A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU146501" target="_blank" >RIV/00216305:26210/23:PU146501 - isvavai.cz</a>
Result on the web
<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0360544222028626" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0360544222028626</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.energy.2022.125976" target="_blank" >10.1016/j.energy.2022.125976</a>
Alternative languages
Result language
angličtina
Original language name
A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction
Original language description
The accurate prediction of natural gas consumption plays a central role in long-distance pipeline system production and transportation planning, and it becomes even more important during present political situation. The existing prediction methods for natural gas consumption barely consider spatial correlations and domain knowledge. As a result, the study proposes a novel deep learning prediction method (knowledge-enhanced graph-based temporal-spatial network, abbreviated to KE-GB-TSN) for predicting natural gas consumption by integrating domain knowledge into association graph construction and capturing temporal-spatial features via a hybrid deep learning network. This study first applies the domain knowledge that analyses the operation technique of the natural gas pipeline network and combines the historical data to establish an association graph. Subsequently, the historical data and association graphs are input to a hybrid deep learning network to predict natural gas consumption. The comparative experiments are conducted by taking real-world cases of natural gas consumption as examples. At last, a sensitivity analysis of different components combination is carried out to exhibit the significance of each component in the proposed model. The results prove that the proposed model is capable of achieving more accurate and efficient predicted results compared to the advanced models, such as decision trees and gated recurrent units. The Mean Absolute Relative Errors and Root Mean Squared Relative Errors gotten by the proposed model are less than 0.11 and 0.14 in all cases, indicating an improvement compared to previous works. Additionally, it is also suggested that domain knowledge and temporal-spatial correlations are crucial for the excellent performance of the prediction model.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20704 - Energy and fuels
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy
ISSN
0360-5442
e-ISSN
1873-6785
Volume of the periodical
D
Issue of the periodical within the volume
263
Country of publishing house
GB - UNITED KINGDOM
Number of pages
19
Pages from-to
125976-125976
UT code for WoS article
000895343100001
EID of the result in the Scopus database
2-s2.0-85141790856