Editorial: Machine learning to support low carbon energy transition

Hua, H., Wu, H., Shen, J. et al. (2 more authors) (2023) Editorial: Machine learning to support low carbon energy transition. Frontiers in Energy Research, 11. 1175280. ISSN 2296-598X

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Item Type: Article
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© 2023 Hua, Wu, Shen, Li and Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: low carbon, machine learning, renewable energy, load forecasting, cybersecurity
Dates:
  • Published: 3 April 2023
  • Published (online): 3 April 2023
  • Accepted: 24 March 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 15 Jul 2024 14:49
Last Modified: 15 Jul 2024 14:49
Status: Published
Publisher: Frontiers
Identification Number: 10.3389/fenrg.2023.1175280
Open Archives Initiative ID (OAI ID):

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