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

^*Corresponding author for this work

Research output: Journal Publications and Reviews › Editorial Preface

9 Citations (Scopus)

34 Downloads (CityUHK Scholars)

Original language	English
Article number	1175280
Journal	Frontiers in Energy Research
Volume	11
Online published	3 Apr 2023
DOIs	https://doi.org/10.3389/fenrg.2023.1175280
Publication status	Published - 2023
Externally published	Yes

Research Keywords

cybersecurity
load forecasting
low carbon
machine learning
renewable energy

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3389/fenrg.2023.1175280

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@article{1cc1e0bf08074f8db31e4205399f2b47,

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

keywords = "cybersecurity, load forecasting, low carbon, machine learning, renewable energy",

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",

issn = "2296-598X",

publisher = "Frontiers Research Foundation",

}

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

UR - http://www.scopus.com/inward/record.url?scp=85153524520&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85153524520&origin=recordpage

U2 - 10.3389/fenrg.2023.1175280

DO - 10.3389/fenrg.2023.1175280

M3 - Editorial Preface

SN - 2296-598X

VL - 11

JO - Frontiers in Energy Research

JF - Frontiers in Energy Research

M1 - 1175280

ER -

Editorial: Machine learning to support low carbon energy transition

Research Keywords

Publisher's Copyright Statement

UN SDGs

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