Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering
The detection of abnormal electricity consumption behavior has been of great importance in recent years. However, existing research often focuses on algorithm improvement and ignores the process of obtaining features. The optimal feature set, which reflects customers' electricity consumption be...
Main Authors: | Wei Zhang, Xiaowei Dong, Huaibao Li, Jin Xu, Dan Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9032114/ |
Similar Items
-
Detecting Abnormal Vehicular Dynamics at Intersections Based on an Unsupervised Learning Approach and a Stochastic Model
by: Teresa Garcia-Ramírez, et al.
Published: (2010-08-01) -
Towards an Unsupervised Feature Selection Method for Effective Dynamic Features
by: Naif Almusallam, et al.
Published: (2021-01-01) -
Unsupervised Dual Learning for Feature and Instance Selection
by: Liang Du, et al.
Published: (2020-01-01) -
Abnormal behaviour analysis algorithm for electricity consumption based on density clustering
by: Min Xiang, et al.
Published: (2019-05-01) -
Automated Optical Inspection Method for Light-Emitting Diode Defect Detection Using Unsupervised Generative Adversarial Neural Network
by: Che-Hsuan Huang, et al.
Published: (2021-08-01)