Detail publikace

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

Du, J., Zheng, J., Liang, Y., Wang, B., Klemeš, J.J., Lu, X., Tu, R., Liao, Q., Xu, N., Xia, Y.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Daily prediction; Deep learning; Domain knowledge; Natural gas consumption; Temporal-spatial correlations

Autoři

Du, J., Zheng, J., Liang, Y., Wang, B., Klemeš, J.J., Lu, X., Tu, R., Liao, Q., Xu, N., Xia, Y.

Vydáno

15. 1. 2023

Nakladatel

Elsevier Ltd

ISSN

0360-5442

Periodikum

Energy

Ročník

D

Číslo

263

Stát

Spojené království Velké Británie a Severního Irska

Strany od

125976

Strany do

125976

Strany počet

19

URL

https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0360544222028626

BibTex

@article{BUT180343,
  author="Jiří {Klemeš} and Bohong {Wang}",
  title="A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction",
  journal="Energy",
  year="2023",
  volume="D",
  number="263",
  pages="19",
  doi="10.1016/j.energy.2022.125976",
  issn="0360-5442",
  url="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0360544222028626"
}