A hybrid approach for high precision prediction of gas flows

Milena Petkovic*, Ying Chen, Inken Gamrath, Uwe Gotzes, Natalia Selini Hadjidimitrou, Janina Zittel, Xiaofei Xu, Thorsten Koch

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

About 23% of the German energy demand is supplied by natural gas. Additionally, for about the same amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by transmissions system operators (TSOs). The number one priority of the TSOs is to ensure the security of supply. However, the TSOs have only very limited knowledge about the intentions and planned actions of the shippers (traders). Open Grid Europe (OGE), one of Germany’s largest TSO, operates a high-pressure transport network of about 12,000 km length. With the introduction of peak-load gas power stations, it is of great importance to predict in- and out-flow of the network to ensure the necessary flexibility and security of supply for the German Energy Transition (“Energiewende”). In this paper, we introduce a novel hybrid forecast method applied to gas flows at the boundary nodes of a transport network. This method employs an optimized feature selection and minimization. We use a combination of a FAR, LSTM and mathematical programming to achieve robust high-quality forecasts on real-world data for different types of network nodes.

Original languageEnglish
JournalEnergy Systems
DOIs
Publication statusAccepted/In press - 2021
Externally publishedYes

Keywords

  • FAR
  • Gas forecast
  • Hybrid method
  • LSTM
  • Mathematical optimisation
  • Time series

ASJC Scopus subject areas

  • Modelling and Simulation
  • Economics and Econometrics
  • Energy(all)

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