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Electricity Theft Detection in Smart Grids Based on Deep Neural Network
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inferred← citeshttps://doi.org/10.1002/9781394193769.ch8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-64608-9_5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-031-96146-5_4openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-032-02312-4_27openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-032-28573-7_25openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-95-9207-4_33openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-96-8104-4_47openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-96-8283-6_14openalex-snapshot
Enhancing Smart Grid Reliability Through Anomaly Detection and Theft Detection
inferred← citeshttps://doi.org/10.1007/978-981-99-5091-1_5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00202-024-02342-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00202-024-02429-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00202-025-03044-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s13198-024-02333-8openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s13369-025-10416-2openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.apenergy.2025.126632openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.egyr.2023.09.100openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.egyr.2024.08.068openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.egyr.2026.109694openalex-snapshot
Smart grid security: A deep neural network model to detect and mitigate electricity theft
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111111openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111333openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.epsr.2025.111503openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eswa.2025.129425openalex-snapshot
TFSM: A network for time-frequency synergistic modeling integrating Mamba temporal pathway and spectral features for electricity theft detection
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e35167openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.ijepes.2024.109848openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.iotcps.2025.05.002openalex-snapshot
Advanced Machine Learning in Smart Grids: An overview
inferred← citeshttps://doi.org/10.1016/j.matcom.2023.04.023openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.mlwa.2023.100458openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.prime.2024.100452openalex-snapshot
CNN-AdaBoost based hybrid model for electricity theft detection in smart grid
inferred← citeshttps://doi.org/10.1016/j.rineng.2025.104478openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.rineng.2025.105125openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.rser.2023.113544openalex-snapshot
Systematic review of energy theft practices and autonomous detection through artificial intelligence methods
inferred← citeshttps://doi.org/10.1016/j.segan.2022.100833openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.segan.2025.101808openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.suscom.2025.101175openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.tej.2026.107530openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.xcrp.2026.103327openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-023-48011-wopenalex-snapshot
Practical prototype for energy management system in smart microgrid considering uncertainties and energy theft
inferred← citeshttps://doi.org/10.1038/s41598-025-26009-wopenalex-snapshot
Improving efficiency in smart grid monitoring using hybrid classification and dimensionality reduction
inferred← citeshttps://doi.org/10.1038/s41598-025-93140-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1049/gtd2.13056openalex-snapshot
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inferred← citeshttps://doi.org/10.1049/rpg2.12785openalex-snapshot
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inferred← citeshttps://doi.org/10.1049/smt2.12133openalex-snapshot
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inferred← citeshttps://doi.org/10.1049/tje2.70216openalex-snapshot
Anomaly Detection in a Smart Grid Using a Novel Hybrid Deep Learning Approach
inferred← citeshttps://doi.org/10.1080/17455030.2023.2239368openalex-snapshot
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inferred← citeshttps://doi.org/10.1088/2631-8695/ae6153openalex-snapshot
GRMC: a calendar-based gated residual Mamba classifier for interpretable electricity theft detection on low-resolution daily meter data
