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

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.

Macroeconomic Short-Term High-Precision Combined Forecasting Algorithm Based on Grey Model

“…Model. is paper mainly uses the Cobb Douglas production function to estimate the potential growth rate of the country's economy [17].…”

Analysis and Forecasting the Agriculture Production Sector in Rwanda

“…Hence, to improve its prediction capacity and spread the grey method application range, researches provided various solutions. Several new varieties of grey models, like the generalized discrete grey model (GDGM), nonlinear grey Bernoulli model (NGBM) with an added suggestion of the NGBM optimal parameter model (NGBM-OP) and others were established following the GM(1,1) grey theory (Liu and Lin, 2011), (George et al, 2016), (Cheng et al, 2020). In this research the, nonlinear grey Bernoulli model (NGBM) with an optimal parameter model (NGBM-OP) was adopted based on its smallest values of MAE, RMSE, and MAPE compared with others in the study.…”

“…In this research the, nonlinear grey Bernoulli model (NGBM) with an optimal parameter model (NGBM-OP) was adopted based on its smallest values of MAE, RMSE, and MAPE compared with others in the study. As a robust system, the grey models estimated the future projections robustly in several areas, like engineering (Jiang et al, 2004), economics (Chen et al, 2008), and recently in the electronic paper industry and energy (Huang et al, 2015;Cheng et al, 2020). However, grey models, as others, do not have any control over future shocks or uncontrolled structures.…”

A new multivariate grey prediction model for forecasting China’s regional energy consumption

“…Wu et al ( 2018 ) found, by using the grey convex correlation model, that population is the most important factor affecting power consumption in China’s Shandong Province, and established a fractional GM(1,N) model to predict the consumption of electricity. Cheng et al ( 2020 ) applied an improved GM(1, N) model for predicting clean energy consumption while considering the GDP and population.…”

“…Some scholars have sought to increase the accuracy of forecasts of energy consumption by improving the structure of the traditional grey multivariate model. Cheng et al ( 2020 ) modified the traditional multivariate grey forecasting model by adding a constant term to the whitening differential equation to predict China’s consumption of clean energy. Wang and Hao ( 2016 ) used the improved multivariable GMC(1,N) model with optimized parameters to predict industrial energy consumption in China.…”