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Title: Research on energy supply chain risk prediction based on the fuzzy C-means clustering algorithm

Authors: Tao Xiao; Tao Zhang; Ning Zhang

Addresses: College of Science, Hebei Agricultural University, Baoding 071001, Hebei, China ' Youth League Committee, Hebei Agricultural University, Baoding 071001, Hebei, China ' Department of Computer Science and Technology, Renai College of Tianjin University, Jinghai, Tianjin 301636, China

Abstract: In order to improve the ability of risk prediction, a risk prediction method of energy supply chain based on fuzzy C-means clustering algorithm is proposed. Based on the regression analysis results of risk data samples, panel data fusion is carried out to extract the correlation feature of risk panel data of energy supply chain. Using the prior information distributed detection method to construct the statistical characteristic quantity of energy supply chain risk prediction. According to the prior sample regression analysis results of risk prediction of energy supply chain, the risk characteristics of energy supply chain are extracted, and the fuzzy C-means clustering method is used to cluster the risk characteristics, and the risk prediction of energy supply chain is carried out. The simulation results show that this method has high accuracy and credibility for energy supply chain risk prediction, and improves the risk management ability of energy supply chain.

Keywords: fuzzy C-means; clustering; energy supply chain; risk prediction.

DOI: 10.1504/IJGEI.2022.10044875

International Journal of Global Energy Issues, 2022 Vol.44 No.1, pp.65 - 75

Received: 01 Feb 2020
Accepted: 16 Jan 2021

Published online: 08 Feb 2022 *

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