Innovative grey multivariate prediction model for forecasting Chinese natural gas consumption

Accurate estimation of natural gas consumption plays a crucial role in energy planning, economic stability, environmental protection, and investment decisions. Despite numerous predictive studies related to natural gas consumption, most of these studies focus on univariate prediction tasks, lacking a reasonable and efficient multivariate prediction approach. To obtain precise forecasting results, this paper constructs a new adaptive grey multivariate prediction model called AGMPM(r,N) based on the fractional -order accumulation operation and grey system theory. It is found that AGMPM(r,N) can degenerate into some grey multivariable prediction models by replacing its own hyperparameters, which reflects its uniformity. In addition, the new model is unbiased for some special time series. In particular, the grey wolf optimizer is used to facilitate the model solution process. To validate the effectiveness of AGMPM(r,N), AGMPM(r,N) and 27 competing algorithms (9 natural gas consumption prediction methods, 10 multivariate prediction models, 7 machine learning algorithms and a statistical predictive model) are used to study the natural gas consumption in China. In addition, ablation experiments are executed. The results of experimental analysis show that the MAPE and MAE of AGMPM(r,N) are 2.795% and 89.880, respectively, which are superior to all competing methods and ablation models.