Application of a novel time-delay grey model based on mixed-frequency data to forecast the energy consumption in China

Abstract

It is of great constructive significance to predict total energy consumption accurately. At present, there are many prediction tools for processing and analyzing energy consumption, but most of them have the disadvantages of complex modeling or low accuracy, and less involve the modeling in the case of mixed-frequency data. For the sake of reflecting the timeliness of prediction, this paper analyzes the annual energy consumption influenced by high-frequency data. Based on mixed-frequency data, a novel grey model which comprehensively considers the time-delay cumulative effect of system behavior variable is designed. When there are high-frequency correlation factor sequences and low-frequency system behavior sequences, the model can automatically assign weights according to the characteristics of the high-frequency sequence. In addition, the inertia of the system behavior variable is considered more systematically, and the time-delay cumulative effect of the system behavior variable is incorporated into the original grey model. From the angle of verifying the feasibility of this new constructed model, an original multivariable grey prediction model and two non-grey methods are selected as the comparative models. Empirical findings indicate that the mean absolute percentage errors of the proposed model are 0.0387% and 0.0287% in the simulated stage and predicted stage, which is the lowest among the four models, and highlights the excellent performance. Eventually, the proposed model is applied to make a short-term forecast about total energy consumption in China from 2020 to 2023, and the predictions provide guidance for the formulation of energy consumption objectives.

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