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Electricity Price Forecasting with Neural Networks on EPEX Order Books

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  • Simon Schnürch
  • Andreas Wagner

Abstract

This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected total demand. Appropriate feature extraction for the order book data is developed proceeding from existing literature. Using cross-validation to optimize hyperparameters, neural networks and random forests are fit to the data. Their in-sample and out-of-sample performance is compared to statistical reference models. The machine learning models outperform traditional approaches.

Suggested Citation

  • Simon Schnürch & Andreas Wagner, 2020. "Electricity Price Forecasting with Neural Networks on EPEX Order Books," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(3), pages 189-206, May.
    • Handle: RePEc:taf:apmtfi:v:27:y:2020:i:3:p:189-206
    DOI: 10.1080/1350486X.2020.1805337
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    Cited by:

    1. Wagner, Andreas & Ramentol, Enislay & Schirra, Florian & Michaeli, Hendrik, 2022. "Short- and long-term forecasting of electricity prices using embedding of calendar information in neural networks," Journal of Commodity Markets, Elsevier, vol. 28(C).
    2. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Fawaz Alassery & Habib Hamam & Omar Cheikhrouhou, 2021. "A Novel Machine Learning-Based Price Forecasting for Energy Management Systems," Sustainability, MDPI, vol. 13(22), pages 1-26, November.
    3. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    4. Adela Bâra & Simona-Vasilica Oprea & Bogdan George Tudorică, 2024. "From the East-European Regional Day-Ahead Markets to a Global Electricity Market," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2525-2557, June.

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