A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy

ISSN

0360-5442

e-ISSN

1873-6785

Svazek periodika

D

Číslo periodika v rámci svazku

263

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

125976-125976

Kód UT WoS článku

000895343100001

EID výsledku v databázi Scopus

2-s2.0-85141790856