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CORRECTION article

Front. Energy Res., 07 December 2023
Sec. Smart Grids

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

Faisal ShahzadFaisal Shahzad1Zahid Ullah
Zahid Ullah2*Musaed AlhusseinMusaed Alhussein3Khursheed AurangzebKhursheed Aurangzeb3Sheraz Aslam,Sheraz Aslam4,5
  • 1University of Klagenfurt, Klagenfurt am Wörthersee, Austria
  • 2Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, MI, Italy
  • 3Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
  • 4Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol, Cyprus
  • 5Department of Computer Science, CTL Eurocollege, Limassol, Cyprus

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

by Shehzad F, Ullah Z, Alhussein M, Aurangzeb K and Aslam S (2023). Front. Energy Res. 11:1232930. doi: 10.3389/fenrg.2023.1232930

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.

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.

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

Copyright © 2023 Shahzad, Ullah, Alhussein, Aurangzeb and Aslam. 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.

*Correspondence: Zahid Ullah, emFoaWQudWxsYWhAcG9saW1pLml0

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.