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

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 to forecast China’s future energy consumption in 2022–2030. It is explored...

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Bibliographic Details
Main Authors: Xiwen Cui, Xinyu Guan, Dongyu Wang, Dongxiao Niu, Xiaomin Xu
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Energies
Subjects:
energy consumption
sparrow algorithm
support vector regression machine
kernel density estimation
Online Access:https://www.mdpi.com/1996-1073/15/16/6019

Internet

https://www.mdpi.com/1996-1073/15/16/6019

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