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

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-79728-7_12openalex-snapshot
Towards Energy Efficient Smart Grids: Data Augmentation Through BiWGAN, Feature Extraction and Classification Using Hybrid 2DCNN and BiLSTM
inferredcites →https://doi.org/10.1007/978-3-642-34266-0_16openalex-snapshot
Incentives and Security in Electricity Distribution Networks
inferredcites →https://doi.org/10.1007/s12046-020-01512-0openalex-snapshot
Detection of electricity theft using data processing and LSTM method in distribution systems
inferredcites →https://doi.org/10.1016/j.agwat.2020.106649openalex-snapshot
A hybrid CNN-GRU model for predicting soil moisture in maize root zone
inferredcites →https://doi.org/10.1016/j.apenergy.2019.01.076openalex-snapshot
A practical feature-engineering framework for electricity theft detection in smart grids
inferredcites →https://doi.org/10.1016/j.bspc.2018.08.035openalex-snapshot
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
inferredcites →https://doi.org/10.1016/j.compeleceng.2021.107039openalex-snapshot
Stacked recurrent neural network for botnet detection in smart homes
inferredcites →https://doi.org/10.1016/j.compeleceng.2021.107329openalex-snapshot
Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets
inferredcites →https://doi.org/10.1016/j.compeleceng.2021.107389openalex-snapshot
An incremental Optimum-Path Forest classifier and its application to non-technical losses identification
inferredcites →https://doi.org/10.1016/j.epsr.2020.106904openalex-snapshot
Ensemble machine learning models for the detection of energy theft
inferredcites →https://doi.org/10.1016/j.ijepes.2020.106448openalex-snapshot
Electricity theft detection based on stacked sparse denoising autoencoder
inferredcites →https://doi.org/10.1016/j.ijepes.2020.106544openalex-snapshot
Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM
inferredcites →https://doi.org/10.1016/j.jpdc.2021.03.002openalex-snapshot
An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids
inferredcites →https://doi.org/10.1016/j.patcog.2020.107320openalex-snapshot
Training bidirectional generative adversarial networks with hints
inferredcites →https://doi.org/10.1016/j.trc.2020.102674openalex-snapshot
Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values
inferredcites →https://doi.org/10.1109/access.2019.2891315openalex-snapshot
Drift-Aware Methodology for Anomaly Detection in Smart Grid
inferredcites →https://doi.org/10.1109/access.2019.2943667openalex-snapshot
Sample Efficient Home Power Anomaly Detection in Real Time Using Semi-Supervised Learning
inferredcites →https://doi.org/10.1109/access.2020.3034113openalex-snapshot
ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load
inferredcites →https://doi.org/10.1109/access.2021.3056566openalex-snapshot
Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks
inferredcites →https://doi.org/10.1109/access.2021.3092645openalex-snapshot
Using GANCNN and ERNET for Detection of Non Technical Losses to Secure Smart Grids
inferredcites →https://doi.org/10.1109/icmla.2016.0052openalex-snapshot
Identifying Nontechnical Power Loss via Spatial and Temporal Deep Learning
inferredcites →https://doi.org/10.1109/jiot.2019.2903312openalex-snapshot
Energy Theft Detection With Energy Privacy Preservation in the Smart Grid
inferredcites →https://doi.org/10.1109/tetc.2013.2274043openalex-snapshot
CONSUMER: A Novel Hybrid Intrusion Detection System for Distribution Networks in Smart Grid
inferredcites →https://doi.org/10.1109/tii.2017.2785963openalex-snapshot
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
inferredcites →https://doi.org/10.1109/tii.2018.2873814openalex-snapshot
A Novel Combined Data-Driven Approach for Electricity Theft Detection
inferredcites →https://doi.org/10.1109/tnnls.2020.2994116openalex-snapshot
Nontechnical Losses Detection Through Coordinated BiWGAN and SVDD
inferredcites →https://doi.org/10.1109/tpwrs.2017.2721435openalex-snapshot
Non-Technical Losses Reduction by Improving the Inspections Accuracy in a Power Utility
inferredcites →https://doi.org/10.1109/tpwrs.2018.2853162openalex-snapshot
