A Comparative Analysis of Unbalanced Data Handling Techniques for Machine Learning Algorithms to Electricity Theft Detection
Author:
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9178820/9185488/09185822.pdf?arnumber=9185822
Cited by 19 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. Reducing non-technical losses in electricity distribution networks: Leveraging explainable AI and three lines of defence model to manage operational staff-related factors;e-Prime - Advances in Electrical Engineering, Electronics and Energy;2024-09
2. Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review;Energies;2024-06-20
3. 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
4. Enhancing Rainfall Prediction Accuracy through XGBoost Model with Data Balancing Techniques;2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA);2024-03-01
5. Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning‐Based Data Analysis Technique;Journal of Electrical and Computer Engineering;2024-01
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