Self-Adaptive Deep Learning Framework for Non-Intrusive Load Monitoring: Addressing Aging Appliance Challenges With Transfer Learning and Pseudo Labeling

Self-Adaptive Deep Learning Framework for Non-Intrusive Load Monitoring: Addressing Aging Appliance Challenges With Transfer Learning and Pseudo Labeling

Efficient energy management practices are recognized as crucial for optimizing energy utilization. Non-intrusive load monitoring (NILM) has been identified as a promising solution, particularly with the use of deep learning techniques. Conventional NILM models often face difficulties in adapting to...

Full description

Saved in:
Bibliographic Details
Main Authors: W. A. Yasodya, S. M. L. Arampola, M. S. K. Nisakya, V. Logeeshan, S. Kumarawadu, Chathura Wanigasekara
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Non-intrusive load monitoring (NILM)
deep learning
self-adaptive NILM
transfer learning
pseudo labeling
Online Access:https://ieeexplore.ieee.org/document/11044358/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/11044358/

Similar Items