Editorial: Machine learning to support low carbon energy transition

Haochen Hua; Haoxing Wu; Jun Shen; Kang Li; Zhao Yang Dong

doi:10.3389/fenrg.2023.1175280

Editorial: Machine learning to support low carbon energy transition

Haochen Hua^*
, Haoxing Wu
, Jun Shen
, Kang Li
, Zhao Yang Dong

^*此作品的通讯作者

Hohai University
Beijing Institute of Technology
University of Leeds
Nanyang Technological University

科研成果: 期刊稿件 › 社论

15 引用（Scopus）

源语言	英语
期刊论文编号	1175280
期刊	Frontiers in Energy Research
卷	11
DOI	https://doi.org/10.3389/fenrg.2023.1175280
出版状态	已出版 - 2023
已对外发布	是

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.3389/fenrg.2023.1175280

其它文件与链接

链接到 Scopus 的出版物

引用此

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title = "Editorial: Machine learning to support low carbon energy transition",

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author = "Haochen Hua and Haoxing Wu and Jun Shen and Kang Li and Dong, \{Zhao Yang\}",

year = "2023",

doi = "10.3389/fenrg.2023.1175280",

language = "English",

volume = "11",

journal = "Frontiers in Energy Research",

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TY - JOUR

T1 - Editorial

T2 - Machine learning to support low carbon energy transition

AU - Hua, Haochen

AU - Wu, Haoxing

AU - Shen, Jun

AU - Li, Kang

AU - Dong, Zhao Yang

PY - 2023

Y1 - 2023

KW - cybersecurity

KW - load forecasting

KW - low carbon

KW - machine learning

KW - renewable energy

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DO - 10.3389/fenrg.2023.1175280

M3 - Editorial

AN - SCOPUS:85153524520

SN - 2296-598X

VL - 11

JO - Frontiers in Energy Research

JF - Frontiers in Energy Research

M1 - 1175280

ER -