电网技术2018，Vol.42Issue(5)：1595-1604,10.DOI:10.13335/j.1000-3673.pst.2017.1586
基于高斯核函数改进的电力用户用电数据离群点检测方法
Improved Outlier Detection Method of Power Consumer Data Based on Gaussian Kernel Function
摘要
Abstract
In allusion to applicability of power consumer data outlier detection method in context of big data in smart power distribution and consumption systems, and high cost of obtaining abnormal samples for power consumption in actual data sets, an improved outlier detection method of power consumer data based on Gaussian kernel function was proposed. Firstly, the users were classified with fuzzy clustering method. Then various features of each type of users were extracted and PCA (principal components analysis) was used to reduce the dimension of feature vectors. Finally, Gaussian kernel function was used to improve local outlier factor (LOF) algorithm, and Gaussian kernel density local outlier factor (GKLOF) algorithm was proposed. Effectiveness of GKLOF algorithm was verified by combination of theoretical analysis and simulation. 5000 users' real power data were selected to perform the simulation, and simulation results proved that the proposed method had high detection accuracy and stable decision threshold. In addition, local data distribution had minor impact on this method. Therefor it is more suitable for outlier detection in the case that power consumption behavior is complex and type of power consumption behavior is unknown.关键词
电力大数据/数据挖掘/离群点检测/高斯核密度局部离群因子/用电行为分析Key words
power big data/data mining/outlier detection/Gaussian kernel density local outlier factor/power consumption behavior analysis分类
信息技术与安全科学引用本文复制引用
孙毅,李世豪,崔灿,李彬,陈宋宋,崔高颖..基于高斯核函数改进的电力用户用电数据离群点检测方法[J].电网技术,2018,42(5):1595-1604,10.基金项目
国家重点研究发展计划项目(2016YFB0901104).Project Supported by The National Key Research and Development Program (2016YFB0901104). （2016YFB0901104）
