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

Xiwen Cui (), Xinyu Guan, Dongyu Wang, Dongxiao Niu and Xiaomin Xu
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Xiwen Cui: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Xinyu Guan: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongyu Wang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Xiaomin Xu: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Energies, 2022, vol. 15, issue 16, 1-13

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

Keywords: energy consumption; sparrow algorithm; support vector regression machine; kernel density estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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