inferred← citeshttps://doi.org/10.1093/ijlct/ctae147openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/ijlct/ctaf129openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2022.3215532openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2023.3284681openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2023.3326724openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2024.3358683openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2024.3477714openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2024.3498733openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/access.2025.3526764openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/aibthings63359.2024.10863656openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/areev64307.2025.10925179openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cybercom63683.2024.10803231openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/delcon64804.2024.10866405openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/eee-am66675.2025.11473798openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/eeeic/icpseurope54979.2022.9854628openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/eespe68405.2026.11649237openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/epdc62178.2024.10571723openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/fmec65595.2025.11119354openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ic_aset61847.2024.10596174openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icccnt61001.2024.10724504openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iccct63501.2025.11019827openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/iccpct61902.2024.10672962openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icdsca67083.2025.11350252openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icicacs68679.2026.11578902openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icicnct66124.2025.11232695openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icicnct66124.2025.11232927openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icima68728.2026.11564165openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icima68728.2026.11564287openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icirca65293.2025.11089641openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ickecs65700.2025.11035164openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icrito56286.2022.9965151openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ne-iecce64154.2025.11183183openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tce.2025.3581699openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tencon61640.2024.10902996openalex-snapshot
Detecting and Localizing Electricity Pilferage Using Deep Neural Networks in a Low Voltage Distribution Network with both Smart and Traditional Meters
inferred← citeshttps://doi.org/10.1109/tifs.2023.3265884openalex-snapshot
ETD-ConvLSTM: A Deep Learning Approach for Electricity Theft Detection in Smart Grids
inferred← citeshttps://doi.org/10.1109/tsg.2025.3546219openalex-snapshot
Self-Supervised Latent Feature-Guided Multi-Step Diffusion Model for Electricity Theft Detection With Imbalanced and Missing Data
inferred← citeshttps://doi.org/10.1117/12.2685710openalex-snapshot
A method for the detection of electricity theft behavior based on Xavier weight initialization
inferred← citeshttps://doi.org/10.1155/2024/5566402openalex-snapshot
Enhancing Smart City Functions through the Mitigation of Electricity Theft in Smart Grids: A Stacked Ensemble Method
inferred← citeshttps://doi.org/10.1186/s41601-023-00319-5openalex-snapshot
Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
inferred← citeshttps://doi.org/10.1186/s42162-026-00639-4openalex-snapshot
Identification model of distribution equipment insulation aging enhancement based on SCADA knowledge graph
inferred← citeshttps://doi.org/10.14569/ijacsa.2022.0131096openalex-snapshot
Evaluation of Online Machine Learning Algorithms for Electricity Theft Detection in Smart Grids
inferred← citeshttps://doi.org/10.17531/ein/226532openalex-snapshot
Reliability-Oriented Diagnosis of Non-Technical Losses in Electricity Distribution Networks Using Real AMR Data
inferred← citeshttps://doi.org/10.2139/ssrn.5358575openalex-snapshot
Non-Payment of Electricity Services in South Africa: Analysing the Consumers, Utilities, and Institutions
inferred← citeshttps://doi.org/10.2139/ssrn.5493575openalex-snapshot
Scalable Energy Theft Detection in Analog Metering Systems: A Hybrid Machine Learning Framework Integrating Prophet, Isolation Forest, and LSTM Autoencoders
inferred← citeshttps://doi.org/10.2139/ssrn.6628551openalex-snapshot
A Proposed New Methodology of Fault Prediction, Prevention and Optimization in a Multi-Source Smart Grid
inferred← citeshttps://doi.org/10.2139/ssrn.6628552openalex-snapshot
A Proposed New Methodology of Fault Prediction, Prevention and Optimization in a Multi-Source Smart Grid
inferred← citeshttps://doi.org/10.23919/indiacom66777.2025.11115831openalex-snapshot
Temporal Convolutional Networks for Electricity Theft Detection With Limited Data
inferred← citeshttps://doi.org/10.2478/amns-2024-0850openalex-snapshot
Electric Theft Detection Based on Multilayer Backpropagation Neural Network Optimized by Sine Chaotic Genetic Algorithm
inferred← citeshttps://doi.org/10.3233/jhs-230002openalex-snapshot
Decision tree-based prediction approach for improving stable energy management in smart grids
inferred← citeshttps://doi.org/10.32604/cmc.2023.042873openalex-snapshot
RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids
