Forecasting China’s per Capita Living Energy Consumption by Employing a Novel DGM (1, 1, tα) Model with Fractional Order Accumulation

Abstract

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country’s medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China’s per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China’s per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China’s per capita living energy consumption. The results show that the proposed model is more suitable for predicting China’s per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China’s per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers.

Forecasting per Capita Energy Consumption in China Using a Spatial Discrete Grey Prediction Model

“…Good fitting accuracy is a prerequisite for an accurate prediction. In Figure 6, the forecasting results show that the PCECs of most Chinese provinces will increase in the future, which is consistent with the results of Lu et al [59], whose projections indicate that the PCEC of China will continue to grow rapidly in the coming years; however, the PCEC of Tianjin will peak in 2024, the PCEC of Qinghai will remain largely unchanged, and the PCEC of Beijing will tend to decrease, possibly due to technological advances, a reduction in energy consumption in the surrounding areas, or supply-side reform to improve the efficiency of energy consumption in the Beijing-Tianjin region.…”

“…It is necessary to establish a matrix prior to correlation testing. Therefore, referring to the practice of Wang and Zhang [59], we select a spatial geographic matrix to model, and the specific settings are shown below.…”