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Prognosis of EPEX SPOT Electricity Prices Using Artificial Neural Networks

In: Operations Research Proceedings 2017

Author

Listed:
  • Johannes Hussak

    (Technical University Munich
    Siemens AG, Corporate Technology)

  • Stefanie Vogl

    (Siemens AG, Corporate Technology)

  • Ralph Grothmann

    (Siemens AG, Corporate Technology)

  • Merlind Weber

    (Technical University Munich)

Abstract

The rising shares of volatile renewable energy supply in the European electricity markets lead to an increased relevance for short-term trading at the EPEX SPOT. In this paper, we propose a holistic modeling approach for a robust prediction of EPEX SPOT day-ahead prices for the bidding zone Germany, Austria and Luxembourg applying three-layer and deep neural networks. In the first step, we describe, why neural networks are well suited for econometric modeling tasks. In the modeling part, we distinguish an optimal set of meta-parameters and then gradually adjust the final model setup. Lastly, we further improve the performance accuracy by applying a quantile-based scaling process. Thus, we obtain accurate and robust predictions even for sharp price peaks in rare events.

Suggested Citation

  • Johannes Hussak & Stefanie Vogl & Ralph Grothmann & Merlind Weber, 2018. "Prognosis of EPEX SPOT Electricity Prices Using Artificial Neural Networks," Operations Research Proceedings, in: Natalia Kliewer & Jan Fabian Ehmke & Ralf Borndörfer (ed.), Operations Research Proceedings 2017, pages 89-94, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-89920-6_13
    DOI: 10.1007/978-3-319-89920-6_13
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