Convolutional neural network applied to detect electricity theft: A comparative study on unbalanced data handling techniques
出版年份 2021 全文链接
标题
Convolutional neural network applied to detect electricity theft: A comparative study on unbalanced data handling techniques
作者
关键词
Electricity theft, Convolutional neural network, Deep learning, Unbalanced data
出版物
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 131, Issue -, Pages 107085
出版商
Elsevier BV
发表日期
2021-05-01
DOI
10.1016/j.ijepes.2021.107085
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Data mining for abnormal power consumption pattern detection based on local matrix reconstruction
- (2020) Zhiying Feng et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach
- (2019) Md. Nazmul Hasan et al. Energies
- Fraud Detection in Electric Power Distribution: An Approach That Maximizes the Economic Return
- (2019) Pablo Massaferro et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- Review of non-technical loss detection methods
- (2018) George M. Messinis et al. ELECTRIC POWER SYSTEMS RESEARCH
- Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
- (2018) Zibin Zheng et al. IEEE Transactions on Industrial Informatics
- NTL Detection in Electric Distribution Systems Using the Maximal Overlap Discrete Wavelet-Packet Transform and Random Undersampling Boosting
- (2018) Nelson Fabian Avila et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- Clustering-based novelty detection for identification of non-technical losses
- (2018) Joaquim L. Viegas et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Detection of illegal consumers using pattern classification approach combined with Levenberg-Marquardt method in smart grid
- (2018) Ali Akbar Ghasemi et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Detection of Non-Technical Losses Using Smart Meter Data and Supervised Learning
- (2018) Madalina-Mihaela Buzau et al. IEEE Transactions on Smart Grid
- Social-Spider Optimization-based Support Vector Machines applied for energy theft detection
- (2016) Danillo R. Pereira et al. COMPUTERS & ELECTRICAL ENGINEERING
- Unsupervised non-technical losses identification through optimum-path forest
- (2016) Leandro Aparecido Passos Júnior et al. ELECTRIC POWER SYSTEMS RESEARCH
- A Multi-Sensor Energy Theft Detection Framework for Advanced Metering Infrastructures
- (2013) Stephen McLaughlin et al. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
- Analysis of sampling techniques for imbalanced data: An n=648 ADNI study
- (2013) Rashmi Dubey et al. NEUROIMAGE
- Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
- (2012) Geoffrey Hinton et al. IEEE SIGNAL PROCESSING MAGAZINE
- A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection
- (2011) Caio C.O. Ramos et al. COMPUTERS & ELECTRICAL ENGINEERING
- Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines
- (2009) Jawad Nagi et al. IEEE TRANSACTIONS ON POWER DELIVERY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started