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NTL detection of electricity theft and abnormalities for large power consumers In TNB Malaysia

https://doi.org/10.1109/scored.2010.5704002
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What this work says

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

inferredcites →https://doi.org/10.1049/pe:19870056openalex-snapshot
System loss in a metropolitan utility network
inferredcites →https://doi.org/10.1049/pe:20020201openalex-snapshot
Technical loss computation and economic dispatch model for T&D systems in a deregulated ESI
inferredcites →https://doi.org/10.1109/africon.2004.1406768openalex-snapshot
A statistical method to minimize electrical energy losses in a local electricity distribution network
inferredcites →https://doi.org/10.1109/icpst.2006.321964openalex-snapshot
Customer Information System Data Pre-Processing with Feature Selection Techniques for Non-Technical Losses Prediction in an Electricity Market
inferredcites →https://doi.org/10.1109/pecon.2006.346624openalex-snapshot
Load Profiling Method in Detecting non-Technical Loss Activities in a Power Utility
inferredcites →https://doi.org/10.1109/pecon.2008.4762604openalex-snapshot
Non-Technical Loss analysis for detection of electricity theft using support vector machines
inferredcites →https://doi.org/10.1109/pes.2006.1709335openalex-snapshot
Load profiling and data mining techniques in electricity deregulated market
inferredcites →https://doi.org/10.1109/pes.2007.385883openalex-snapshot
A Data Mining Based NTL Analysis Method
inferredcites →https://doi.org/10.1109/psce.2009.4840253openalex-snapshot
Identification and detection of electricity customer behaviour irregularities
inferredcites →https://doi.org/10.1109/tdc.2002.1177814openalex-snapshot
Wavelet based feature extraction and multiple classifiers for electricity fraud detection
inferredcites →https://doi.org/10.1109/tencon.2008.4766403openalex-snapshot
Detection of abnormalities and electricity theft using genetic Support Vector Machines
inferredcites →https://doi.org/10.1109/tpwrd.2009.2030890openalex-snapshot
Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines
inferredcites →https://doi.org/10.1109/tpwrd.2010.2055670openalex-snapshot
Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System
inferredcites →https://doi.org/10.1109/tpwrs.2005.846236openalex-snapshot
Allocation of the Load Profiles to Consumers Using Probabilistic Neural Networks
inferredcites →https://doi.org/10.1109/tpwrs.2008.926431openalex-snapshot
Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method

