A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction. (15th January 2023)

• Record Type: Journal Article
• Authors: Du, Jian; Zheng, Jianqin; Liang, Yongtu; Wang, Bohong; Klemeš, Jiří Jaromír; Lu, Xinyi; Tu, Renfu; Liao, Qi; Xu, Ning; Xia, Yuheng
• Is Part Of: Energy. Volume 263:Part D(2023)
• Publication Date: 2023-01-15
• Subjects: Natural gas consumption -- Daily prediction -- Domain knowledge -- Temporal-spatial correlations -- Deep learning; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125976
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: A hybrid spatial-temporal network is proposed for predicting natural gas consumption. The knowledge-enhanced association graph is established to represent spatial correlation patterns. Verification is carried out on two different natural gas pipeline network cases. Sensitivity analysis on the significance of different model components is conducted.