Modelling Flow in Gas Transmission Networks Using Shape-Constrained Expectile Regression

Fabian Otto-Sobotka (), Radoslava Mirkov (), Benjamin Hofner () and Thomas Kneib ()
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Fabian Otto-Sobotka: Division of Epidemiology and Biometry, Carl von Ossietzky University Oldenburg
Radoslava Mirkov: Humboldt University Berlin, Department of Mathematics
Benjamin Hofner: Section Biostatistics, Paul-Ehrlich-Institut
Thomas Kneib: Georg-August-Universität Göttingen, Department of Economics

A chapter in Advances in Contemporary Statistics and Econometrics, 2021, pp 261-280 from Springer

Abstract: Abstract The flow of natural gas within a gas transmission network is studied with the aim to model high-demand situations. Knowledge about the latter can be used to optimise such networks. The analysis of data using shape-constrained expectile regression provides deeper insights into the behaviour of gas flow within the network. The models describe dependence of the maximal daily gas flow on the air temperature, including further effects, like day of the week and type of node. Particular attention is given to spatial effects. Geoadditive models offer a combination of such effects and are easily estimated with penalised mean regression. In order to put special emphasis on the highest demands, we use expectile regression, a quantile-like extension of mean regression that offers the same flexibility. Additional assumptions on the influence of the temperature can be added via shape-constraints. The forecast of gas loads for very low temperatures based on this approach and the application of the obtained results is discussed.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-73249-3_14

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DOI: 10.1007/978-3-030-73249-3_14

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