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Machine and Deep Learning Base Stacking Model Using Hybrid Class Imbalanced Technique for Electricity Theft Detection in Smart Grid

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

inferredcites →https://doi.org/10.1002/cpe.6316openalex-extracted

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

inferredcites →https://doi.org/10.1016/j.epsr.2020.106904openalex-extracted

Ensemble machine learning models for the detection of energy theft

inferredcites →https://doi.org/10.1016/j.ijepes.2021.107085openalex-extracted

Convolutional neural network applied to detect electricity theft: A comparative study on unbalanced data handling techniques

inferredcites →https://doi.org/10.1109/access.2021.3113592openalex-extracted

A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

inferredcites →https://doi.org/10.1109/access.2022.3150016openalex-extracted

AlexNet, AdaBoost and Artificial Bee Colony Based Hybrid Model for Electricity Theft Detection in Smart Grids

inferredcites →https://doi.org/10.1109/access.2022.3230952openalex-extracted

Adaptive Data Balancing Method Using Stacking Ensemble Model and Its Application to Non-Technical Loss Detection in Smart Grids

inferredcites →https://doi.org/10.1109/icbslp.2018.8554396openalex-extracted

A Deep Recurrent Neural Network with BiLSTM model for Sentiment Classification

inferredcites →https://doi.org/10.1109/jsac.2013.130714openalex-extracted

A Multi-Sensor Energy Theft Detection Framework for Advanced Metering Infrastructures

inferredcites →https://doi.org/10.1109/tbdata.2017.2723563openalex-extracted

Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid

inferredcites →https://doi.org/10.1109/tii.2017.2785963openalex-extracted

Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

inferredcites →https://doi.org/10.1109/tpwrs.2018.2853162openalex-extracted

NTL Detection in Electric Distribution Systems Using the Maximal Overlap Discrete Wavelet-Packet Transform and Random Undersampling Boosting

inferredcites →https://doi.org/10.1109/tsg.2018.2807925openalex-extracted

Detection of Non-Technical Losses Using Smart Meter Data and Supervised Learning

inferredcites →https://doi.org/10.1109/tsg.2019.2892595openalex-extracted

Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing

inferredcites →https://doi.org/10.1109/tsg.2021.3134018openalex-extracted

A Stacked Machine and Deep Learning-Based Approach for Analysing Electricity Theft in Smart Grids

inferredcites →https://doi.org/10.1155/2021/9933111openalex-extracted

A Hybrid Deep Neural Network for Electricity Theft Detection Using Intelligent Antenna‐Based Smart Meters

inferredcites →https://doi.org/10.3390/en10050584openalex-extracted

Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data

inferredcites →https://doi.org/10.3390/en12173310openalex-extracted

Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach

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