摘要
针对目前用户负荷曲线分析方法研究中传统方法在单一用户典型负荷曲线提取以及负荷曲线特征提取的不足,提出了一种基于时间序列数据挖掘的用户负荷曲线分析方法。该方法首先基于分段聚合近似方法对单一用户的负荷曲线降维,并基于符号聚合近似方法对该用户一段时期内的负荷曲线重表达,用符号化序列表示该用户的负荷曲线,提取该用户的典型负荷曲线。然后结合不同用户典型负荷曲线的负荷特性、指标特征和时间序列特征,基于k-means算法对不同用户的典型负荷曲线聚类分析,分析不同类型用户的用电特征。以UCI一个测试数据集进行算例分析,结果表明所提方法能够挖掘出用户的典型用电行为特征,并提升用户负荷曲线分析效率与聚类质量。
There is a shortage of traditional methods for extracting the typical user load curve and the feature extraction of the load curve in the current user load curve analysis method.This paper proposes a user load curve analysis method based on time series data mining.First,the method reduces the dimension of the load curve of a single user based on piecewise aggregate approximation,and re-expresses the load curve of the user for a period based on the symbolic aggregate approximation method,and represents the user load curve with a symbolized sequence to extract the typical load curve of the user.Then,combined with the load characteristics and time series characteristics of the typical load curve of different users,based on the k-means algorithm,the typical load curves of different users are clustered to analyze the power consumption characteristics of different types of users.A UCI test data set is used for case analysis,and the results show that the proposed method can mine the typical power consumption characteristics of users and improve the user load curve analysis efficiency and clustering quality.
作者
唐俊熙
曹华珍
高崇
吴亚雄
石颖
TANG Junxi;CAO Huazhen;GAO Chong;WU Yaxiong;SHI Ying(Grid Planning&Research Center,Guangdong Power Grid Co.,Ltd.,Guangzhou 510080,China;Beijing Tsingsoft Innovation Technology Co.,Ltd.,Beijing 100085,China)
出处
《电力系统保护与控制》
CSCD
北大核心
2021年第5期140-148,共9页
Power System Protection and Control
基金
中国南方电网公司科技项目资助(GDKJXM20173251)。
