Abstract
Accurately forecasting China’s total electricity consumption is of great significance for the government in formulating sustainable economic development policies, especially, China as the largest total electricity consumption country in the world. The calculation method of the background value of the GM(1, 1) model is an important factor of unstable model performance. In this paper, an extrapolation method with variable weights was used for calculating the background value to eliminate the influence of the extreme values on the performance of the GM(1, 1) model, and the novel extrapolation-based grey prediction model called NEGM(1, 1) was proposed and optimized. The NEGM(1, 1) model was then used to simulate the total electricity consumption in China and found to outperform other grey models. Finally, the total electricity consumption of China from 2018 to 2025 was forecasted. The results show that China’s total electricity consumption will be expected to increase slightly, but the total is still very large. For this, some corresponding recommendations to ensure the effective supply of electricity in China are suggested.
Highlights
With the rapid growth of the Chinese economy, the total electricity consumption is increasing year by year, and China is the largest energy consumer in the world currently
Since the end of the last century, numerous models have been introduced for forecasting electricity consumption, such as the time series analysis model [2, 3], autoregressive integrated moving average (ARIMA) model [4, 5], support vector machine (SVM) [6, 7], Bayesian statistics [8], random forest [9], composite system method [10], artificial neural networks (ANNs) [11, 12], and deep learning models [13, 14]
Electricity consumption is closely related to a series of uncertain factors, such as the degree of economic development, industrial structure, population, and distribution losses; on the other hand, as China’s statistical departments did not release annual data relating to the electricity consumption before 2000 years ago, the sample size is limited
Summary
With the rapid growth of the Chinese economy, the total electricity consumption is increasing year by year, and China is the largest energy consumer in the world currently. Many scholars have put forward methods to improve the traditional method of calculation of grey model background value to increase modeling accuracy. Inspired by the literature [33], a novel extrapolation method for calculating the background value of the grey forecasting model (NEGM(1, 1)) is proposed to predict China’s total electricity consumption in this study. Compared with the traditional grey models, NEGM(1, 1) can significantly improve the smoothness effect of the background value and can effectively weaken the influence of extreme values on the model’s performance.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
