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

Shuanghua Liu,Chong Liu,Ting Feng,Zijie Dong,Haodan Pang,Xin Ma

doi:10.1155/2021/6635462

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.

Highlights

With the advancement of urbanization, economic growth, and improvement of the living standards of residents, the demand for energy in China has been greatly increased, in which the energy consumption of residents shows the characteristics of rapid growth, accounting for a large proportion of China’s total energy consumption
The fractional accumulated generating operator is crucial for processing original sequences that is affected by nonlinearity and uncertainty, generating satisfactory results in many applications. erefore, we introduce the fractional order accumulation into the grey forecasting model (GM) (1, 1, tα) model to improve the performance of the existing grey models
When r ∈ (0, 1) and α 1, the FDGM (1,1, tα) yields the FNDGM (1, 1) model [27]. us, it can be seen that the FDGM (1, 1, tα) model is a highly adaptive grey prediction model

Summary

Introduction

With the advancement of urbanization, economic growth, and improvement of the living standards of residents, the demand for energy in China has been greatly increased, in which the energy consumption of residents shows the characteristics of rapid growth, accounting for a large proportion of China’s total energy consumption. Whether the future energy supply can support the sustainable growth of China’s economy has become a topic of concern at home and abroad. Erefore, it is of great practical significance to accurately predict the per capita living energy consumption in the future for maintaining the healthy, sustainable, and stable development of China’s social economy. E main difference between them is the different samples required for modeling. E former requires only a small amount of sample data, while the latter is based on large sample modeling. Some data in China has been lost due to various reasons. In this small sample case, the regression prediction model is obviously no longer applicable

Methods

Results

Conclusion