Anomaly detection for power consumption patterns based on unsupervised learning

被引：0

作者：

Zhuang C. ^{[1
]}

Zhang B. ^{[2
]}

Hu J. ^{[1
]}

Li Q. ^{[3
]}

Zeng R. ^{[1
]}

机构：

[1] State Key Lab of Control and Simulation of Power Systems and Generation Equipments, Dept. of Electrical Engineering, Tsinghua University, Haidian District, Beijing

[2] Northwest Branch of State Grid Corporation China, Xi'an, 710048, Shaanxi Province

[3] CSG Electric Power Research Institute, Guangzhou, 510080, Guangdong Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2016年 / 36卷 / 02期

关键词：

Anomaly detection; Anti-stealing of power energy; Local outlier factor; Power big data; Power consumption patterns; Unsupervised learning;

D O I：

10.13334/j.0258-8013.pcsee.2016.02.008

中图分类号：

学科分类号：

摘要：

The primary purpose of anomaly detection for power consumption patterns is to lower the non-technical losses (NTL), thus reducing the operating costs for power utility. A model based on unsupervised learning was proposed to detect anomaly consumption patterns. This model is suitable for load dataset without training set. The model includes modules of feature extraction, principal component analysis, grid processing, calculation of local outlier factor (LOF), etc. Firstly, various features were extracted from load profiles to characterize consumption patterns of the customers. Then PCA was used to map customers to a two-dimensional plane. This mapping procedure is in favor of data visualization and LOF calculation. The grid processing procedure can screen data in low density region and thus lift calculation efficiency. The output of the model is abnormal degree for all customers' consumption patterns. The result indicates that with the use of this abnormality sequence, detecting customers with higher LOF rank can find out most abnormal consumption patterns. © 2016 Chin. Soc. for Elec. Eng.

引用

页码：379 / 387

页数：8

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