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

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

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Improving performance of transactional memory through machine learning
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A2Cloud‐RF : A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud
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Sequential Model-Based Optimization for General Algorithm Configuration
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A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks
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Hybrid meta-heuristic optimization based home energy management system in smart grid
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Learning from imbalanced data: open challenges and future directions
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A practical feature-engineering framework for electricity theft detection in smart grids
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Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives
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Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification
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/s0031-3203(96)00142-2openalex-snapshot
The use of the area under the ROC curve in the evaluation of machine learning algorithms
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Efficient Global Optimization of Expensive Black-Box Functions
inferredcites →https://doi.org/10.1109/access.2019.2942813openalex-snapshot
Exploiting Nature-Inspired-Based Artificial Intelligence Techniques for Coordinated Day-Ahead Scheduling to Efficiently Manage Energy in Smart Grid
inferredcites →https://doi.org/10.1109/access.2020.3042636openalex-snapshot
An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems
inferredcites →https://doi.org/10.1109/bigdata47090.2019.9006061openalex-snapshot
Gradient boosting decision trees for cyber security threats detection based on network events logs
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
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/isgt.2016.7781159openalex-snapshot
Large-scale detection of non-technical losses in imbalanced data sets
inferredcites →https://doi.org/10.1109/peci.2017.7935765openalex-snapshot
Distilling provider-independent data for general detection of non-technical losses
inferredcites →https://doi.org/10.1109/pesgm.2017.8273852openalex-snapshot
Improved practices in machine learning algorithms for NTL detection with imbalanced data
inferredcites →https://doi.org/10.1109/sciot50840.2020.9250194openalex-snapshot
Decision Tree based Electricity Theft Detection in Smart Grid
inferredcites →https://doi.org/10.1109/smartgridcomm.2018.8587554openalex-snapshot
Short-Term Load Forecasting based on ResNet and LSTM
inferredcites →https://doi.org/10.1109/tii.2016.2543145openalex-snapshot
Decision Tree and SVM-Based Data Analytics for Theft Detection 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/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.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.2020.2973681openalex-snapshot
Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation
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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
inferredcites →https://doi.org/10.1142/s0218001409007326openalex-snapshot
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
inferredcites →https://doi.org/10.1145/2487575.2487629openalex-snapshot
Auto-WEKA
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The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing
inferredcites →https://doi.org/10.1609/aaai.v29i1.9354openalex-snapshot
Initializing Bayesian Hyperparameter Optimization via Meta-Learning
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/e22010010openalex-snapshot
Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids
inferredcites →https://doi.org/10.3390/en12173310openalex-snapshot
Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach
inferredcites →https://doi.org/10.37052/ml.31(1)no1openalex-snapshot
What's In A Name? Malay Seals As Onomastic Sources
inferredcites →https://doi.org/10.4236/jpee.2016.49001openalex-snapshot
Methods and Techniques of Electricity Thieving in Pakistan
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1206.2944openalex-snapshot
Practical Bayesian Optimization of Machine Learning Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1609.06570openalex-snapshot
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

What points at this work

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

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Detection of Non-Technical Losses Using MLP-GRU Based Neural Network to Secure Smart Grids
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Towards Energy Efficient Smart Grids: Data Augmentation Through BiWGAN, Feature Extraction and Classification Using Hybrid 2DCNN and BiLSTM
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Comparative Study of Data Driven Approaches Towards Efficient Electricity Theft Detection in Micro Grids
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Worker Activity Classification Using Multimodal Data Fusion from Wearable Sensors
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Enhancing Smart Grid Reliability Through Anomaly Detection and Theft Detection
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A flexibility architectural design for enhancing energy management and sharing benefits in low-voltage microgrid systems using a big data platform
inferred← citeshttps://doi.org/10.1016/j.egyr.2024.12.041openalex-snapshot
Robust resampling and stacked learning models for electricity theft detection in smart grid
inferred← citeshttps://doi.org/10.1016/j.epsr.2022.107895openalex-snapshot
Online electricity theft detection framework for large-scale smart grid data
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Electricity theft detection using big data and genetic algorithm in electric power systems
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Extremely randomised trees machine learning model for electricity theft detection
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Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids
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Adaptive Data Balancing Method Using Stacking Ensemble Model and Its Application to Non-Technical Loss Detection in Smart Grids
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Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis
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Electricity Theft Detection for Smart Homes: Harnessing the Power of Machine Learning With Real and Synthetic Attacks
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Gang Theft Crime Behavior and Prevention Control System Based on Computer Data Analysis
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Detection of Electricity Theft using a Deep Learning-based Bayesian optimization algorithm and LSTM
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An Exploratory Study of Periodicity Detection Algorithms for Energy Theft Prevention in Smart Grids
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Big Data Analytics for Electricity Theft Detection in Smart Grids
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Anomaly Detection in Billing Data Using Voting Classification
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Optimizing Electricity Theft Detection using Neural Turing Machines and Machine Learning
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Energy Management of Microgrids Using Artificial Intelligent Control: A Review
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Robust Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid
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A Stacked Machine and Deep Learning-Based Approach for Analysing Electricity Theft in Smart Grids
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Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution
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Data Generative Model to Detect the Anomalies for IDS Imbalance CICIDS2017 Dataset
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Detecting Nontechnical Losses in Smart Meters Using a MLP-GRU Deep Model and Augmenting Data via Theft Attacks
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