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 341-360.
    17. repec:ipg:wpaper:2014-523 is not listed on IDEAS
    18. Caputo, Antonio C. & Federici, Alessandro & Pelagagge, Pacifico M. & Salini, Paolo, 2023. "Offshore wind power system economic evaluation framework under aleatory and epistemic uncertainty," Applied Energy, Elsevier, vol. 350(C).

    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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