Nontechnical losses (NTLs) are estimated to be considerable and increasing every year. Recently, high-resolution measurements from globally laid smart meters have brought deeper insights on users' consumption patterns that can be exploited potentially by NTL detection. However, consumption-pattern-based NTL detection is now facing two major challenges: the inefficiency of harnessing high dimensionality and the severe lack of fraudulent samples. To overcome them, an NTL detection model based on deep learning and anomaly detection is proposed in this article, namely bidirectional Wasserstein GAN and support vector data description-based NTL detector (BSBND). Motivated by the powerful ability of generative adversarial networks (GANs) to learn deep representation from high-dimensional distributions of data, in the BSBND, we utilized a BiWGAN for feature extraction from high-dimensional raw consumption records, and a one-class classifier trained only on benign samples-SVDD-is adopted to map features into judgments. Moreover, a novel alternate coordinating algorithm is proposed to optimize the cooperation between the upstream BiWGAN and the downstream SVDD, and also, an interpreting algorithm is proposed to visualize the basis of each fraudulent judgment. Case studies have demonstrated the superiority of the BSBND over the state of the arts, the powerful feature extraction ability of BiWGAN, and also the effectiveness of the proposed coordinating and interpreting algorithms.

中文翻译：

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通过协调的 BiWGAN 和 SVDD 进行非技术损失检测。

据估计，非技术损失 (NTL) 相当可观，而且每年都在增加。最近，来自全球铺设的智能电表的高分辨率测量对用户的消费模式带来了更深入的了解，这些模式可以被 NTL 检测潜在地利用。然而，基于消费模式的 NTL 检测现在面临两大挑战：利用高维的效率低下和欺诈样本的严重缺乏。为了克服它们，本文提出了一种基于深度学习和异常检测的 NTL 检测模型，即双向 Wasserstein GAN 和基于支持向量数据描述的 NTL 检测器（BSBND）。受生成对抗网络 (GAN) 从高维数据分布中学习深度表征的强大能力的启发，在 BSBND 中，我们利用 BiWGAN 从高维原始消费记录中提取特征，并采用仅在良性样本上训练的一类分类器 SVDD 将特征映射到判断中。此外，提出了一种新颖的替代协调算法来优化上游 BiWGAN 和下游 SVDD 之间的合作，并提出一种解释算法来可视化每个欺诈判断的基础。案例研究证明了 BSBND 优于现有技术、BiWGAN 强大的特征提取能力以及所提出的协调和解释算法的有效性。提出了一种新颖的替代协调算法来优化上游 BiWGAN 和下游 SVDD 之间的合作，并提出一种解释算法来可视化每个欺诈判断的基础。案例研究证明了 BSBND 优于现有技术、BiWGAN 强大的特征提取能力以及所提出的协调和解释算法的有效性。提出了一种新颖的替代协调算法来优化上游 BiWGAN 和下游 SVDD 之间的合作，并提出一种解释算法来可视化每个欺诈判断的基础。案例研究证明了 BSBND 优于现有技术、BiWGAN 强大的特征提取能力以及所提出的协调和解释算法的有效性。