基于多时间尺度深度学习的窃电用户检测方法研究

吴若冰; 路辉; 朱昱坤; 冀南囡

吴若冰,路辉,朱昱坤,冀南囡.基于多时间尺度深度学习的窃电用户检测方法研究[J].电测与仪表,2024,61(12):178-184.
WU Ruobing,LU Hui,ZHU Yukun,JI Nannan.Research on user detection method for electricity theft based on multi-time scale deep learning[J].Electrical Measurement & Instrumentation,2024,61(12):178-184.

基于多时间尺度深度学习的窃电用户检测方法研究

Research on user detection method for electricity theft based on multi-time scale deep learning

DOI：10.19753/j.issn1001-1390.2024.12.022

中文关键词: 窃电用户识别方法多时间尺度深度学习方法 AdaBoost分类器

英文关键词:electricity theft user, identification method, multi-time scale, deep learning method, AdaBoost classifier

基金项目:中国南方电网有限责任公司科技项目（YDKJ23030070）

作者中文名	作者英文名	单位
吴若冰	WU Ruobing	1.云南电网有限责任公司信息中心，昆明 650000； 2.云南电网有限责任公司，昆明 650000
路辉	LU Hui	1.云南电网有限责任公司信息中心，昆明 650000； 2.云南电网有限责任公司，昆明 650000
朱昱坤	ZHU Yukun	云南电网有限责任公司，昆明 650000
冀南囡	JI Nannan	哈尔滨电工仪表研究所有限公司，哈尔滨 150028

摘要点击次数: 475

中文摘要:

针对现有窃电用户检测方法存在的检测精度低的问题，在智能电网数据采集系统基础上，提出了一种用于窃电用户检测的多时间尺度深度学习方法。双向长短时记忆网络提取日用电特征，残差网络ResNet提取周用电特征，深度卷积神经网络AlexNet提取月用电特征，AdaBoost分类器对用户是否窃电进行分类。通过算例分析验证所提方法的优越性。结果表明，所提方法充分利用深度学习在特征提取方面的强大能力，以及AdaBoost在分类任务中的高效性，通过不同时间尺度上的用电数据分析，提高了窃电用户检测性能。与常规窃电检测方法相比，所提窃电检测方法能更全面地反应正常用户和窃电用户的特征，在多个指标中具有最优性能，精确率为91.28%，ROC-AUC值为0.950 5，PR-AUC值为0.946 9。所提窃电检测方法不仅有助于减少电能损失，也可为双碳目标的实现提供一定的助力。

英文摘要:

Aiming at the problem of low detection accuracy in existing methods for detecting electricity theft users, a multi-time scale deep learning method for detecting electricity theft users is proposed based on the smart grid data acquisition system. The bidirectional long short-term memory network extracts daily electricity consumption features, the residual network ResNet extracts weekly electricity consumption features, the deep convolutional neural network AlexNet extracts monthly electricity consumption features, and the AdaBoost classifier classifies whether users steal electricity. The superiority of the proposed method is verified through case analysis. The results indicate that, the proposed method fully utilizes the powerful feature extraction capabilities of deep learning and the efficiency of AdaBoost in classification tasks, by analyzing electricity consumption data at different time scales, it improves the detection performance of electricity theft users. Compared with conventional electricity theft detection methods, the proposed method can more comprehensively reflect the characteristics of normal users and electricity theft users, and has the best performance in multiple indicators, with an accuracy rate of 91.28%, ROC-AUC value of 0.950 5, and PR-AUC value of 0.946 9. The proposed method not only helps reduce energy loss, but also provides some assistance for achieving the dual carbon target.

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