基于大数据聚合的电力用户行为实时云监测方法
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引用本文:朱 克1,张 莉2,王笑一2,张 浩3,李 玮2.基于大数据聚合的电力用户行为实时云监测方法[J].计算技术与自动化,2022,(4):173-178
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朱 克1,张 莉2,王笑一2,张 浩3,李 玮2 (1.国家电网有限公司营销部, 北京 1000312.国家电网有限公司客户服务中心天津 3003003.北京中电普华信息技术有限公司北京 100031) 
中文摘要:为提升电力用户行为监测效果及准确性,判断电力用户异常行为,提出一种基于大数据聚合的电力用户行为实时云监测方法。该方法将基础设施及终端等获取的电力用户行为大数据储存至数据层的关系数据库内,处理层调用数据层存储电力用户行为大数据,采用大数据处理技术,通过数据降维、清洗以及标准化处理后,提升电力用户行为大数据质量;应用层采用改进流数据聚类算法,通过用户及簇典型曲线提取、曲线相似度度量,实现用户用电行为异常监测,并通过显示层云展现监测结果。实验结果证明,该方法的数据聚类质量高,可以有效获取电力用户行为监测结果,判断电力用户是否存在异常行为,具备较高监测准确性。
中文关键词:大数据聚合  电力用户行为  实时云监测  M-BIRCH算法  相似度度量  典型曲线
 
Real-time Cloud Monitoring Method for Power User Behavior Based on Big Data Aggregation
Abstract:In order to improve the effect and accuracy of power user behavior monitoring and judge the abnormal behavior of power users, a real-time cloud monitoring method of power user behavior based on big data aggregation is proposed. The big data of power user behavior obtained by infrastructure and terminals are stored in the relational database of the data layer. The processing layer calls the data layer to store the big data of power user behavior. The big data processing technology is adopted to improve the quality of big data of power user behavior through data dimensionality reduction, cleaning and standardization; The application layer adopts the improved stream data clustering algorithm, realizes the abnormal monitoring of users' power consumption behavior through the extraction of typical curves of users and clusters and the measurement of curve similarity, and displays the monitoring results through the display of layer cloud. The experimental results show that this method has high data clustering quality, can effectively obtain the behavior monitoring results of power users, judge whether there is abnormal behavior of power users, and has high monitoring accuracy.
keywords:big data aggregation  power user behavior  real-time cloud monitoring  M-BIRCH algorithm  similarity measure  typical curve
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