Nontechnical Losses Detection Through Coordinated BiWGAN and SVDD

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-patt...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2021-05, Vol.32 (5), p.1866-1880
Hauptverfasser:	Hu, Tianyu, Guo, Qinglai, Sun, Hongbin, Huang, Tian-En, Lan, Jian
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Anomalies Anomaly detection bidirectional Wasserstein GAN and support vector data \hbox{description-based} NTL detector (BSBND) Consumption Deep learning Feature extraction Gallium nitride generative adversarial network (GAN) Generative adversarial networks Kernel Machine learning nontechnical losses’ (NTLs) detection smart meters support vector data description (SVDD) Support vector machines Task analysis
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