中国电机工程学报2016，Vol.36Issue(2)：379-387,594,10.DOI:10.13334/j.0258-8013.pcsee.2016.02.008
基于无监督学习的电力用户异常用电模式检测
Anomaly Detection for Power Consumption Patterns Based on Unsupervised Learning
庄池杰 1张斌 2胡军 1李秋硕 3曾嵘1
作者信息
- 1. 电力系统及发电设备控制和仿真国家重点实验室(清华大学电机系),北京市海淀区 100084
- 2. 国家电网公司西北分部,陕西省西安市 7100481
- 3. 南方电网科学研究院,广东省广州市 510080
- 折叠
摘要
Abstract
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.关键词
用电模式/电力大数据/异常检测/无监督学习/局部离群因子/反窃电技术Key words
power consumption patterns/power big data/anomaly detection/unsupervised learning/local outlier factor/anti-stealing of power energy分类
信息技术与安全科学引用本文复制引用
庄池杰,张斌,胡军,李秋硕,曾嵘..基于无监督学习的电力用户异常用电模式检测[J].中国电机工程学报,2016,36(2):379-387,594,10.
