摘要
在负荷曲线形态较多时,传统聚类方法对用户负荷分类的效率不高,阻碍了聚类方法在电力负荷大数据分析中的应用。该文提出一种"进化"主成分分析法。首先,采用主成分分析法对用户的负荷特征矩阵进行降维;之后,在主成分分析法的基础上,提出基于欧式距离的分类规则。以某地区用户实际负荷为算例,通过余弦相似定理拟合各类用户曲线形态,验证所提出算法的有效性。经过与传统负荷曲线分类方法的对比,证明了基于"进化"主成分分析法能提升负荷曲线分类效率。在负荷曲线分类的基础上,与当地总体负荷曲线进行对比,将用户负荷分为迎峰用电型、部分迎峰用电型、少量迎峰用电型以及异常用电型4类,分析结果证明了基于"进化"主成分分析法的负荷分类的有效性和实用性。所提出的负荷分类方法可以更加有效地对用户用电行为进行分类,从而针对各类用户制定动态电价,作为开展智能电网相关增值服务的基础。
When there are many kinds of load curves,the efficiency of the traditional clustering method is not high in user load classification,which hinders the application of clustering method in the big data analysis of power load. This paper proposes a ‘Evolution＇principal component analysis（ PCA） method. Firstly,we adopt PCA to reduce the load matrix dimensionality of users; then,proposes the classification rules based on Euclidean distance,on the basis of PCA. Taking the actual load of users in a certain area as an example,all kinds of user curve shapes are fitted by cosine similarity theorem,which verifies the effectiveness of the proposed algorithm. Compared with traditional load curve classification method,it is showed that the ‘Evolution＇-based PCA can improve the classification efficiency of load curve. On the basis of load curve classification,compared with the local overall load curve,the user is divided into 4 categories： peak electricity users,part meeting peak electricity users,a fewmeeting peak electricity and abnormal electric type. The analysis results showthe effectiveness and practicability of the load classification based on‘Evolution＇PCA. The proposed load classification method can be more effective in the classification of user behaviour,so as to establish the dynamic electricity price for all kinds of users,which can be the basis for the development of smart grid related value-added services.
作者
和敬涵
卢育梓
陆金耀
胡波
杨方
何博
HE Jinghan LU Yuzi LU Jinyao HU Bo YANG Fang HE BO(School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China State Grid Energy Research Institute, Beijing 102209, China)
出处
《电力建设》
北大核心
2017年第3期101-107,共7页
Electric Power Construction
基金
国家自然科学基金项目(51277009)
国家电网公司科技项目(52110415000Q)~~
关键词
智能电网
主成分分析(PCA)
用户分类
行为分析
smart grid
principal component analysis（PCA）
user classification
behavior analysis
