Related references
Note: Only part of the references are listed.Review of non-technical loss detection methods
George M. Messinis et al.
ELECTRIC POWER SYSTEMS RESEARCH (2018)
Non-Technical Losses Reduction by Improving the Inspections Accuracy in a Power Utility
Juan Ignacio Guerrero et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2018)
Dynamic classifier selection: Recent advances and perspectives
Rafael M. O. Cruz et al.
INFORMATION FUSION (2018)
A Novel Fault Classification Scheme for Series Capacitor Compensated Transmission Line Based on Bagged Tree Ensemble Classifier
Praveen Kumar Mishra et al.
IEEE ACCESS (2018)
Ensemble learning for data stream analysis: A survey
Bartosz Krawczyk et al.
INFORMATION FUSION (2017)
Solutions for detection of non-technical losses in the electricity grid: A review
Joaquim L. Viegas et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)
Ensemble classification based on supervised clustering for credit scoring
Hongshan Xiao et al.
APPLIED SOFT COMPUTING (2016)
Social-Spider Optimization-based Support Vector Machines applied for energy theft detection
Danillo R. Pereira et al.
COMPUTERS & ELECTRICAL ENGINEERING (2016)
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
Anish Jindal et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2016)
Conditional abnormality detection based on AMI data mining
Seon Yeong Han et al.
IET GENERATION TRANSMISSION & DISTRIBUTION (2016)
Electricity Theft Detection in AMI Using Customers' Consumption Patterns
Paria Jokar et al.
IEEE TRANSACTIONS ON SMART GRID (2016)
Of pilferers and poachers: Combating electricity theft in India
Tanushree Sharma et al.
ENERGY RESEARCH & SOCIAL SCIENCE (2016)
A new ensemble learning methodology based on hybridization of classifier ensemble selection approaches
R. Mousavi et al.
APPLIED SOFT COMPUTING (2015)
A dynamic and on-line ensemble regression for changing environments
Symone G. Soares et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Using the rough set theory to detect fraud committed by electricity customers
Josif V. Spiric et al.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2014)
Energy-Theft Detection Issues for Advanced Metering Infrastructure in Smart Grid
Rong Jiang et al.
TSINGHUA SCIENCE AND TECHNOLOGY (2014)
Chaos in power: Pakistan's electricity crisis
Ioannis N. Kessides
ENERGY POLICY (2013)
A Multi-Sensor Energy Theft Detection Framework for Advanced Metering Infrastructures
Stephen McLaughlin et al.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2013)
Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction
Myoung-Jong Kim et al.
EXPERT SYSTEMS WITH APPLICATIONS (2012)
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
Mikel Galar et al.
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2012)
A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection
Caio C. O. Ramos et al.
COMPUTERS & ELECTRICAL ENGINEERING (2011)
Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System
Jawad Nagi et al.
IEEE TRANSACTIONS ON POWER DELIVERY (2011)
Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems
Eduardo Werley S. dos Angelos et al.
IEEE TRANSACTIONS ON POWER DELIVERY (2011)
Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies
Carlos Leon et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2011)
A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest
Caio Cesar Oba Ramos et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2011)
Smart meters for power grid: Challenges, issues, advantages and status
Soma Shekara Sreenadh Reddy Depuru et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2011)
Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines
Jawad Nagi et al.
IEEE TRANSACTIONS ON POWER DELIVERY (2010)
Feature deduction and ensemble design of intrusion detection systems
S Chebrolu et al.
COMPUTERS & SECURITY (2005)
Share Paper
