孙毅;李世豪;崔灿;李彬;陈宋宋;崔高颖;

• 1:华北电力大学电气与电子工程学院
• 2:需求侧多能互补优化与供需互动技术北京市重点实验室(中国电力科学研究院有限公司)
• 3:国网江苏省电力公司电力科学研究院

摘要(Abstract)：

针对智能配用电大数据背景下用电数据离群点检测方法的适用性以及实际数据集中异常用电样本获取成本较高的问题,提出一种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。首先通过模糊聚类的方法将用户分类;然后提取每一类用户的用电行为特征量,采用主成分分析法对特征集进行降维;最后利用高斯核函数改进局部离群因子算法,提出高斯核密度局部离群因子(Gaussian kernel densitybased local outlier factor,GKLOF)算法,通过理论推导与仿真实验相结合的方式分析了GKLOF算法的特性。选取了5000个用户真实的用电数据进行实验分析,实验结果表明,该方法具有较高的检测准确率以及较为稳定的判定阈值,并且受局部数据分布的影响较小,更加适用于用户用电行为复杂多样以及实际数据集中所有用户用电行为类型信息未知情况下的离群点检测。

关键词(KeyWords)： 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

Abstract:

Keywords:

基金项目(Foundation): 国家重点研究发展计划项目(2016YFB0901104)~~

作者(Author): 孙毅;李世豪;崔灿;李彬;陈宋宋;崔高颖;

Email:

DOI:

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