Electricity Theft Detection and Localization in Smart Grids for Industry 4.0-Reference-Cited by-同舟云学术

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Electricity Theft Detection and Localization in Smart Grids for Industry 4.0

Published:2022 Issue:3 Volume:33 Page:1473-1483
ISSN:1079-8587
Container-title:Intelligent Automation & Soft Computing
language:en
Short-container-title:

Author:

Wisetsri Worakamol,Qamar Shamimul,Verma Gaurav,Verma Deval,Kumar Kakar Varun,Chansongpol Thanyanant,Somtawinpongsai Chanyanan,Ching Tan Chai

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Software

Link

https://www.techscience.com/iasc/v33n3/47112/pdf

Reference16 articles.

1. Industry 4.0 based process data analytics platform: A waste-to-energy plant case study;Kabugo;International Journal of Electrical Power and Energy Systems,2020

2. IoT-enabled smart appliances under industry 4.0: A case study;Aheleroff;Advanced Engineering Informatics,2020

3. Internet of things in smart grid: Architecture, applications, services, key technologies, and challenges;Ghasempour;Inventions,2019

4. Electricity theft detection in smart grid systems: A CNN-LSTM based approach;Hasan;Energies,2019

5. Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids;Zheng;IEEE Transactions on Industrial Informatics,2018

Cited by 5 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 Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review;Electricity;2024-06-07

2. Development of new pocket distribution network inspection equipment;Journal of Physics: Conference Series;2024-02-01

3. Internet of Things Based Reduction of Electricity Theft in Urban Areas;2023 6th International Conference on Contemporary Computing and Informatics (IC3I);2023-09-14

4. Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure;Intelligent Automation & Soft Computing;2023

5. A BiLSTM-based Industry Abnormal Electricity Consumption Warning Model in the Context of Electricity IoT Edge Cloud;2022 Asia Power and Electrical Technology Conference (APET);2022-11-11

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