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Title: A hybrid approach for high precision prediction of gas flows
Authors: Petkovic, Milena
Chen, Ying
Gamrath, Inken
Gotzes, Uwe
Hadjidimitrou, Natalia Selini
Zittel, Janina
Xu, Xiaofei 
Koch, Thorsten
Keywords: FAR
Gas forecast
Hybrid method
Mathematical optimisation
Time series
Issue Date: 12-Aug-2021
Publisher: Springer Science and Business Media Deutschland GmbH
Citation: Petkovic, Milena, Chen, Ying, Gamrath, Inken, Gotzes, Uwe, Hadjidimitrou, Natalia Selini, Zittel, Janina, Xu, Xiaofei, Koch, Thorsten (2021-08-12). A hybrid approach for high precision prediction of gas flows. Energy Systems. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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. © 2021, The Author(s).
Source Title: Energy Systems
ISSN: 1868-3967
DOI: 10.1007/s12667-021-00466-4
Rights: Attribution 4.0 International
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