电工技术学报  2019, Vol. 34 Issue (zk1): 368-377    DOI: 10.19595/j.cnki.1000-6753.tces.L80097
电力系统 |
基于优化SAX和带权负荷特性指标的AP聚类用户用电行为分析
李春燕, 蔡文悦, 赵溶生, 余长青, 张谦
输配电装备及系统安全与新技术国家重点实验室(重庆大学) 重庆 400044
Customer Behavior Analysis Based on Affinity Propagation Algorithm with Optimized SAX and Weighted Load Characteristic Indices
Li Chunyan, Cai Wenyue, Zhao Rongsheng, Yu Changqing, Zhang Qian
State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing 400044 China
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摘要 智能电表的推广和安装,使用户侧累积了海量用电数据。特征提取和聚类分析作为有效的数据处理手段,有助于挖掘用电数据中隐藏的宝贵信息,提取用户的用电行为特性。为提取有效直观的负荷特性,本文提出利用优化SAX和带权负荷指标的AP聚类算法,对负荷曲线进行聚类。针对AP聚类复杂度较高的问题,首先利用SAX算法对负荷曲线进行降维并提取特征,利用基于模拟退火粒子群算法,优化确定合理的字符数和状态数;然后结合负荷特性指标,运用改进AP聚类算法,对负荷曲线进行聚类,聚类过程中采用熵权法对负荷特性指标进行客观赋权,避免指标设置的主观性。基于聚类结果,对各类用户的用电行为以及需求响应潜力进行分析。案例分析验证了该算法的高效性和有效性,并可应用于电网公司决策,如负荷预测、异常检测和提供差异化服务等。
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李春燕
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关键词 特征提取AP聚类SAX算法改进粒子群用电行为分析    
Abstract:The installation of smart meters has resulted in the accumulation of massive electricity data at the demand side. Feature extraction and clustering analysis, as effective data processing means, can help utilities to mine the valuable information hidden in the data and extract customer behavior characteristics. In order to extract effective and intuitive load characteristics, this paper proposes a clustering algorithm based on affinity propagation (AP) clustering algorithm with optimized symbolic aggregate approximation (SAX) and weighted load characteristic indices. First, to solve the problem of high complexity, SAX algorithm is applied to reduce the dimension of load curves, and the appropriate symbolic representation scheme is obtained by simulated annealing particle swarm optimization. Then, combined with the load characteristic indices, the improved AP clustering algorithm is utilized to cluster load curves. In addition, to avoid the subjectivity of the index setting, entropy weighting method is used to objectively weight the load characteristic indices. Based on the clustering results, the consumption behavior and demand response potential of different customers are analyzed. Case studies indicate that the proposed methods and approaches is efficient and effective, which can be applied in utilities for decision making, such as load forecasting, anomaly detection, differential services, etc.
Key wordsFeature extraction    affinity propagation    symbolic aggregate approximation    particle swarm optimization    consumption behavior analysis   
收稿日期: 2018-07-10      出版日期: 2019-07-29
PACS: TM769  
基金资助:国家自然科学基金(51477123)资助项目
通讯作者: 王建国,男,1968年生,教授,博士生导师,研究方向为高电压与绝缘技术、高电压试验技术。E-mail:wjg@whu.edu.cn   
作者简介: 蔡文悦,女,1994年生,硕士研究生,研究方向为聚类算法、用户用电行为分析及需求响应。E-mail:linna199404@126.com
引用本文:   
李春燕, 蔡文悦, 赵溶生, 余长青, 张谦. 基于优化SAX和带权负荷特性指标的AP聚类用户用电行为分析[J]. 电工技术学报, 2019, 34(zk1): 368-377. Li Chunyan, Cai Wenyue, Zhao Rongsheng, Yu Changqing, Zhang Qian. Customer Behavior Analysis Based on Affinity Propagation Algorithm with Optimized SAX and Weighted Load Characteristic Indices. Transactions of China Electrotechnical Society, 2019, 34(zk1): 368-377.
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