Study on Pearl River Delta oil security impact model based on LSTM and multi-indicator integration

Xianghui Meng; Yongzhi Wang; Jiaxiang Wang

doi:10.1117/12.2675257

25 May 2023 Study on Pearl River Delta oil security impact model based on LSTM and multi-indicator integration

Xianghui Meng, Yongzhi Wang, Jiaxiang Wang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126363C (2023) https://doi.org/10.1117/12.2675257
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Oil is a crucial strategic resource and a guarantee for the steady operation of a nation. Lack of oil supply has a significant impact on both national security and social, economic, and social development. Given that the PRD region is one of China's most significant economic zones, we took nine indicators from both supply and demand (pipeline transportation of refined oil products, oil and by-product production, electricity consumption, car and motor vessel ownership, population, GDP, output value of primary, secondary, and tertiary industries), and obtained the oil consumption of the PRD for a lengthy period of 15 years. We have also designed and implemented an oil consumption forecasting model based on a long and short-term memory network algorithm, with a loss rate of 0.1, which verifies the validity of the model. The suggested model is a useful tool for predicting regional oil consumption and can serve as a foundation for study of future oil supply and demand in the PRD region.

Citation Download Citation

Xianghui Meng, Yongzhi Wang, and Jiaxiang Wang "Study on Pearl River Delta oil security impact model based on LSTM and multi-indicator integration", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126363C (25 May 2023); https://doi.org/10.1117/12.2675257

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