知识引导和无监督学习混合驱动的电力用户用电异常辨析方法

吕伟嘉; 李晓辉; 滕永兴; 陈娟; 门英君; 杜天硕

doi:10.3969/j.issn.1008-0198.2025.04.011

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知识引导和无监督学习混合驱动的电力用户用电异常辨析方法

电网运行与控制 | 更新时间：2025-12-08

- 知识引导和无监督学习混合驱动的电力用户用电异常辨析方法
- A Hybrid-Driven Method of Knowledge Guidance and Unsupervised Learning for Power Users Anomaly Discrimination
- 湖南电力 2025年45卷第4期页码：74-82
- 作者机构：
  
  1. 国网天津市电力公司营销服务中心,天津,300210
  2. 国网天津市电力公司,天津,300010
  3. 天津大学,天津,300072
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.04.011
  中图分类号： TM714
- 网络出版：2025-09-05，
  
  纸质出版：2025-09-05
- 稿件说明：
移动端阅览
吕伟嘉, 李晓辉, 滕永兴, 陈娟, 门英君, 杜天硕. 知识引导和无监督学习混合驱动的电力用户用电异常辨析方法[J]. 湖南电力, 2025, 45(4): 74-82.

LYU Weijia, LI Xiaohui, TENG Yongxing, et al. A Hybrid-Driven Method of Knowledge Guidance and Unsupervised Learning for Power Users Anomaly Discrimination[J]. 2025, 45(4): 74-82.
吕伟嘉, 李晓辉, 滕永兴, 陈娟, 门英君, 杜天硕. 知识引导和无监督学习混合驱动的电力用户用电异常辨析方法[J]. 湖南电力, 2025, 45(4): 74-82. DOI： 10.3969/j.issn.1008-0198.2025.04.011.

LYU Weijia, LI Xiaohui, TENG Yongxing, et al. A Hybrid-Driven Method of Knowledge Guidance and Unsupervised Learning for Power Users Anomaly Discrimination[J]. 2025, 45(4): 74-82. DOI： 10.3969/j.issn.1008-0198.2025.04.011.

摘要

针对基于规则的传统电力用户异常检测方法存在人工标注成本高、数据特征表征能力有限等问题

提出一种知识引导与无监督学习融合的电力用户异常检测混合模型。首先

基于用电行为分析构建包含负荷率、峰谷差率等特征的多维负荷特性指标体系

运用主成分分析实现高维数据的特征提取与可视化呈现;进而通过重建误差和局部离群因子算法分别量化用户用电模式的异常偏离程度与空间分布异常概率。其次对用户用电行为的异常度和疑似概率进行排序

只需检测少数异常度高的用户即可发现大部分异常用户。最后

在算例分析中分别通过小型和大型数据集对所提方法进行验证。结果表明

该方法的AUC值可达0.825 1

不仅能有效识别出异常用户

还能降低误报率

提高检测准确性

为电力公司提供一个高效、可靠的用电异常辨析工具。

Abstract

In view of the problems of high manual annotation cost and limited data feature representation ability of traditional rule-based detection methods

a hybrid model for anomaly detection of power users that integrates knowledge guidance and unsupervised learning is proposed. First

a multi-dimensional load characteristic index system containing load rate

peak-to-valley difference rate and other features is constructed based on power consumption behavior analysis

and principal component analysis is used to realize feature extraction and visualization of high-dimensional data. Then

the abnormal deviation degree of users' power consumption modes and abnormal probability of spatial distribution are quantified by reconstruction error and local outlier factor algorithm respectively. Secondly

the abnormality degree and suspected probability of users' power consumption behavior are ranked

so that most abnormal users can be found by detecting only a few users with high abnormality degree. Finally

the proposed method is verified by small and large data sets respectively in the case analysis. The results show that the AUC score of this method can reach 0.8251

which has significant superiority. In addition

this method can not only effectively identify abnormal users

but also reduce the false alarm rate and improve the accuracy of detection

providing an efficient and reliable power consumption anomaly analysis tool for power companies.

关键词

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