Abstract
Forecasting energy demand accurately is the basis for the formulation and implementation of energy planning. In this paper, energy demand influencing factors are mainly decomposed into scale economy effect, population size effect, energy structure effect, and residential consumption effect based on the Logarithmic Mean Divisia Index (LMDI). Then, the Cointegration and Granger Causality tests are used to discover the influencing factors of energy demand in China. On this basis, a hybrid optimization algorithm, the least-squares support-vector regression optimized by particle swarm optimization (PSO-LSSVR), is proposed to forecast the energy demand of China. Then, three scenarios are set up to analyze the further development of drive factors of energy demand. Finally, in accordance with the forecasting results, some suggestions related to China’s energy development policy are given. The main results are as follows. First, gross domestic product (GDP), the total population at the end of the year (POP), the coal consumption ratio in energy (CCR), and residential consumption levels (RCLs) are dominant indicators of energy demand in China. Second, the improved PSO-LSSVR model has significant superiority than other models in energy demand forecasting, a complex and nonlinear system with small samples. Third, China’s energy demand will peak in 2022, which is 4.9 million tce in all scenarios.
Highlights
We found that energy demand influencing factors can mainly be decomposed into scale economy effect, population size effect, energy structure effect, and residential consumption effect [21]
As of 2025, the coal consumption ratio was already less than 50%, which means that energy structure adjustment has been completed
The hybrid model, the LSSVR model optimized by the particle swarm optimization (PSO) algorithm, is proposed to forecasting China’s energy demand during 2020-2025
Summary
Energy demand forecasting has a guiding significance on the formulation and implementation of energy policy. Driven by technological advances and sustainable development, the global energy supply became cleaner and lower carbon. China has gradually entered the period of energy structure adjustments and transformation upgrading. As the largest developing country, has the largest total energy consumption in the world. The overwhelming growth of energy consumption in China will lead to an imbalance between energy supply and demand. This problem can occur in other developing counties. Forecasting the accurate energy demand in China plays a decisive role in formulating and implementing energy policy and provides enlightenment and reference significant for other developing countries. The existing research on the prediction model of energy demand is weak, so this paper focuses on the prediction model suitable for an energy system
Sign-in/Register to view highlights & summary 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.
