Hybrid-Order Representation Learning for Electricity Theft Detection
Hybrid-Order Representation Learning for Electricity Theft Detection
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DOI：
10.1109/tii.2022.3179243
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发表时间：
2023-02
影响因子：
12.3
通讯作者：
Yuying Zhu;Yang Zhang;Lingbo Liu;Yang Liu;Guanbin Li;Mingzhi Mao;Liang Lin
中科院分区：
文献类型：
--
作者：
Yuying Zhu;Yang Zhang;Lingbo Liu;Yang Liu;Guanbin Li;Mingzhi Mao;Liang Lin
窃电是造成电力系统电能损耗的主要原因，严重损害了供电商的经济效益，威胁着供电安全。然而，由于用电量固有的复杂相关性和周期性，以及大规模数据处理的低效率，准确、高效地检测用电量数据中的异常仍然具有挑战性。现有的方法通常只关注一阶信息，而忽略了能有效建模全局时间相关性的二阶表征学习，便于用电量数据的区别性表征学习。提出了一种新的窃电检测框架--混合顺序表示学习网络(HORLN)。具体地说，将顺序用电量数据转换为包含每周用电量记录的矩阵格式。然后，设计了周间和周内卷积块，以局部到全局的方式捕获多尺度特征。同时，提出了一种自相关建模模块，从自相关矩阵中学习二阶表示，最后与一阶表示相结合来预测用户的异常得分。在真实世界基准上的广泛实验表明，我们的HORLN比最先进的方法更具优势。
Electricity theft is the primary cause of electrical losses in power systems, which severely harms the economic benefits of electricity providers and threatens the safety of the power supply. However, due to the inherent complex correlation and periodicity of electricity consumption and the low efficiency of large-scale data processing, detecting anomalies in electricity consumption data accurately and efficiently remains challenging. Existing methods usually focus on first-order information and ignore the second-order representation learning that can efficiently model global temporal dependency and facilitate discriminative representation learning of electricity consumption data. In this article, we propose a novel electricity theft detection framework named hybrid-order representation learning network (HORLN). Specifically, the sequential electricity consumption data is transformed into the matrix format containing weekly consumption records. Then, an inter-and-intra week convolution block is designed to capture multiscale features in a local-to-global manner. Meanwhile, a self-dependency modeling module is proposed to learn the second-order representations from self-correlation matrices, which are finally combined with the first-order representations to predict the anomaly scores of electricity consumers. Extensive experiments on a real-world benchmark demonstrate the advantages of our HORLN over state-of-the-art methods.