基于用电行为数字特征画像的电力用户两阶段分类方法

doi:10.12204/j.issn.1000-7229.2022.02.009

电力建设 ›› 2022, Vol. 43 ›› Issue (2): 70-80.doi: 10.12204/j.issn.1000-7229.2022.02.009

基于用电行为数字特征画像的电力用户两阶段分类方法

王磊¹(), 刘洋¹^,³(), 李文峰²(), 张杰¹(), 许立雄¹(), 邢哲铭⁴()

1.四川大学电气工程学院,成都市 610065
2.国网河南省电力公司经济技术研究院,郑州市 450052
3.智能电网四川省重点实验室(四川大学),成都市610065
4.大连市大数据中心,辽宁省大连市 116000

收稿日期:2021-07-09 出版日期:2022-02-01 发布日期:2022-03-24
通讯作者: 刘洋 E-mail:wanglei4@stu.scu.edu.cn;yang.liu@scu.edu.cn;809406879@qq.com;zj@stu.scu.edu.cn;xulixiong@163.com;hyfhce123@163.com
作者简介:王磊(1995),男,硕士研究生,研究方向为电力用户画像与电力用户需求响应潜力评估,E-mail: wanglei4@stu.scu.edu.cn;
李文峰 (1985),男,博士,研究方向为电力市场和电能质量评估等,E-mail: 809406879@qq.com;
张杰(1997),男,硕士研究生,研究方向为电力系统数据挖掘与用电行为精细化辨识,E-mail: zj@stu.scu.edu.cn;
许立雄(1982),男,博士,副教授,研究方向为综合能源系统规划运行、电力系统稳定分析及控制等,E-mail: xulixiong@163.com;
邢哲铭(1982),男,学士,研究方向为大数据处理及分析,E-mail: hyfhce123@163.com。
基金资助:
国家电网公司科技项目(5217L021000C)

Two-Stage Power User Classification Method Based on Digital Feature Portraits of Power Consumption Behavior

WANG Lei¹(), LIU Yang¹^,³(), LI Wenfeng²(), ZHANG Jie¹(), XU Lixiong¹(), XING Zheming⁴()

1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China
2. Economic Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450052, China
3. Key Laboratory of Intelligent Electric Power Grid of Sichuan Province (Sichuan University), Chengdu 610065, China
4. Dalian Big Data Center, Dalian 116000, Liaoning Province, China

Received:2021-07-09 Online:2022-02-01 Published:2022-03-24
Contact: LIU Yang E-mail:wanglei4@stu.scu.edu.cn;yang.liu@scu.edu.cn;809406879@qq.com;zj@stu.scu.edu.cn;xulixiong@163.com;hyfhce123@163.com
Supported by:
State Grid Corporation of China Research Program(5217L021000C)

摘要/Abstract

摘要：

对用户开展精细化用电行为画像及分类,是电力企业精准掌握用户用电规律、提升服务水平和市场竞争力的关键因素之一。针对当前电力用户分类研究中用户用电行为画像结果片面、集成学习负荷分类研究中的基分类器冗余问题及负荷类别不平衡问题,提出一种基于用电行为数字特征画像的电力用户两阶段分类算法。第一阶段,提出一种结合谱聚类和集成强基分类器的用户日负荷曲线分类算法:首先,针对集成学习基分类器学习能力弱的不足,提出一种基于改进长短期记忆网络(long short-term memory,LSTM)的强基分类器;其次,针对基分类器冗余问题,提出一种基于最小正则化代理经验风险的优化选择集成策略;然后,提出一种基于密度的高斯过采样方法处理类别不平衡。第二阶段,基于负荷曲线分类结果,构建以日负荷模式发生概率为数字特征的用户用电行为画像,采用谱聚类算法对用户画像实施分类。最后,通过实测用户负荷数据验证了所提方法的有效性。

关键词: 电力用户分类, 数字特征画像, 负荷曲线分类, 类别不平衡, 优化选择集成

Abstract:

Fine power consumption behavior portrait and classification has been one of the key factors for power enterprises to accurately grasp the electricity consumption law of power consumers, improve service level and market competitiveness. To solve the issues of fragmentary portraits of electricity consumption behavior in current power user classification research, base classifier redundancy and class imbalance in ensemble learning load classification, a two-stage power consumer classification method based on digital feature portraits of power consumption behavior is proposed. In the first stage, a classification method for power user daily load curves is proposed combining spectral clustering and integrated strong base classifier. Firstly, a strong base classifier is developed on the basis of LSTM network to improve the weak learning capability of base classifier in ensemble learning. Secondly, an Optimal Selection Ensemble (OSE) strategy based on minimum regularized surrogate empirical risk is proposed to solve the problem of base classifier redundancy. Thirdly, a Density Based Gaussian Synthetic (DBGS) minority over-sampling technique is proposed for class imbalance. In the second stage, the power consumption behavior portraits with daily load-pattern occurrence probability as the digital features are constructed, and the portraits are classified by spectral clustering. Finally, the effectiveness of the proposed method is verified by the measured user load data.

Key words: power user classification, digital feature portrait, load curve classification, class imbalance, optimization selection ensemble

中图分类号:

TM714

王磊, 刘洋, 李文峰, 张杰, 许立雄, 邢哲铭. 基于用电行为数字特征画像的电力用户两阶段分类方法[J]. 电力建设, 2022, 43(2): 70-80.

WANG Lei, LIU Yang, LI Wenfeng, ZHANG Jie, XU Lixiong, XING Zheming. Two-Stage Power User Classification Method Based on Digital Feature Portraits of Power Consumption Behavior[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(2): 70-80.

图/表 13

图1 DBGS算法示意图

Fig.1 Schematic diagram of DBGS algorithm

图2 LSTMP单元内部结构

Fig.2 Internal structure of LSTMP unit

图3 ALN-LSTMP分类模型

Fig.3 ALN-LSTMP classification model

图4 OSE-ALN-LSTMP算法示意图

Fig.4 Schematic diagram of OSE-ALN-LSTMP

图5 最佳聚类数目选择

Fig.5 Selection of optimal number of clusters

图6 超参数λ调节特性

Fig.6 Regulation characteristics of super parameter λ

表1 日负荷曲线分类效果对比

Table 1 Comparison of classification accuracy of algorithms

表2 常用经典分类算法分类效果对比

Table 2 Comparison of classification accuracy of algorithms

图7 负荷曲线分类结果

Fig.7 Results of load curve classification

图8 七类典型负荷模式

Fig.8 Seven typical load patterns

图9 8类典型用户画像

Fig.9 Eight typical user portraits

图10 不同用户分类方法效果对比

Fig.10 Comparison of user classification methods

图11 图A1 用户分类结果高维可视化窗口

Fig.11 Fig.A1 High dimensional visualization window of user classification results

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基于用电行为数字特征画像的电力用户两阶段分类方法

Two-Stage Power User Classification Method Based on Digital Feature Portraits of Power Consumption Behavior

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