摘要
智能电网用户行为特征的分析在电力营销策略中扮演者重要的角色。文中结合KL变换和KL散度的方法,提取与分类用电数据信息的特征,实现不同类型的用电数据划分。同时通过综合分析所有用户的日负荷曲线,提取不同类型用户的典型日负荷曲线。研究结果表明:基于KL变换的方法,通过对原始数据的压缩和主要特征的保留,大大降低了智能电网数据提取与分类的计算量,提高了时间效率;基于KL散度的方法,通过对k-means算法中的k值和初始聚类中心的选择进行优化,提高了聚类效果的准确率;实例中电网用户正常数据为38组,可分为3类典型用户,迎峰用电型、错峰用电型、部分迎峰用电型。该研究结果可以更加有效地对电网用户用电行为进行分类,从而为售电公司进行业务拓展提供技术基础。
The analysis of behavior feature in smart grid users plays an important role in power marketing strategy. Based on KL-divergence and KL transform,this paper completed feature extraction and classification in smart grid data,which achieved grid data division of different types. Moreover,based on the overall analysis of the daily load profile in all users,it is extracted that the typical daily load profile in different types of users. The research results prove that it is greatly reduced that calculation of extraction and classification in smart grid users by means of KL transform,it increases the time efficiency by the compression of raw data and the retention of main feature,it is optimized that the selection of initial clustering center and cluster number by means of KL-divergence,so the accuracy of clustering is improved;the normal data of grid users in this instance is 38 groups,the users are divided into 3 categories of peak electricity type,peak avoidance electricity type and part meeting peak electricity type. The results can be used to classify behavior feature of smart grid users more effectively,it will provide a technical basis for business expansion of electric power company.
作者
李晖照
王雪
郭莹
Li Huizhao;Wang Xue;Guo Ying(State Grid Hubei Electric Power Corporation,Wuhan 430077,China;Customers Service Center of State Grid Hubei Electric Power Company,Wuhan 430077,China)
出处
《电测与仪表》
北大核心
2019年第6期87-92,共6页
Electrical Measurement & Instrumentation
