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基于时间序列变密度处理的负荷曲线聚类分析

Clustering analysis of load curve based on time series density-changing processing
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摘要 负荷曲线聚类是分析用户负荷特性的基础,能够从大量负荷数据中挖掘典型用电模式,了解用户电力消费的特点,对需求响应、电价设计、电网规划等应用具有重要意义。针对现有聚类方法对负荷时段特征考虑不足的问题,为提升聚类精度和满足实际应用需求,提出一种基于时间序列变密度处理的聚类方法。首先,采用线性插值法增加峰、谷、爬坡等3个关键时段数据点的密度,突出和放大其在聚类中的影响,并基于自适应分段聚合近似(adaptive piecewise aggregate approximation,APAA)降维方法减小冗余数据密度。然后,结合欧式距离和相关距离构建综合指标,对负荷曲线开展k-medoids聚类分析。最后,利用UCI数据集的居民用户实测数据对所提方法进行验证。实验结果表明,该方法能有效改善负荷聚类效果,真实反映了居民用户的用电特性。 Load curve clustering constitutes the foundational methodology for the analysis of customer load characteristics,enabling the extraction of typical power consumption patterns from a substantial volume of load data.Understanding these patterns is pivotal for applications including demand response,tariff design and power grid planning.In light of the inadequacies of existing clustering methodologies,specifically their insufficient consideration of the unique characteristics of load time periods,an advanced clustering technique based on time series density-changing processing is introduced in this study.The proposed method commences with the application of linear interpolation to augment the density of data points during critical periods,including peak,trough and ramp-up periods,thereby accentuating and amplifying their influence within the clustering framework.Concurrently,the technique incorporates the adaptive piecewise aggregate approximation(APPA)for dimensionality reduction to mitigate the density of superfluous data.Subsequently,a comprehensive index,formulated through the integration of Euclidean distance and correlation distance,is employed to conduct k-medoids clustering analysis of the load curves.The effectiveness of the proposed methodology is validated utilizing actual residential customer data from the UCI dataset.The results of these empirical investigations affirm that the method significantly enhances the efficacy of load clustering,thereby providing an authentic representation of the power consumption characteristics of residential customers.
作者 郭文熙 李知艺 尹建兵 陈琳 鞠平 GUO Wenxi;LI Zhiyi;YIN Jianbing;CHEN Lin;JU Ping(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;State Grid Hangzhou Power Supply Company of Zhejiang Electric Power Co.,Ltd.,Hangzhou 310000,China;College of Energy and Electrical Engineering,Hohai University,Nanjing 211100,China)
出处 《电力工程技术》 北大核心 2024年第6期21-32,共12页 Electric Power Engineering Technology
基金 国家电网有限公司科技项目“广义负荷柔性成分的分析、建模及应用验证方法研究”(5108-202218280A-2-445-XG)。
关键词 变密度 负荷聚类 关键时段 常规时段 插值法 自适应分段聚合近似(APAA) density-changing load clustering critical periods ordinary periods interpolation adaptive piecewise ag ̄gre ̄ga ̄te approximation(APPA)
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