inferred← citeshttps://doi.org/10.32604/cmc.2025.067818openalex-snapshot
A Fog-Based Approach for Theft Detection and Zero-Day Attack Prevention in Smart Grid Systems
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.2022.988183openalex-snapshot
An improved deep learning algorithm in enabling load data classification for power system
inferred← citeshttps://doi.org/10.3389/fenrg.2023.1287413openalex-snapshot
A deep learning technique Alexnet to detect electricity theft in smart grids
inferred← citeshttps://doi.org/10.3389/fenrg.2024.1383090openalex-snapshot
Electricity theft detection in smart grid using machine learning
inferred← citeshttps://doi.org/10.3390/app13095710openalex-snapshot
Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
inferred← citeshttps://doi.org/10.3390/app14219897openalex-snapshot
XAI-Based Accurate Anomaly Detector That Is Robust Against Black-Box Evasion Attacks for the Smart Grid
inferred← citeshttps://doi.org/10.3390/axioms14040273openalex-snapshot
Detecting Benford’s Law Effectiveness Threshold Differences According to Affecting Operation
inferred← citeshttps://doi.org/10.3390/computers14080321openalex-snapshot
Electric Network Frequency as Environmental Fingerprint for Metaverse Security: A Comprehensive Survey
inferred← citeshttps://doi.org/10.3390/electricity5020017openalex-snapshot
Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review
inferred← citeshttps://doi.org/10.3390/electronics15040874openalex-snapshot
TGCformer: A Transformer-Based Dual-Channel Fusion Framework for Power Load Anomaly Detection
inferred← citeshttps://doi.org/10.3390/en15186799openalex-snapshot
Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions
inferred← citeshttps://doi.org/10.3390/en16217460openalex-snapshot
Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review
inferred← citeshttps://doi.org/10.3390/en17010087openalex-snapshot
A Method for the Characterization of the Energy Demand Aggregate Based on Electricity Data Provided by AMI Systems and Metering in Substations
inferred← citeshttps://doi.org/10.3390/en17123057openalex-snapshot
Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review
inferred← citeshttps://doi.org/10.3390/en18102435openalex-snapshot
An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids
inferred← citeshttps://doi.org/10.3390/en18123156openalex-snapshot
Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks
inferred← citeshttps://doi.org/10.3390/en19173989openalex-snapshot
Mean Shift Test-Based Identification of Severe Missing Events in Low-Voltage Distribution Networks
inferred← citeshttps://doi.org/10.3390/math11183863openalex-snapshot
Benford’s Law in Electric Distribution Network
inferred← citeshttps://doi.org/10.3390/s22114048openalex-snapshot
GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection
inferred← citeshttps://doi.org/10.3390/s22207818openalex-snapshot
Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing
inferred← citeshttps://doi.org/10.3390/s24041148openalex-snapshot
Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns
inferred← citeshttps://doi.org/10.3390/s24103236openalex-snapshot
Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks
inferred← citeshttps://doi.org/10.36227/techrxiv.171177491.13025779/v1openalex-snapshot
Synthetic Minority Oversampling Technique Enhanced Machine Learning Models for Energy Theft Detection
inferred← citeshttps://doi.org/10.36227/techrxiv.171177491.13025779/v2openalex-snapshot
Synthetic Minority Oversampling Technique Enhanced Machine Learning Models for Energy Theft Detection
inferred← citeshttps://doi.org/10.37936/ecti-cit.2024181.252738openalex-snapshot
Feature Extraction of Risk Group and Electricity Theft by using Electrical Profiles and Physical Data for Classification in the Power Utilities
inferred← citeshttps://doi.org/10.5121/ijaia.2024.15304openalex-snapshot
Systematic Review of Models Usedto Handle Class Imbalance in Anomaly Detection for Energy Consumption
inferred← citeshttps://doi.org/10.52589/bjcnit-k4pvqdakopenalex-snapshot
Review on Temporal Convolutional Networks for Electricity Theft Detection with Limited Data
inferred← citeshttps://doi.org/10.53759/7669/jmc202505177openalex-snapshot
A Novel Deep Adaptive Feature Learning Framework for Efficient Electricity Theft Detection in Smart Grids
inferred← citeshttps://doi.org/10.55041/ijsrem18597openalex-snapshot
Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm
inferred← citeshttps://doi.org/10.62381/i245b05openalex-snapshot
Research on Electricity Theft Detection Technology Based on Deep Learning
inferred← citeshttps://doi.org/10.62520/fujece.1551601openalex-snapshot
Machine Learning-Based Detection of Non-Technical Losses in Power Distribution Networks
inferred← citeshttps://doi.org/10.7717/peerj-cs.1840openalex-snapshot
Security risk models against attacks in smart grid using big data and artificial intelligence
inferred← citeshttps://doi.org/10.7717/peerj.15313openalex-snapshot
Topological feature generation for link prediction in biological networks
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