Unsupervised Detection of Non-Technical Losses via Recursive Transform Learning-Reference-Cited by-同舟云学术

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Unsupervised Detection of Non-Technical Losses via Recursive Transform Learning

Published:2021-04 Issue:2 Volume:36 Page:1241-1244
ISSN:0885-8977
Container-title:IEEE Transactions on Power Delivery
language:
Short-container-title:IEEE Trans. Power Delivery

Author:

Sharma Shalini,Majumdar Angshul

Funder

Infosys Center for Artificial Intelligence @ IIIT Delhi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

http://xplorestaging.ieee.org/ielx7/61/9385771/09216034.pdf?arnumber=9216034

Cited by 7 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. Edge AI for Internet of Energy: Challenges and perspectives;Internet of Things;2024-04

2. Identification of customer electricity usage anomalies based on random matrix theory;International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023);2024-01-09

3. Transfer learning based graph convolutional network with self-attention mechanism for abnormal electricity consumption detection;Energy Reports;2023-12

4. An industry 4.0 based data analytics framework for the detection of non-technical losses in a smart grid;2023 IEEE 6th Colombian Conference on Automatic Control (CCAC);2023-10-17

5. A two stage approach to electricity theft detection in AMI using deep learning;International Journal of Electrical Power & Energy Systems;2023-08

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