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An Alternative to Index-Based Gas Sourcing Using Neural Networks

Author

Listed:
  • Stephan Schlüter

    (Department of Mathematics, Natural and Economic Sciences, Ulm University of Applied Scienes, 89075 Ulm, Germany
    These authors contributed equally to this work.)

  • Sejung Jung

    (Department of Convergence and Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea
    These authors contributed equally to this work.)

  • Andreas von Döllen

    (Wintershall Dea GmbH, 34119 Kassel, Germany
    These authors contributed equally to this work.)

  • Wonhee Lee

    (Department of Convergence and Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea)

Abstract

An index on the gas market commonly refers to the average price of a certain trading product, e.g., over the period of one month. Index-based sourcing is a widely-used habit in modern gas business. Risks are reduced by averaging prices over the purchasing period. Due to the significant volume, there have been many attempts to ”beat the index”, i.e., to design a strategy that, over time, offers cheaper prices than the index. Here, we use neural networks to identify n , n ∈ N , optimal shopping points. Both classification- and forecasting-based strategies are tested to decide on each trading day if gas should be purchased or not. Thereby, we use the Front Month index based on prices from the Dutch Title Transfer Facility as an example. Regarding cumulative performance, all but a very simple myopic algorithm are able to outperform the index. However, each strategy has its flaws and some positive results are due to the price increase during 2021. If one opts for an active sourcing strategy, then a forecasting-based approach is the best choice.

Suggested Citation

  • Stephan Schlüter & Sejung Jung & Andreas von Döllen & Wonhee Lee, 2022. "An Alternative to Index-Based Gas Sourcing Using Neural Networks," Energies, MDPI, vol. 15(13), pages 1-11, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4708-:d:849005
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    References listed on IDEAS

    as
    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    3. Jonathan Berrisch & Florian Ziel, 2022. "Distributional modeling and forecasting of natural gas prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1065-1086, September.
    4. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).
    5. Thompson, Matt, 2016. "Natural gas storage valuation, optimization, market and credit risk management," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 26-44.
    6. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    7. Tim Hill & Marcus O'Connor & William Remus, 1996. "Neural Network Models for Time Series Forecasts," Management Science, INFORMS, vol. 42(7), pages 1082-1092, July.
    8. Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021. "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, vol. 14(11), pages 1-21, May.
    9. Hulshof, Daan & van der Maat, Jan-Pieter & Mulder, Machiel, 2016. "Market fundamentals, competition and natural-gas prices," Energy Policy, Elsevier, vol. 94(C), pages 480-491.
    10. repec:cdl:anderf:qt43n1k4jb is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

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