Electricity Behavior Modeling and Anomaly Detection Services Based on a Deep Variational Autoencoder Network

被引:1
|
作者
Lin, Rongheng [1 ]
Chen, Shuo [1 ]
He, Zheyu [1 ]
Wu, Budan [1 ]
Zou, Hua [1 ]
Zhao, Xin [2 ]
Li, Qiushuang [2 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[2] State Grid Shandong Elect Power Co, Econ & Res Inst, Jinan 250021, Peoples R China
关键词
load pattern; anomaly detection; autoencoder network;
D O I
10.3390/en17163904
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior.
引用
收藏
页数:20
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