CNN-AdaBoost based hybrid model for electricity theft detection in smart grid
As the use of deep learning models is increased in smart grid systems, especially in load forecasting, supply-demand response, vulnerability detection, and finding abnormal behavior of the customers, it becomes necessary to find out the models’ flaws and increase their classification accuracy. While...
Main Authors: | Santosh Nirmal, Pramod Patil, Jambi Ratna Raja Kumar |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2024-03-01
|
Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671124000342 |
Similar Items
-
AlexNet, AdaBoost and Artificial Bee Colony Based Hybrid Model for Electricity Theft Detection in Smart Grids
by: Ashraf Ullah, et al.
Published: (2022-01-01) -
Deceptive Maneuvers: Subverting CNN-AdaBoost Model for Energy Theft Detection
by: Santosh Nirmal, et al.
Published: (2024-12-01) -
Adversarial measurements for convolutional neural network-based energy theft detection model in smart grid
by: Santosh Nirmal, et al.
Published: (2025-03-01) -
A novel data balancing approach and a deep fractal network with light gradient boosting approach for theft detection in smart grids
by: Afrah Naeem, et al.
Published: (2023-09-01) -
A hybrid model for smart grid theft detection based on deep learning
by: Yinling LIAO, et al.
Published: (2024-02-01)