摘要
针对当前电力大数据背景下用户用电行为分析对尖峰负荷特性挖掘不足的问题,提出一种考虑尖峰负荷特性指标的用户用电行为分析方法。首先,对尖峰负荷定义及尖峰负荷特性指标进行说明,并根据尖峰负荷特性指标构建尖峰特性特征集;然后,使用K-means算法对特征集进行聚类并获取聚类结果标签,使用轮廓系数评估不同类别的聚类性能;最后对不同标签用户用电特性进行分析。采用美国国家可再生能源实验室开源用户用电数据进行仿真计算,计算结果表明,使用尖峰特性特征集较原始用户数据集具有更好的聚类效果。
Aiming at the insufficient mining of peak load characteristics by user power consumption behavior analysis under the background of current electric power big data, an analysis method considering peak load characteristic indicators is proposed. Firstly, the definition of peak load and peak load characteristic index are explained, and the peak characteristic feature set is constructed according to peak load characteristic index. Then, K-means algorithm is used to cluster the feature set and obtain clustering result labels, and evaluate different clustering performances of different categories by contour coefficient. Finally, power consumption characteristics of different label users are analyzed. Simulation calculation is carried out by using open-source user electricity data of National Renewable Energy Laboratory. The results show that the use of peak characteristic feature set has a better clustering effect than the original user data set.
作者
赵爽
阮俊枭
支刚
吴政声
万航羽
王志敏
刘民伟
ZHAO Shuang;RUAN Junxiao;ZHI Gang;WU Zhengsheng;WAN Hangyu;WANG Zhimin;LIU Minwei(Power Grid Planning and Construction Research Center of Yunnan Power Grid Co.,Ltd,Kunming 650000,China;Faculty of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650500,China;China Energy Construction Group Yunnan Electric Power Design Institute Co.,Ltd,Kunming 650000,China)
出处
《内蒙古电力技术》
2022年第5期39-45,共7页
Inner Mongolia Electric Power
基金
国家自然科学基金资助项目“基于数据驱动的综合能源电力系统分布互联建模研究”(52107097)
云南电网公司规划专题科技项目“云南‘十四五’尖峰负荷控制实施路径及配套政策机制研究”(0500002021030203GHJ00053)。
关键词
尖峰负荷
K-MEANS算法
聚类
轮廓系数
用电行为
peak load characteristic index
K-means algorithm
cluster analysis
contour coefficient
power consumption behavior
