A hybrid approach for high precision prediction of gas flows
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...
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| Veröffentlicht in: | Energy systems (Berlin. Periodical) 2022-05, Vol.13 (2), p.383-408 |
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| creator | Petkovic, Milena Chen, Ying Gamrath, Inken Gotzes, Uwe Hadjidimitrou, Natalia Selini Zittel, Janina Xu, Xiaofei Koch, Thorsten |
| description | 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. |
| doi_str_mv | 10.1007/s12667-021-00466-4 |
| format | Article |
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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.</description><identifier>ISSN: 1868-3975</identifier><identifier>ISSN: 1868-3967</identifier><identifier>EISSN: 1868-3975</identifier><identifier>DOI: 10.1007/s12667-021-00466-4</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer</publisher><subject>Economics and Management ; Energy ; Energy demand ; Energy Policy ; Energy Systems ; FAR ; Gas flow ; Gas forecast ; Hybrid method ; LSTM ; Mathematical optimisation ; Mathematical programming ; Natural gas ; Nodes ; Operations Research/Decision Theory ; Operators (mathematics) ; Optimization ; Original Paper ; Peak load ; Power plants ; Robustness (mathematics) ; Security ; Time series ; Transportation networks</subject><ispartof>Energy systems (Berlin. 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| identifier | ISSN: 1868-3975 |
| ispartof | Energy systems (Berlin. Periodical), 2022-05, Vol.13 (2), p.383-408 |
| issn | 1868-3975 1868-3967 1868-3975 |
| language | eng |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Economics and Management Energy Energy demand Energy Policy Energy Systems FAR Gas flow Gas forecast Hybrid method LSTM Mathematical optimisation Mathematical programming Natural gas Nodes Operations Research/Decision Theory Operators (mathematics) Optimization Original Paper Peak load Power plants Robustness (mathematics) Security Time series Transportation networks |
| title | A hybrid approach for high precision prediction of gas flows |
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