Search and application of gas load prediction method based on LSTM

Jianan Huang; Fei Zhang; Fanduo Bu; Chenchen Yang; Hao Zhang

doi:10.1117/12.2641023

13 October 2022 Search and application of gas load prediction method based on LSTM

Jianan Huang, Fei Zhang, Fanduo Bu, Chenchen Yang, Hao Zhang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871O (2022) https://doi.org/10.1117/12.2641023
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Accurate prediction of gas loads is of great significance to the rational planning of gas consumption by gas companies. Aiming at the problem that traditional forecasting methods cannot model the variation law of the load itself and its influencing factors at the same time, a load forecasting method based on long short-term memory (LSTM) is proposed. Firstly, the paper considers the historical load and various influencing factors comprehensively. Secondly, the temporal ability of LSTM is used to identify the internal variation on load in a longer time range. Then, the load prediction performance of different network architectures based on actual data is verified and compared with other algorithms. The results show that the proposed method can effectively improve the accuracy of load forecasting.

Citation Download Citation

Jianan Huang, Fei Zhang, Fanduo Bu, Chenchen Yang, and Hao Zhang "Search and application of gas load prediction method based on LSTM", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871O (13 October 2022); https://doi.org/10.1117/12.2641023

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