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In order to improve the application area and the prediction accuracy of GM(1,1) model, a novel Grey model is proposed in this paper. To remedy the defects about the applications of traditional Grey model and buffer operators in medium- and long-term forecasting, a Variable Weights Buffer Grey model is proposed. The proposed model integrates the variable weights buffer operator with the background value optimized GM(1,1) model to implement dynamic preprocessing of original data. Taking the maximum degree of Grey incidence between fitting value and actual value as objective function, then the optimal buffer factor is chosen, which can improve forecasting precision, make forecasting results embodying the internal trend of original data to the maximum extent, and improve the stability of the prediction. To verify the effectiveness of the proposed model, the energy consumption in China from 2002 to 2009 is used for the modeling to forecast the energy consumption in China from 2010 to 2020, and the forecasting results prove that the GVGM(1,1) model has remarkably improved the forecasting ability of medium- and long-term energy consumption in China

Geometrical Variable Weights Buffer GM(1,1) Model and Its Application in Forecasting of China’s Energy Consumption