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Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case

Author

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  • Emma Viviani

    (Department of Computer Science, College of Mathematics, University of Verona, 37134 Verona, Italy
    These authors contributed equally to this work.)

  • Luca Di Persio

    (Department of Computer Science, College of Mathematics, University of Verona, 37134 Verona, Italy
    These authors contributed equally to this work.)

  • Matthias Ehrhardt

    (Department of Applied Mathematics and Numerical Analysis, University of Wuppertal, 42119 Wuppertal, Germany
    These authors contributed equally to this work.)

Abstract

In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.

Suggested Citation

  • Emma Viviani & Luca Di Persio & Matthias Ehrhardt, 2021. "Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case," Energies, MDPI, vol. 14(2), pages 1-33, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:364-:d:478401
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    References listed on IDEAS

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    Cited by:

    1. Miguel A. Jaramillo-Morán & Daniel Fernández-Martínez & Agustín García-García & Diego Carmona-Fernández, 2021. "Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study," Energies, MDPI, vol. 14(23), pages 1-23, November.
    2. Yuriy Bilan & Serhiy Kozmenko & Alex Plastun, 2022. "Price Forecasting in Energy Market," Energies, MDPI, vol. 15(24), pages 1-6, December.

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