• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2017, Vol. 38 ›› Issue (3): 101-.doi: 10.3969/j.issn.1000-7229.2017.03.014

• 智能电网 • 上一篇    下一篇

 基于“进化”主成分分析法的用户分类及其应用

 和敬涵1,卢育梓1,陆金耀1,2,胡波2 ,杨方2 ,何博2   

  1.  1.北京交通大学电气工程学院,北京市 100044;2.国网能源研究院,北京市 102209
  • 出版日期:2017-03-01
  • 作者简介:和敬涵(1964),女,博士,博士生导师,主要从事继电保护、主动配电网等方面的研究工作; 卢育梓(1991),男,硕士研究生,主要从事信息化技术在电力系统中的应用等方面的研究工作; 陆金耀(1990),男,硕士研究生,本文通信作者,主要从事智能电网用户行为、负荷预测等方面的研究工作; 胡波(1985),男,博士,主要从事智能电网、电动汽车充电服务网络、微电网等领域的战略规划研究和管理咨询等方面的研究工作; 杨方(1981),女,博士,高级工程师,主要从事智能电网、电动汽车充电服务网络等方面的研究工作; 何博(1987),男,博士,主要从事智能电网、电动汽车充电设施规划、大数据等方面的研究工作。
  • 基金资助:
     国家自然科学基金项目(51277009);国家电网公司科技项目(52110415000Q)

 User Classification Method Based on ‘Evolution’ PCA and Its Application

 HE Jinghan1,LU Yuzi 1, LU Jinyao1,2,HU Bo2,YANG Fang2, HE Bo2   

  1.  1. School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China;
     
    2. State Grid Energy Research Institute, Beijing 102209, China
  • Online:2017-03-01
  • Supported by:
     Project supported by the National Natural Science Foundation of China(51277009)

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。

 

关键词:  , 智能电网, 主成分分析(PCA), 用户分类, 行为分析

Abstract:  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 few meeting peak electricity and abnormal electric type. The analysis results show the 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.

 

Key words:   smart grid, principal component analysis(PCA), user classification, behavior analysis

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