A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

Abstract

Oil is an important strategic material and civil energy. Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production, oil import and export, and optimize the allocation of social resources. Therefore, a new grey model FENBGM(1,1) is proposed to predict oil consumption in China. Firstly, the grey effect of the traditional GM(1,1) model was transformed into a quadratic equation. Four different parameters were introduced to improve the accuracy of the model, and the new initial conditions were designed by optimizing the initial values by weighted buffer operator. Combined with the reprocessing of the original data, the scheme eliminates the random disturbance effect, improves the stability of the system sequence, and can effectively extract the potential pattern of future development. Secondly, the cumulative order of the new model was optimized by fractional cumulative generation operation. At the same time, the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model, and the particle swarm optimization algorithm (PSO) was used to search the optimal parameters of the model to enhance the adaptability of the model. Based on the above improvements, the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results. Then, taking the petroleum consumption of China's manufacturing industry and transportation, storage and postal industry as an example, this paper verifies the validity of FENBGM(1,1) model, analyzes and forecasts China's crude oil consumption with several commonly used forecasting models, and uses FENBGM(1,1) model to forecast China's oil consumption in the next four years. The results show that FENBGM(1,1) model performs best in all cases. Finally, based on the prediction results of FENBGM(1,1) model, some reasonable suggestions are put forward for China's oil consumption planning.

Keywords

Grey forecasting model / Variable weighted buffer operator / Particle swarm optimization / Oil consumption forecast

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 71901184, No. 72001181).

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