NTL Detection in Electric Distribution Systems Using the Maximal Overlap Discrete Wavelet-Packet Transform and Random Undersampling Boosting
inferredcites →https://doi.org/10.1109/tpwrs.2019.2943115openalex-snapshot
Hybrid Deep Neural Networks for Detection of Non-Technical Losses in Electricity Smart Meters
inferredcites →https://doi.org/10.1109/tsg.2015.2425222openalex-snapshot
Electricity Theft Detection in AMI Using Customers’ Consumption Patterns
inferredcites →https://doi.org/10.1109/tsg.2016.2560801openalex-snapshot
On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
inferredcites →https://doi.org/10.1109/tsg.2018.2807925openalex-snapshot
Detection of Non-Technical Losses Using Smart Meter Data and Supervised Learning
inferredcites →https://doi.org/10.1109/tsg.2019.2892595openalex-snapshot
Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing
inferredcites →https://doi.org/10.1109/tsg.2020.3047864openalex-snapshot
Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids
inferredcites →https://doi.org/10.1145/1541880.1541882openalex-snapshot
Anomaly detection
inferredcites →https://doi.org/10.1155/2019/4136874openalex-snapshot
Electricity Theft Detection in Power Grids with Deep Learning and Random Forests
inferredcites →https://doi.org/10.3390/app10093151openalex-snapshot
Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters
inferredcites →https://doi.org/10.3390/electronics8080860openalex-snapshot
Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan
inferredcites →https://doi.org/10.3390/en12173310openalex-snapshot
Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach
inferredcites →https://doi.org/10.3390/en12183452openalex-snapshot
Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid
inferredcites →https://doi.org/10.3390/en13174291openalex-snapshot
Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder
inferredcites →https://doi.org/10.48550/arxiv.1406.2661openalex-snapshot
Generative Adversarial Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1109/access.2022.3171229openalex-snapshot
Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids
inferred← citeshttps://doi.org/10.1109/access.2022.3215532openalex-snapshot
RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model
inferred← citeshttps://doi.org/10.1109/access.2022.3221818openalex-snapshot
Pattern Recognition Based Auto-Reclosing Scheme Using Bi-Directional Long Short-Term Memory Network
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Unbalanced Data Handling Techniques for Classifying Energy Theft and Defective Meters in the Provincial Electricity Authority of Thailand
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RMCW: An Improved Residual Network With Multi-Channel Weighting for Machinery Fault Diagnosis
inferred← citeshttps://doi.org/10.1109/cinti-macro57952.2022.10029542openalex-snapshot
A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids
inferred← citeshttps://doi.org/10.1109/eee-am66675.2025.11473798openalex-snapshot
Supervised Contrastive ResNet for Electricity Theft Detection in Smart Grid
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Detecting Non-Technical Losses in the Energy Sector using MLPGRU: An Anomaly Detection Approach
inferred← citeshttps://doi.org/10.1109/iecon49645.2022.9968378openalex-snapshot
Deep Learning with Recurrent Expansion for Electricity Theft Detection in Smart Grids
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Multiverse Recurrent Expansion With Multiple Repeats: A Representation Learning Algorithm for Electricity Theft Detection in Smart Grids
inferred← citeshttps://doi.org/10.31219/osf.io/abkjwopenalex-snapshot
A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids
inferred← citeshttps://doi.org/10.3389/fenef.2026.1853133openalex-snapshot
Artificial intelligence-based efficient model for the detection of non-technical losses in smart grids
inferred← citeshttps://doi.org/10.3389/fenrg.2024.1383090openalex-snapshot
Electricity theft detection in smart grid using machine learning
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Non-Hardware-Based Non-Technical Losses Detection Methods: A Review
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