Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)

Maolin Cheng,Yun Liu,Jiano Li,Bin Liu

doi:10.3390/su12020698

Abstract

Forecasting China’s clean energy consumption has great significance for China in making sustainably economic development strategies. Because the main factors affecting China’s clean energy consumption are economic scale and population size, and there are three variables in total, this paper tries to simulate and forecast China’s clean energy consumption using the grey model GM (1, 3). However, the conventional grey GM (1, N) model has great simulation and forecasting errors, the main reason for which is the structural inconsistency between the grey differential equation for parameter estimation and the whitening equation for forecasting. In this case, this paper improves the conventional model and provides an improved model GM (1, N). The modeling results show that the improved grey model GM (1, N) built with the method proposed improves simulation and forecasting precision greatly compared with conventional models. To compare the model with other forecasting models, this paper builds a grey GM (1, 1) model, a regression model and a difference equation model. The comparison results show that the improved grey model GM (1, N) built with the method proposed shows simulation and forecasting precision superior to that of other models as a whole. In the final section, the paper forecasts China’s clean energy consumption from 2019 to 2025 using the improved grey model GM (1, N). The forecasting results show that, by 2025, China’s clean energy consumption shall reach the equivalent of 1.504976082 billion tons of standard coal. From 2019 to 2025, clean energy consumption shall increase by 11.32% annually on average, far above the economic growth rate, indicating China’s economic growth shall have a great demand for clean energy in the future. Studies have shown that China’s clean energy consumption shall increase rapidly with economic growth and population increase in the next few years.

Highlights

The Chinese economy is in an important development stage—the middle and late period of urbanization
Clean energy consumption has a great influence on economic growth, so forecasting China’s clean energy consumption has important significance for China in making energy development strategies and sustainable economic development strategies
The results show that the improved grey model GM (1, N) built with the method proposed has greatly improved precision compared with the conventional model

Summary

Introduction

The Chinese economy is in an important development stage—the middle and late period of urbanization. The developed hybrid system included three steps: first, preprocessing data, capturing and digging the wind energy time sequence’s main characteristics, and reducing the negative influence of noise; second, proposing a multi-objective optimization method, realizing the forecasting of subsequences and improving the precision and stability of forecasting precision; searching for the optimal function using the optimized extreme learning machine based on various modeling objectives, and getting the deterministic forecasting and uncertainty analysis results. Dumitru and Gligor [21] analyzed different time sequence modeling methods for the purpose of forecasting the power output of renewable energy like wind energy They used two models, the random model (ARIMA) and the model based on neural network (FFANN or MLP) for the forecasting. Forecasting results show that China’s clean energy consumption shall grow rapidly with the growth of economy and population in the few years

Methodology

Variables and Data

Empirical Results

Main Conclusions

Discussion