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Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks
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Zeitschriftentitel: | Energy Science & Engineering |
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Personen und Körperschaften: | , , |
In: | Energy Science & Engineering, 10, 2022, 6, S. 1812-1825 |
Format: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Wiley
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Schlagwörter: |
author_facet |
Petkovic, Milena Koch, Thorsten Zittel, Janina Petkovic, Milena Koch, Thorsten Zittel, Janina |
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author |
Petkovic, Milena Koch, Thorsten Zittel, Janina |
spellingShingle |
Petkovic, Milena Koch, Thorsten Zittel, Janina Energy Science & Engineering Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks General Energy Safety, Risk, Reliability and Quality |
author_sort |
petkovic, milena |
spelling |
Petkovic, Milena Koch, Thorsten Zittel, Janina 2050-0505 2050-0505 Wiley General Energy Safety, Risk, Reliability and Quality http://dx.doi.org/10.1002/ese3.932 <jats:title>Abstract</jats:title><jats:p>Germany is the largest market for natural gas in the European Union, with an annual consumption of approx. 95 billion cubic meters. The German high‐pressure gas pipeline network's length is roughly 40 000 km, which enables highly fluctuating quantities of gas to be transported safely over long distances. Considering that similar amounts of gas are also transshipped through Germany to other EU states, it is clear that Germany's gas transport system is essential to the European energy supply. Since the average velocity of gas in a pipeline is only 25 km/h, an adequate high‐precision, high‐frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission network. We propose a deep learning model based on spatio‐temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high‐pressure transmission network. Experiments show that our model effectively captures comprehensive spatio‐temporal correlations through modeling gas networks and consistently outperforms state‐of‐the‐art benchmarks on real‐world data sets by at least 15%. The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness.</jats:p> Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks Energy Science & Engineering |
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Energy Science & Engineering |
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title |
Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_unstemmed |
Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_full |
Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_fullStr |
Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_full_unstemmed |
Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_short |
Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_sort |
deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
topic |
General Energy Safety, Risk, Reliability and Quality |
url |
http://dx.doi.org/10.1002/ese3.932 |
publishDate |
2022 |
physical |
1812-1825 |
description |
<jats:title>Abstract</jats:title><jats:p>Germany is the largest market for natural gas in the European Union, with an annual consumption of approx. 95 billion cubic meters. The German high‐pressure gas pipeline network's length is roughly 40 000 km, which enables highly fluctuating quantities of gas to be transported safely over long distances. Considering that similar amounts of gas are also transshipped through Germany to other EU states, it is clear that Germany's gas transport system is essential to the European energy supply. Since the average velocity of gas in a pipeline is only 25 km/h, an adequate high‐precision, high‐frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission network. We propose a deep learning model based on spatio‐temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high‐pressure transmission network. Experiments show that our model effectively captures comprehensive spatio‐temporal correlations through modeling gas networks and consistently outperforms state‐of‐the‐art benchmarks on real‐world data sets by at least 15%. The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness.</jats:p> |
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author | Petkovic, Milena, Koch, Thorsten, Zittel, Janina |
author_facet | Petkovic, Milena, Koch, Thorsten, Zittel, Janina, Petkovic, Milena, Koch, Thorsten, Zittel, Janina |
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container_issue | 6 |
container_start_page | 1812 |
container_title | Energy Science & Engineering |
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description | <jats:title>Abstract</jats:title><jats:p>Germany is the largest market for natural gas in the European Union, with an annual consumption of approx. 95 billion cubic meters. The German high‐pressure gas pipeline network's length is roughly 40 000 km, which enables highly fluctuating quantities of gas to be transported safely over long distances. Considering that similar amounts of gas are also transshipped through Germany to other EU states, it is clear that Germany's gas transport system is essential to the European energy supply. Since the average velocity of gas in a pipeline is only 25 km/h, an adequate high‐precision, high‐frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission network. We propose a deep learning model based on spatio‐temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high‐pressure transmission network. Experiments show that our model effectively captures comprehensive spatio‐temporal correlations through modeling gas networks and consistently outperforms state‐of‐the‐art benchmarks on real‐world data sets by at least 15%. The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness.</jats:p> |
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spelling | Petkovic, Milena Koch, Thorsten Zittel, Janina 2050-0505 2050-0505 Wiley General Energy Safety, Risk, Reliability and Quality http://dx.doi.org/10.1002/ese3.932 <jats:title>Abstract</jats:title><jats:p>Germany is the largest market for natural gas in the European Union, with an annual consumption of approx. 95 billion cubic meters. The German high‐pressure gas pipeline network's length is roughly 40 000 km, which enables highly fluctuating quantities of gas to be transported safely over long distances. Considering that similar amounts of gas are also transshipped through Germany to other EU states, it is clear that Germany's gas transport system is essential to the European energy supply. Since the average velocity of gas in a pipeline is only 25 km/h, an adequate high‐precision, high‐frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission network. We propose a deep learning model based on spatio‐temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high‐pressure transmission network. Experiments show that our model effectively captures comprehensive spatio‐temporal correlations through modeling gas networks and consistently outperforms state‐of‐the‐art benchmarks on real‐world data sets by at least 15%. The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness.</jats:p> Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks Energy Science & Engineering |
spellingShingle | Petkovic, Milena, Koch, Thorsten, Zittel, Janina, Energy Science & Engineering, Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks, General Energy, Safety, Risk, Reliability and Quality |
title | Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_full | Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_fullStr | Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_full_unstemmed | Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_short | Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_sort | deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
title_unstemmed | Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks |
topic | General Energy, Safety, Risk, Reliability and Quality |
url | http://dx.doi.org/10.1002/ese3.932 |