Hybrid Deep Neural Networks for Detection of Non-Technical Losses in Electricity Smart Meters-Reference-Cited by-同舟云学术

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Hybrid Deep Neural Networks for Detection of Non-Technical Losses in Electricity Smart Meters

Published:2020-03 Issue:2 Volume:35 Page:1254-1263
ISSN:0885-8950
Container-title:IEEE Transactions on Power Systems
language:
Short-container-title:IEEE Trans. Power Syst.

Author:

Buzau Madalina-Mihaela^ORCID,Tejedor-Aguilera Javier,Cruz-Romero Pedro^ORCID,Gomez-Exposito Antonio^ORCID

Funder

Endesa

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

http://xplorestaging.ieee.org/ielx7/59/9003429/08846082.pdf?arnumber=8846082

Cited by 157 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Electricity behaviors anomaly detection based on multi-feature fusion and contrastive learning;Information Systems;2025-01

2. Stacked machine learning models for non-technical loss detection in smart grid: A comparative analysis;Energy Reports;2024-12

3. Detection of electricity theft based on Minimal Gated Memory network combined adaptive synthesis sampling and decision tree;Sustainable Energy, Grids and Networks;2024-09

4. NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning;Sensors;2024-07-30

5. Two-stage generalizable approach for electricity theft detection in new regions;Applied Energy;2024-07

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