Novel compositional data's grey model for structurally forecasting Arctic crude oil import

Novel compositional data's grey model for structurally forecasting Arctic crude oil import

Crude oil

Optimization

Forecasting

Petroleum transportation

Proven reserves

Transportation routes

Evolutionary algorithms

The reserve of crude oil in Arctic area is abundant. Ice melting is making it possible to have intermediate access to the Arctic crude oil and its transportation. A novel compositional datas grey model is proposed in this paper to structurally forecast Arctic crude oil import. Firstly, the general accumulative operation sequence of multivariate compositional data is defined according to Aitchison geometry, then obtaining the novel model with the form of the compositional data vectors. Secondly, this paper studies the least square parameter estimation of the model. The novel model is deduced and selected as the time-response expression of the solution. Thirdly, this papers infuses the novel model with traditional grey model to improve its robustness. Differential Evolution algorithm is introduced to determine the optimal value of the general matrix. Lastly, two validation examples are provided for confirming the effectiveness of the novel model by comparing with other existing models, before being employed to forecast the crude oil import structure in China. The results show that the novel model provides better performance in all crude oil cases in short-term forecasting. Therefore, by using the new model, the Chinas development parameter is 0.5214 and Determination Factor of the novel model is 0.5999, which mean that the crude oil import structure of China is being changed. Specifically, the amount of crude oil imported from Arctic area are obviously increasing in the next 6 years, showing sufficient proof of the edge owned by Arctic area: abundant crude oil reserves and shortening transportation distance. Copyright 2020, The Authors. All rights reserved.

2020

Qilong, Pan

Jieru, Yin

Xinping, Xiao

journalArticle

23318422