基于多维特征融合的居民电力消费异质性模式识别研究  

The research of pattern recognition of heterogeneous residential power consumption based on multi⁃dimensional features

作　　者：刘向向 卢婕 周琪 赵文辉 冯颖 LIU Xiangxiang;LU Jie;ZHOU Qi;ZHAO Wenhui;FENG Yin(Power Supply Service Management Center,State Grid Jiangxi Electric Power Co.,Ltd.,Nanchang 330001,China;State Grid Jiangxi Electric Power Co.,Ltd.,Nanchang 330077,China;School of Management and Economics,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]国网江西省电力有限公司供电服务管理中心,南昌330001 [2]国网江西省电力有限公司,南昌330077 [3]北京理工大学管理与经济学院,北京100081

出　　处：《电力需求侧管理》2020年第6期90-95,共6页Power Demand Side Management

基　　金：国网江西省电力有限公司科技项目(52182018001D)。

摘　　要：基于海量数据的居民电力消费行为特征分析与模式识别研究,对电网负荷预测以及需求响应潜力挖掘等有着至关重要的作用,而居民电力消费大规模数据与多维特征的涌现,成为居民电力消费异质性模式识别的难点。研究基于大规模居民电力消费数据,首先采用电力负荷特征分解技术构建特征工程;其次通过因子分析对所构建的多维特征进行融合,并采用聚类算法对居民电力消费模式进行识别;最后以江西省居民电力消费数据为例,分别在居民家庭与台区维度进行实证分析,分别得到4种典型居民电力消费模式,可为电网公司个性化与差异化政策制定,进一步拓展服务的深度和广度提供科学支撑。Research of pattern recognition on resident power consumption behavior based on massive data plays a crucial role in power grid load prediction and demand response potential development.However,the emergence of large⁃scale data and multi⁃dimensional features of residents’electricity consumption has become a difficulty in identifying the heterogeneous pattern of residents'electricity consumption.Firstly,based on the large⁃scale residential electricity consumption data,the power load characteristic decomposition technology is used to construct the feature project.And then,the multi⁃dimensional features constructed are fused by factor analysis,and the clustering algorithm is adopted to identify the residential electricity consumption pattern.Finally,taking Jiangxi province residents’electricity consumption data as an example,an empirical analysis is made in both households and community level,and four typical residents’electricity consumption patterns have been obtained,which can provide scientific support for the grid companies to make tailored and differentiated policies and further expand the depth and breadth of services.

关 键 词：经验模态分解 特征工程 行为模式挖掘 多维特征融合 聚类算法 

分 类 号：TM715[电气工程—电力系统及自动化]