Adaptive Data Balancing Method Using Stacking Ensemble Model and Its Application to Non-Technical Loss Detection in Smart Grids-Reference-Cited by-同舟云学术

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Adaptive Data Balancing Method Using Stacking Ensemble Model and Its Application to Non-Technical Loss Detection in Smart Grids

Published:2022 Issue: Volume:10 Page:133244-133255
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ullah Ashraf¹,Javaid Nadeem¹^ORCID,Javed Muhammad Umar¹^ORCID,Pamir ¹^ORCID,Kim Byung-Seo²^ORCID,Bahaj Saeed Ali³

Affiliation:

1. Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan

2. Department of Computer and Information Communications Engineering, Hongik University, Sejong, South Korea

3. Department of Management Information Systems, College of Business Administration, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

Funder

National Research Foundation (NRF), South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09994696.pdf?arnumber=9994696

Reference36 articles.

1. LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection

2. AlexNet, AdaBoost and Artificial Bee Colony Based Hybrid Model for Electricity Theft Detection in Smart Grids

3. Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach

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

5. On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization

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1. Prediction of machine tool spindle assembly quality variation based on the stacking ensemble model;The International Journal of Advanced Manufacturing Technology;2024-05-16

2. Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection;Sensors;2024-02-15

3. Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities;IET Generation, Transmission & Distribution;2024-01-04

4. Machine and Deep Learning Base Stacking Model Using Hybrid Class Imbalanced Technique for Electricity Theft Detection in Smart Grid;2024

5. Prediction of Machine Tool Spindle Assembly Quality Variation Based on the Stacking Ensemble Model;2023-10-30

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