Deep Active Learning-Enabled Cost-Effective Electricity Theft Detection in Smart Grids

Author:

Zhu Lipeng1ORCID,Wen Weijia2ORCID,Li Jiayong1ORCID,Zhang Cong1ORCID,Zhou Bin1ORCID,Shuai Zhikang1ORCID

Affiliation:

1. College of Electrical and Information Engineering, Hunan University, Changsha, China

2. State Grid Hunan Information and Telecommunication Company, Changsha, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Science and Technology Innovation Program of Hunan Province

Hunan Key Laboratory for Internet of Things in Electricity

Key Research and Development Program of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

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. Research on FCM-LR cross electricity theft detection based on big data user profile;International Journal of System Assurance Engineering and Management;2024-04-18

2. Improving Smart Grids Security: An Active Learning Approach for Smart Grid-Based Energy Theft Detection;IEEE Access;2024

3. Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection;IEEE Transactions on Instrumentation and Measurement;2024

4. The Effectiveness of Using AutoML in Electricity Theft Detection: The Impact of Data Preprocessing and Balancing Techniques;Lecture Notes in Computer Science;2024

5. A Hybrid Deep Learning Neural Network Model and Data-Driven Methods for Smart Grid Theft Detection;2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES);2023-12-14

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