Can China Meet Its 2030 Total Energy Consumption Target? Based on an RF-SSA-SVR-KDE Model

In order to accurately predict China’s future total energy consumption, this article constructs a random forest (RF)–sparrow search algorithm (SSA)–support vector regression machine (SVR)–kernel density estimation (KDE) model forecast consumption in 2022–2030. It is explored whether China can reach the relevant target 2030. This begins by using screen for influences be used as input set model. Then, sparrow applied optimize SVR overcome drawback of difficult parameter setting SVR. Finally, SSA-SVR China. interval forecasting was performed kernel estimation, which enhanced predictive significance By comparing prediction results and error values with those RF-PSO-SVR, RF-SVR RF-BP, it demonstrated that combined proposed paper more accurate. will have even better accuracy predictions.