A New Hybrid Method for China’s Energy Supply Security Forecasting Based on ARIMA and XGBoost

Pin Li,Jin-Suo Zhang

doi:10.3390/en11071687

Abstract

Energy supply security is a significant part of China’s security, directly influencing national security and economic and social sustainability. To ensure both China’s present and the future energy supply, it is essential to evaluate and forecast the energy supply level. However, forecasting the energy supply security level is difficult because energy supply security is dynamic, many factors affect it and there is a lack of accurate and comprehensive data. Therefore, based on previous studies and according to the characteristics of energy supply and the social development of China, first, the authors apply a comprehensive evaluation method to quantify the energy supply security. Second, based on the ARIMA-XGBoost hybrid model, the authors create two novel approaches for forecasting the energy supply security level of China. The authors find that: (1) energy supply security is dynamic, and green development has become the theme of China’s energy development. The energy industry urgently needs to provide more high-quality ecological energy products to meet the people’s desire for a beautiful ecological environment; (2) since the mean absolute percentage errors are below 4.5% when forecasting the energy supply security indicators, the ARIMA-XGBoost hybrid model is more accurate for forecasting China’s energy supply security level and (3) the security level of China’s energy supply has developed periodic features; the ESSI can improve by about 0.2 every five years, but, due to the low starting point and multiple types of constraints, it is difficult to reach the safety level in a short time.

Highlights

Energy is the most basic source of power for the development of human society, a constraint condition for economic development and the progress of civilization, and an important basis for the national economy to achieve sustainable development and national security
XGBoost scalability is derived from several important systems and algorithm optimization, including a new tree learning algorithm for processing sparse data and a reasonable weighted quantile sketch process which allows the processing of instance weights in approximate tree learning
A combination of the linear ARIMA model and the appropriate nonlinear XGBoost model is obtained according to Equations (9) and (12)

Summary

Introduction

Energy is the most basic source of power for the development of human society, a constraint condition for economic development and the progress of civilization, and an important basis for the national economy to achieve sustainable development and national security. The energy supply and security have a bearing on the overall development of a country’s economy, which is crucial for a country’s prosperity and development, improvement of people’s life, and its long-term social stability. The global energy demand will continue to grow in the future. It is expected that by 2040, total global energy demand will increase by one third, and China and India are the two driving forces [1]. Following a long period of development, China’s energy supply has entered a “new era” where China’s energy development has achieved remarkable results: a continuous slowdown in the total energy demand

Methods

Results

Conclusion