A novel multivariable grey prediction model and its application in forecasting coal consumption. (January 2022) - Duan, Huiming; Luo, Xilin
Abstract: Coal is an important energy source worldwide. Objectively and accurately predicting coal consumption is conducive to healthy coal industry development, because such predictions can provide references and warnings that are useful in formulating energy strategies and implementing environmental policies. Population size and area economic development are the main factors that affect coal consumption. Considering the above influences, this paper first establishes a differential equation and proposes a novel multivariable Verhulst grey model (MVGM(1, N)) based on grey information differences. MVGM(1, N) extends classical model from single-variable to multivariate and diminishes the characteristics of Verhulst's reliance on saturated S-shaped and single-peak data, making classical model more applicable to real situations. To prove the effectiveness of MVGM(1, N) simulation experiments are carried out in areas with high coal consumption. The result of this proposed model is more precise than that of NLARX, ARIMA and five classical grey models Finally, this novel multivariable model predicates coal consumption of Inner Mongolia and Gansu Provinces in China, the results show that MVGM(1, N) is preferable to other models, indicating that this model can effectively predict coal consumption. Highlights: Based on the impact of GDP and population on coal consumption, a differential equation is established. A new multivariable Verhulst grey prediction model (MVGM(1, n)) is established. The comparative study shows that the new model is superior to the other seven benchmark models. Coal consumption have been successfully predicted.