Detecting Non-Technical Losses in the Energy Sector using MLPGRU: An Anomaly Detection Approach-Reference-Cited by-同舟云学术

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Detecting Non-Technical Losses in the Energy Sector using MLPGRU: An Anomaly Detection Approach

Published:2023-12-03 Issue: Volume:15 Page:1-6
ISSN:
Container-title:2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)
language:
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Author:

Khan Nitasha¹,Shahid Zeeshan²,Bakar Sajak Aznida Abu³,Alam Mansoor⁴

Affiliation:

1. Universiti Kuala Lumpur,British Malaysian Institute,Sungai Pusu,Malaysia

2. Nazeer Hussain University,Electrical Engineering Department,Karachi,Pakistan

3. Universiti Kuala Lumpur,MIIT,Lumpur,Malaysia

4. Riphah International University,USA Faculty of Computing,Islamabad,Pakistan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10406302/10407105/10408309.pdf?arnumber=10408309

Reference38 articles.

1. Detection of electricity theft using data processing and LSTM method in distribution systems

2. A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

3. A review of non-technical loss attack models and detection methods in the smart grid

4. Performance analysis of machine learning classifiers for non-technical loss detection

5. Using multiple data sources to detect manipulated electricity meter by an entropy-inspired metric

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