基于海量用电数据的用户负荷模式快速提取方法研究  

Study on fast extraction method of user load pattern based on massive data

作　　者：卢锦玲[1] 马冲[1] 冯翠香 LU Jinling;MA Chong;FENG Cuixiang(School of Electrical and Electronic Engineering,North China Electric Power Universit)

机构地区：[1]华北电力大学电气与电子工程学院,河北保定071003

出　　处：《电力科学与工程》2018年第4期49-56,共8页Electric Power Science and Engineering

摘　　要：对用电大数据进行快速、准确、高效的挖掘,是得到用户负荷模式不可或缺的基础工作。首先分析了用电数据的分布特点,利用统计学中四分位法的快速性和3σ法的精确性,提出了一种"横向—纵向"检测法,对异常用电数据进行检测与修正;其次,在综合对比了几种典型降维方法的基础上,采用主成分分析法对海量高维用电数据进行降维处理将极大地提高负荷模式提取效率;最后,对传统K-means算法进行改进,得到一种Fast K-means(FK-means)算法,该方法为减小聚类时间引入二分法思想,为提高聚类结果可靠性,将聚类有效性指标DBI与CHI相结合。采用中国南方某市实际量测用电数据验证了该算法能够快速对负荷模式进行提取且具有鲁棒性好的特点。The work of mining power big data quickly,accurately and efficiently is an indispensable basic task to get the load pattern of users. Firstly,this paper analyzes the distribution characteristics of electricity data. By using the fastness of Quarterback method and the accuracy of 3σ method,a＂horizontal-vertical＂detection method is proposed to detect and correct the abnormal electricity data.Secondly, after comparing several typical methods for dimensionality reduction, the principal component analysis is adopted to reduce the dimensionality of massive high-dimensional electricity data,which will greatly improve the efficiency of load pattern extraction. Then,an improved FKmeans algorithm is obtained based on the traditional K-means algorithm. In this method,the idea of dichotomy for reducing the clustering time is introduced. To improve the reliability of clustering results,the clustering validity indexes DBI and CHI are introduced. Finally,the characteristics of fast extracting load pattern and good robustness of the algorithm are verified by using the actual measurement of electricity in a city in southern China.

关 键 词：大数据 异常数据 数据处理 聚类有效性指标 聚类算法 负荷模式 

分 类 号：TM714[电气工程—电力系统及自动化]