Electricity Theft Detection in AMI Based on Clustering and Local Outlier Factor
As one of the key components of smart grid, advanced metering infrastructure (AMI) provides an immense number of data, making technologies such as data mining more suitable for electricity theft detection. However, due to the unbalanced dataset in the field of electricity theft, many AI-based method...
Main Authors: | Yanlin Peng, Yining Yang, Yuejie Xu, Yang Xue, Runan Song, Jinping Kang, Haisen Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9500192/ |
Similar Items
-
A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss
by: Yining Yang, et al.
Published: (2022-01-01) -
Adaptive electricity theft detection method based on load shape dictionary of customers
by: Chunjiang Yan, et al.
Published: (2022-02-01) -
<i>k</i>-Means+++: Outliers-Resistant Clustering
by: Adiel Statman, et al.
Published: (2020-11-01) -
OFCOD: On the Fly Clustering Based Outlier Detection Framework
by: Ahmed Elmogy, et al.
Published: (2020-12-01) -
The truth about identity theft /
by: 328730 Stickley, Jim
Published: (2009)