An Alternative to Index-Based Gas Sourcing Using Neural Networks

Stephan Schlüter, Sejung Jung, Andreas von Döllen and Wonhee Lee
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Stephan Schlüter: Department of Mathematics, Natural and Economic Sciences, Ulm University of Applied Scienes, 89075 Ulm, Germany
Sejung Jung: Department of Convergence and Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea
Andreas von Döllen: Wintershall Dea GmbH, 34119 Kassel, Germany
Wonhee Lee: Department of Convergence and Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea

Energies, 2022, vol. 15, issue 13, 1-11

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.

Keywords: neural networks; gas trading algorithm; classification; forecasting; TTF prices (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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