CORRECTION article

Front. Energy Res., 07 December 2023

Sec. Smart Grids

Volume 11 - 2023 | https://doi.org/10.3389/fenrg.2023.1345214

Corrigendum: Deep learning-based meta-learner strategy for electricity theft detection

  • 1. University of Klagenfurt, Klagenfurt am Wörthersee, Austria

  • 2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, MI, Italy

  • 3. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

  • 4. Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol, Cyprus

  • 5. Department of Computer Science, CTL Eurocollege, Limassol, Cyprus

In the published article, an Author name was incorrectly written as “Musaed Alhussain.” The correct spelling is “Musaed Alhussein.”

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Statements

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Summary

Keywords

power system, advanced metering infrastructure, deep learning, metaheuristics, smart grids

Citation

Shahzad F, Ullah Z, Alhussein M, Aurangzeb K and Aslam S (2023) Corrigendum: Deep learning-based meta-learner strategy for electricity theft detection. Front. Energy Res. 11:1345214. doi: 10.3389/fenrg.2023.1345214

Received

27 November 2023

Accepted

28 November 2023

Published

07 December 2023

Approved by

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Volume

11 - 2023

Updates

Copyright

*Correspondence: Zahid Ullah,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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