摘要
当前电能替代潜力用户识别模型一般为结合用户的特定行为分析,识别覆盖范围不全面,导致最终误识率上升,为此提出对于用电行为K-Means聚类的电能替代潜力用户识别方法的设计与分析。根据当前测定,先明确潜力用户特征指标及潜力识别目标,结合用电行为K-Means聚类技术,强化识别覆盖范围,构建用电行为K-Means聚类电能替代潜力用户识别模型,采用自适应集中调度的方式来完成识别处理。测试结果表明:对比于机器学习电能替代用户特征分析技术、数据挖掘电能替代潜力用户自动识别方法,此次设计的用电行为K-Means聚类电能替代潜力用户识别方法最终计算得出的误识率相对较低,这说明该方法的整体识别效率高,精准度高,错误识别的次数得到了进一步控制处理,具有实际的应用价值。
Currently,identification models for potential users of electric energy substitution generally rely on the analysis of specific user behaviors,leading to incomplete coverage and an increase in the final misidentification rate.To address this,a design and analysis of an identification method for potential users of electric energy substitution based on K-Means clustering of electricity consumption behavior is proposed.Based on current measurements,the characteristic indicators of potential users and the objectives of potential identification are first clarified.By combining K-Means clustering technology for electricity consumption behavior,the coverage of identification is enhanced,and a model for identifying potential users of electric energy substitution based on K-Means clustering of electricity consumption behavior is constructed.An adaptive centralized scheduling approach is adopted to complete the identification process.Test results show that,compared to machine learning techniques for analyzing the characteristics of electric energy substitution users and data mining methods for automatically identifying potential users,the proposed identification method based on K-Means clustering of electricity consumption behavior results in a relatively lower misidentification rate.This indicates that the overall identification efficiency and accuracy of this method are high,with the number of misidentifications further controlled and handled,demonstrating practical application value.
作者
左正琴
徐桐
杨圣杰
任海歌
Zuo Zhengqin;Xu Tong;Yang Shengjie;Ren Haige(Tongren Power Supply Bureau,Tongren,Guizhou,China,554300)
出处
《仪器仪表用户》
2025年第3期4-6,9,共4页
Instrumentation
关键词
用电行为
K-MEANS聚类
电能替代
潜力用户
识别方法
用电监测
electricity consumption behavior
K-Means clustering
electric energy substitution
potential users
identification method
electricity monitoring
