author_facet Petkovic, Milena
Koch, Thorsten
Zittel, Janina
Petkovic, Milena
Koch, Thorsten
Zittel, Janina
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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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
author_sort petkovic, milena
container_issue 6
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container_title Energy Science & Engineering
container_volume 10
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