Deep learning-based meta-learner strategy for electricity theft detection
Electricity theft damages power grid infrastructure and is also responsible for huge revenue losses for electric utilities. Integrating smart meters in traditional power grids enables real-time monitoring and collection of consumers’ electricity consumption (EC) data. Based on the collected data, it...
Main Authors: | Faisal Shehzad, Zahid Ullah, Musaed Alhussein, Khursheed Aurangzeb, Sheraz Aslam |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-09-01
|
Series: | Frontiers in Energy Research |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1232930/full |
Similar Items
-
Corrigendum: Deep learning-based meta-learner strategy for electricity theft detection
by: Faisal Shahzad, et al.
Published: (2023-12-01) -
Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid
by: Rehan Akram, et al.
Published: (2021-12-01) -
A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users
by: Khursheed Aurangzeb, et al.
Published: (2021-01-01) -
A rule-based model for electricity theft prevention in advanced metering infrastructure
by: Abdulrahaman Okino Otuoze, et al.
Published: (2022-02-01) -
IOT SECURITY FOR SMART GRID ENVIRONMENT: ISSUES AND SOLUTIONS
by: Yuvaraaj Velayutham, et al.
Published: (2021-03-01)