摘要
电力用户参与电网侧互动用电和辅助服务已成为国内外关注热点,用户互动用电行为分析是其中一项核心工作。结合自组织映射SOM神经网络和K-means聚类算法,采用一种自组织中心K-means算法用于用户互动用电行为聚类分析,能够实现更加精准识别和快速聚类。首先,对自组织中心K-means算法原理进行分析,说明其与传统聚类算法相比在用电行为聚类分析中的优势;然后,构建峰谷分时电价背景下,基于用户心理学的调节潜力指标,并分析基于负荷数据和调节潜力指标的用户互动用电行为;最后,以某电力公司管辖区域用户的日常负荷数据为研究对象,将基于自组织中心K-means算法的聚类结果与其他传统聚类方法进行对比,证明基于调节潜力指标的自组织中心K-means算法在用户互动用电行为上的精准识别和准确聚类优势。
The participation of power users in grid-side interactive power and auxiliary services has become a hot topic.Analysis of users’ interaction pow er behavior is a core task. Combining self-organizing map SOM neural network and Kmeans clustering algorithm,this paper uses a self-organizing center K-means algorithm for cluster analysis of users’ interaction electricity behavior and it can achieve more accurate recognition and fast clustering. Firstly,the principle of Kmeans algorithm in self-organizing center is analyzed,which shows its advantages in clustering analysis of electricity usage compared with traditional clustering algorithm. Then,under the background of peak-to-valley time-of-use electricity price,the adjustment potential index based on user psychology is constructed,and the cluster analysis of users’ electricity consumption behavior based on load data and adjustment potential index is analyzed. Finally,the daily load data of users in a jurisdiction of a pow er company is studied and the two-stage clustering results based on self-organizing center K-means algorithm are compared with the clustering results based on K-means algorithm,which proves the advantages of selforganizing center K-means algorithm based on adjustment potential index in the user’s accurate recognition and accurate clustering.
作者
周冰钰
刘博
王丹
兰宇
马喜然
孙冬冬
霍秋屹
ZHOU Bingyu;LIU Bo;WANG Dan;LAN Yu;M A Xiran;SUN Dongdong;HUO Qiuyi;Key Laboratory of Smart Grid of Ministry of Education,Tianjin University;Qingdao Institute for Ocean Technology of Tianjin University(Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China;Qingdao Institute for Ocean Technology of Tianjin University,Qingdao 266235,Shandong Province,China)
出处
《电力建设》
北大核心
2019年第1期68-76,共9页
Electric Power Construction
基金
国家重点研发计划资助项目(2018YFB0905000)
青岛市海洋工程装备与技术智库联合项目(201707071003)~~
