基于特征优选和加权聚类的商场用电模式分析
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张美霞(1979-),女,硕士,副教授,主要从事电力系统运行及需求侧管理研究;E-mail:zhangmeixia@shiep.edu.cn

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TM73

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上海市科委地方能力建设计划基金(16020500900);国家电网公司科技项目(52090016002M)


Analysis of power consumption mode for shopping malls based on feature selection and weighted clustering
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    摘要:

    随着用电信息的采集完善,准确的用户用电模式分析将为电力智能化建设提供重要依据,在此背景下,针对用电模式分析中考虑聚类特征类型单一的问题,提出一种考虑多类型特征优选的加权聚类分析方法。首先,将负荷类特征和气象类特征归一化建立待选择特征集合;然后,结合互信息和灰色关联度优选出聚类特征集;最后,采用权重分配的k-means方法对优选特征集合进行聚类,结合负荷曲线分析各用电模式的典型用电行为。通过对上海市某商场用电负荷数据的分析,证明该方法能够减少数据冗余信息的干扰并提升聚类质量。

    Abstract:

    With the eventually improvement of power consumption information collection, the accurate analysis for user power consumption mode will provide an important basis for power intelligent construction. When analyzing power consumption modes, the load is the only clustering feature to be taken into account. Therefore, a weighted clustering analysis method considering multi-type feature selection is proposed. Firstly, the load and meteorological features are normalized to establish a feature set to be selected. Then, the clustering feature set is selected by combining mutual information and grey correlation degree. Finally, the weighted k-means algorithm is utilized to cluster the selected feature sets, and the typical behavior of each power consumption mode is analyzed with the load curve. Through the analysis of the electrical load data of a shopping mall in Shanghai, it is proved that this method can reduce the interference of redundancy information and improve the clustering quality.

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张美霞,李泰杰,杨秀,等.基于特征优选和加权聚类的商场用电模式分析[J].电力科学与技术学报,2021,36(5):137-143.
Zhang Meixia, Li Taijie, Yang Xiu, et al. Analysis of power consumption mode for shopping malls based on feature selection and weighted clustering[J]. Journal of Electric Power Science and Technology,2021,36(5):137-143.

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  • 在线发布日期: 2021-11-16
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