Using long short-term memory model to study risk assessment and prediction of China’s oil import from the perspective of resilience theory

Sai Chen, Yan Song, Yueting Ding, Ming Zhang and Rui Nie

Energy, 2021, vol. 215, issue PB

Abstract: Oil has to be redistributed around the world because of their uneven distribution. Therefore, the method of accurately identifying and forecasting the risks of oil import has always been the focus of research. Thus, we re-examined the risk of oil import from the whole process of oil import. Based on resilience theory, a framework for risk assessment was established by referring to the 4 A factor (availability, accessibility, affordability and acceptability). Then, long and short term memory network model (LSTM) was constructed and trained to forecast the risk of oil import. Taking the oil import network in China as an example, by comparing with the BP, SVM and CNN model, the better fitting effect and higher forecasting accuracy of LSTM model were verified; According to the results, from 2011 to 2018, China’s oil import system was less resilient and experienced different stages, which are driven by different dominate factors. Besides, availability and affordability risks remain severe in the foreseeable future. Therefore, China should optimize the combination of exporters, actively participate in the development of transportation routes, establish and improve China’s crude oil futures market, and plan the layout in advance to avoid oil import risks.

Keywords: Oil import; Resilience; Forecasting; Long short-term memory (LSTM) (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:215:y:2021:i:pb:s0360544220322593

DOI: 10.1016/j.energy.2020.119152

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