基于多维特征分析与动态定权聚类的电力居民用户分类

Residential electricity users classification based on multidimensional feature analysis and dynamic weighted clustering

中文关键词: 电力居民用户分类  多维负荷数据  用电行为分析  动态聚类  需求响应

英文关键词: classification of electricity residential users  multidimensional load data  electricity consumption behavior analysis  dynamic clustering  demand response

中文摘要:

      由于居民用户用电需求的高度随机性和不规则性,亟需详细的数据分析来定义用户的行为特征,以提供更加合理的用电建议和需求响应潜力。为了进一步挖掘非介入式辨识数据的价值,提出一种基于多维用电行为数据的电力居民用户分类方法。首先通过非介入式智能电表获取居民细粒度用电数据,分析用户的用电行为,寻找到关键用电特征量；接着使用CRITIC权重法自适应配置各指标权重,通过6类聚类评价指标,对4种聚类算法和3个数据距离计算进行对比,实现最优聚类方法和聚类数目的选择。通过某小区实际数据验证了本文所提用电特征量以及定权聚类方法的有效性,将居民用户群体分成用电行为差异明显的两类。

英文摘要:

      Due to the high randomness and irregularity of residential users’electricity demand, detailed data analysis is urgently needed to define the behavior characteristics of users to provide more reasonable electricity suggestions and demand response potential. Based on the fine-grained electricity consumption data and user information of residents, a classification of electricity residential users based on multi- dimensional electricity consumption behavior data is proposed. First of all, the non-intrusive smart meter is used to obtain the fine-grained electricity consumption data of residents；Then the user’s electricity consumption behavior is analyzed, and electricity consumption characteristics are found. Then,the CRITIC weight method is used to adaptively configure the weights of each index, and through the evaluation indicators of 6 types of clusters, 4 kinds of clustering algorithms and 3 data distance calculations are compared to achieve the optimal clustering method and the choice of the number of clusters. The actual data of a residential area are used to verify the power consumption characteristics and the effectiveness of the weight-fixing clustering method proposed in this paper, and the residential user groups are divided into two categories with obvious differences in electricity consumption behavior.