Federated learning-based non-intrusive load monitoring adaptive to real-world heterogeneities

Federated learning-based non-intrusive load monitoring adaptive to real-world heterogeneities

Abstract Non-intrusive load monitoring (NILM) is a key way to cost-effectively acquire appliance-level information in advanced metering infrastructure (AMI). Recently, federated learning has enabled NILM to learn from decentralized meter data while preserving privacy. However, as real-world heteroge...

Full description

Saved in:
Bibliographic Details
Main Authors: Qingquan Luo, Chaofan Lan, Tao Yu, Minhang Liang, Wencong Xiao, Zhenning Pan
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Non-intrusive load monitoring
Dynamic clustering
Model initialization
Knowledge distillation
Weighted mechanism
Online Access:https://doi.org/10.1038/s41598-025-02752-y
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-02752-y

Similar Items