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Short-term electricity price forecastingmodels comparative analysis : Machine Learning vs. Econometrics

Author

Listed:
  • Antoine Ferré

    (IFPEN - IFP Energies nouvelles, IFP School)

  • Guillaume de Certaines

    (IFPEN - IFP Energies nouvelles, IFP School)

  • Jérôme Cazelles

    (IFPEN - IFP Energies nouvelles, IFP School)

  • Tancrède Cohet

    (IFPEN - IFP Energies nouvelles, IFP School)

  • Arash Farnoosh

    (IFPEN - IFP Energies nouvelles, IFP School)

  • Frédéric Lantz

    (IFPEN - IFP Energies nouvelles, IFP School)

Abstract

This paper gives an overview of several models applied to forecast the day-ahead prices of the German electricity market between 2014 and 2015 using hourly wind and solar productions as well as load. Four econometric models were built: SARIMA, SARIMAX, Holt-Winters and Monte Carlo Markov Chain Switching Regimes. Two machine learning approaches were also studied: a Gaussian mixture classification coupled with a random forest and finally, an LSTM algorithm. The best performances were obtained using the SARIMAX and LSTM models. The SARIMAX model makes good predictions and has the advantage through its explanatory variables to better capture the price volatility. The addition of other explanatory variables could improve the prediction of the models presented. The RF exhibits good results and allows to build a confidence interval. The LSTM model provides excellent results, but the precise understanding of the functioning of this model is much more complex.

Suggested Citation

  • Antoine Ferré & Guillaume de Certaines & Jérôme Cazelles & Tancrède Cohet & Arash Farnoosh & Frédéric Lantz, 2021. "Short-term electricity price forecastingmodels comparative analysis : Machine Learning vs. Econometrics," Working Papers hal-03262208, HAL.
    • Handle: RePEc:hal:wpaper:hal-03262208
    Note: View the original document on HAL open archive server: https://ifp.hal.science/hal-03262208
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    References listed on IDEAS

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    Cited by:

    1. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    2. Diankai Wang & Inna Gryshova & Mykola Kyzym & Tetiana Salashenko & Viktoriia Khaustova & Maryna Shcherbata, 2022. "Electricity Price Instability over Time: Time Series Analysis and Forecasting," Sustainability, MDPI, vol. 14(15), pages 1-24, July.

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    Keywords

    Energy Markets; Renewable Energy; Econometric modelling; Bootstrap Method; Merit-Order effect;
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