摘要
用户用电情况随着电网技术发展变得更加复杂,同时产生大量用电特征。以往采用基于神经网络挖掘方法和基于CURE算法的挖掘方法受到噪声数据影响,导致挖掘精准度较低,针对该问题,提出基于k-means聚类算法的用户复杂用电特征挖掘方法。在k-means聚类算法中,研究用户复杂用电特征挖掘原理,并对数据进行清洗、集成、规约变换预处理,避免噪声干扰。利用信息熵原则聚类矩阵规整特征点,根据复杂用电特征,通过簇类决策用电特征点,计算聚类簇之间距离,获取用电特征信息增益,完成用户复杂用电特征挖掘。通过实验对比结果可知,该方法挖掘精准度最高为99%,为用户提供更好优质服务。
With the development of power grid technology,the power consumption of users becomes more complex,and a large number of power consumption characteristics are produced.In the past,the mining methods based on neural network and cure algorithm are affected by noise data,which results in low mining accuracy.To solve this problem,the k-means clustering algorithm is proposed.In the k-means clustering algorithm,we study the mining principle of complex power consumption characteristics of users,and clean,integrate and preprocess the data to avoid noise interference.Using the principle of information entropy to cluster matrix regular feature points,according to the complex power consumption characteristics,the power consumption feature points are determined by cluster class,the distance between clusters is calculated,the power consumption feature information gain is obtained,and the complex power consumption feature mining of users is completed.The experimental results show that the mining accuracy of this method is 99%,which provides better quality services for users.
作者
蒋勇斌
赵炜
曹晶晶
周丹
JIANG Yong-bin;ZHAO Wei;CAO Jing-jing;ZHOU Dan(State Grid Shanghai Jinshan Electric Power Supply Company,Shanghai 200540,China)
出处
《电子设计工程》
2020年第18期11-15,共5页
Electronic Design Engineering
基金
国家电网公司科技项目(520932190025)
国网上海电力公司管理咨询项目(62093216009G)。
关键词
K-MEANS聚类
用户复杂用电
特征
挖掘
k-means clustering
complex power consumption of users
features
mining
作者简介
蒋勇斌(1970-),男,上海人,工程师。研究方向:电力营销。