What points at this work

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

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Electricity Theft Identification by Tracking Usage Data Utilizing Support Vector Machine Algorithm
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A Novel Framework for NTL Detection in Electric Distribution Systems
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Smart Meter Data Analysis for Power Theft Detection
inferred← citeshttps://doi.org/10.1007/978-981-19-4971-5_38openalex-snapshot
Electricity Theft Detection Methods and Analysis Using Machine Learning: Overview
inferred← citeshttps://doi.org/10.1007/978-981-96-3644-0_16openalex-snapshot
IOT-Based Electricity Theft Detection
inferred← citeshttps://doi.org/10.1007/s00202-021-01409-zopenalex-snapshot
Methodological development for the optimisation of electricity cost in cement factories: the use of artificial intelligence in process variables
inferred← citeshttps://doi.org/10.1007/s11831-024-10137-zopenalex-snapshot
AI Techniques in Detection of NTLs: A Comprehensive Review
inferred← citeshttps://doi.org/10.1007/s13369-020-04464-zopenalex-snapshot
A Blockchain-Based Solution to Control Power Losses in Pakistan
inferred← citeshttps://doi.org/10.1016/j.cose.2016.11.009openalex-snapshot
NFD: Non-technical loss fraud detection in Smart Grid
inferred← citeshttps://doi.org/10.1016/j.energy.2017.07.008openalex-snapshot
Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis
inferred← citeshttps://doi.org/10.1016/j.enpol.2018.09.019openalex-snapshot
Electricity theft: Analysis of the underlying contributory factors in Ghana
inferred← citeshttps://doi.org/10.1016/j.ijepes.2017.04.005openalex-snapshot
Detection of energy theft and defective smart meters in smart grids using linear regression
inferred← citeshttps://doi.org/10.1016/j.ijepes.2017.09.019openalex-snapshot
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Advanced Automated Module for Smart and Secure City
inferred← citeshttps://doi.org/10.1016/j.rser.2017.05.193openalex-snapshot
Solutions for detection of non-technical losses in the electricity grid: A review
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Study of Sensor Layout Acquisition and Application Analysis within Distribution Room
inferred← citeshttps://doi.org/10.1057/s41284-020-00261-3openalex-snapshot
Social context of energy theft among electricity consumers in Lagos and Ibadan Metropolis, Nigeria
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Gas Turbine Model Parameter Classification During Abnormal Operation Using Support Vector Machine For Maintenance
inferred← citeshttps://doi.org/10.1109/access.2021.3122009openalex-snapshot
Big Data-Driven Detection of False Data Injection Attacks in Smart Meters
inferred← citeshttps://doi.org/10.1109/cencon.2014.6967476openalex-snapshot
Distribution line monitoring system for the detection of power theft using power line communication
inferred← citeshttps://doi.org/10.1109/icabcd49160.2020.9183812openalex-snapshot
Curbing Electricity Theft Using Wireless Technique with Communication Constraints
inferred← citeshttps://doi.org/10.1109/icacce.2015.106openalex-snapshot
Non-Technical Losses in Power System and Monitoring of Electricity Theft over Low-Tension Poles
inferred← citeshttps://doi.org/10.1109/icatmri51801.2020.9398427openalex-snapshot
Security Threats in Cloud Computing Based Smart Grids and its Countermeasures
inferred← citeshttps://doi.org/10.1109/icc.2014.6883385openalex-snapshot
NFD: A practical scheme to detect non-technical loss fraud in smart grid
inferred← citeshttps://doi.org/10.1109/iccubea58933.2023.10392260openalex-snapshot
Power Theft and Energy Fraud Detection
inferred← citeshttps://doi.org/10.1109/icdmw.2017.40openalex-snapshot
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
inferred← citeshttps://doi.org/10.1109/iceeict.2015.7307524openalex-snapshot
Design and fabrication of power consumption network to prevent energy pilferage
inferred← citeshttps://doi.org/10.1109/icpec.2013.6527720openalex-snapshot
Non-Technical Losses in power system: A review
inferred← citeshttps://doi.org/10.1109/icsgce.2018.8556710openalex-snapshot
Detection of Energy Theft and Metering Defects in Advanced Metering Infrastructure Using Analytics
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Energy Theft Detection in Smart Grids: Taxonomy, Comparative Analysis, Challenges, and Future Research Directions
inferred← citeshttps://doi.org/10.1109/micai.2015.30openalex-snapshot
Towards a Framework to Detect and Prevent Non-technical Losses in Power Distribution Based on Data-Mining Techniques and Bayesian Networks
inferred← citeshttps://doi.org/10.1109/saupec/robmech/prasa48453.2020.9041117openalex-snapshot
Detection of electricity theft in low voltage networks using analytics and machine learning
inferred← citeshttps://doi.org/10.1109/saupec60914.2024.10445037openalex-snapshot
Performance evaluation of selected machine learning techniques in the detection of non-technical losses in the distribution system
inferred← citeshttps://doi.org/10.1109/tim.2025.3545839openalex-snapshot
Highly Sensitive Magnetoelectric Sensor for Characteristic Current Detection in Smart Grid
inferred← citeshttps://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
inferred← citeshttps://doi.org/10.1145/3178461.3178484openalex-snapshot
Machine learning Techniques for Energy Theft Detection in AMI
inferred← citeshttps://doi.org/10.11648/j.eas.20200502.12openalex-snapshot
A Technique for Electrical Energy Theft Detection and Location in Low Voltage Power Distribution Systems
inferred← citeshttps://doi.org/10.18280/jesa.540312openalex-snapshot
Detection of Non-Technical Losses in Electric Distribution Network by Applying Machine Learning and Feature Engineering
inferred← citeshttps://doi.org/10.2991/ijcis.2017.10.1.51openalex-snapshot
The Challenge of Non-Technical Loss Detection Using Artificial Intelligence: A Survey
inferred← citeshttps://doi.org/10.3390/en12173310openalex-snapshot
Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach
inferred← citeshttps://doi.org/10.3390/en14238029openalex-snapshot
Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid
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inferred← citeshttps://doi.org/10.3844/ajeassp.2018.166.185openalex-snapshot
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Deep Learning Approach for Optimum Power Management Using IoT in EV Battery Management System
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Technical analysis mitigation of electricity theft for domestic and commercial end users
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Electric Power System Monitoring and Theft Detection using Power Line Communication
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