Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems
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- Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.
- Hanem I. Hegazy & Adly S. Tag Eldien & Mohsen M. Tantawy & Mostafa M. Fouda & Heba A. TagElDien, 2022. "Real-Time Locational Detection of Stealthy False Data Injection Attack in Smart Grid: Using Multivariate-Based Multi-Label Classification Approach," Energies, MDPI, vol. 15(14), pages 1-18, July.
- Arul Rajagopalan & Dhivya Swaminathan & Meshal Alharbi & Sudhakar Sengan & Oscar Danilo Montoya & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly, 2022. "Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods," Energies, MDPI, vol. 15(23), pages 1-18, November.
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Cited by:
- Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
- Soohyun Kim & Youngghyu Sun & Seongwoo Lee & Joonho Seon & Byungsun Hwang & Jeongho Kim & Jinwook Kim & Kyounghun Kim & Jinyoung Kim, 2024. "Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review," Energies, MDPI, vol. 17(12), pages 1-23, June.
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Keywords
smart grid; false data injection; electricity fraud; deep learning; clustering; privacy preservation; adversarial attacks; ensemble learning;All these keywords.
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