IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1906.06248.html
   My bibliography  Save this paper

Machine Learning on EPEX Order Books: Insights and Forecasts

Author

Listed:
  • Simon Schnurch
  • 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 demand. Appropriate feature extraction for the order book data is developed. Using cross-validation to optimise hyperparameters, neural networks and random forests are proposed and compared to statistical reference models. The machine learning models outperform traditional approaches.

Suggested Citation

  • Simon Schnurch & Andreas Wagner, 2019. "Machine Learning on EPEX Order Books: Insights and Forecasts," Papers 1906.06248, arXiv.org, revised Sep 2019.
  • Handle: RePEc:arx:papers:1906.06248
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1906.06248
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Ziel, Florian & Weron, RafaƂ, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grzegorz Marcjasz, 2020. "Forecasting Electricity Prices Using Deep Neural Networks: A Robust Hyper-Parameter Selection Scheme," Energies, MDPI, Open Access Journal, vol. 13(18), pages 1-18, September.
    2. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1906.06248. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.