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文章摘要
基于高斯核密度估计的典型负荷曲线形态聚类算法
A typical load curve shape clustering algorithm based on Gaussian kernel
Received:February 11, 2020  Revised:February 22, 2020
DOI：10.19753/j.issn1001-1390.2023.02.006
中文关键词: 典型负荷曲线  核密度估计  现货市场  结算  日分时电量
英文关键词: 
基金项目:电力市场化结算电能量数据采集及技术处理研究昆明（2019）070201JS00001
Author NameAffiliationE-mail
yanminghui Kunming power exchange 969627442@qq.com 
xiexiong* Wuhan university tsexiong@qq.com 
liweijie Kunming power exchange 969627442@qq.com 
wudianning Kunming power exchange tsexiong@qq.com 
cuixue wuhan university 969627442@qq.com 
panshucheng wuhan university 969627442@qq.com 
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中文摘要:
      在电力现货市场结算过程中，获取市场化用户的实时电量至关重要。本文聚焦现货市场中非分时计量用户的电量分解，设计了一种利用典型负荷曲线获取分时电量的方法和流程。首先，选取样本用户，对样本用户计量数据进行预处理后得到完整样本典型负荷曲线。然后，文中提出一种基于核密度估计聚类中心的负荷曲线聚类方法，将kmeans算法原有的均值获取聚类中心升级为高斯核密度估计获取最大概率的聚类中心进行迭代计算，并将聚类中心曲线作为典型负荷曲线对不具备分时计量的用户进行日电量划分，划分至以15min为颗粒度的电量进行结算。最后，运用云南省样本用户计量数据，采用传统峰平谷比例分解、传统聚类算法以及本文改进聚类算法获取的典型负荷曲线进行电量的实时分解算例分析，结果显示，本文所提的改进kmeans算法具备更好的分类性能和较好的效率，同时所分解电量具备更高的准确性。
英文摘要:
      
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