Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

In this paper, we present a hybrid deep learning model that is based on a two-dimensional convolutional neural network (2D-CNN) and a bidirectional long short-term memory network (Bi-LSTM)to detect non-technical losses (NTLs) in smart meters. NTLs occur due to the fraudulent use of electricity. The...

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Bibliographic Details
Main Authors: Muhammad Asif, Orooj Nazeer, Nadeem Javaid, Eman H. Alkhammash, Myriam Hadjouni
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Bidirectional generative adversarial network
convolutional neural network
data augmentation
deep learning
electricity theft detection
feature extraction
Online Access:https://ieeexplore.ieee.org/document/9707774/

Internet

https://ieeexplore.ieee.org/document/9707774/

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