Federated learning-enhanced generative models for non-intrusive load monitoring in smart homes
Abstract Non-Intrusive Load Monitoring (NILM) estimates load-specific power by disaggregating household-level power data, enabling smart grids to provide more accurate power estimations and thus prevent energy waste and casualties. Some existing NILM methods employ federated learning (FL) with gener...
Saved in:
| Main Authors: | Yuefeng Lu, Shijin Xu, Yadong Liu, Xiuchen Jiang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11403-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Non-intrusive Load Decomposition Model Based on Deep Fusion of Multi-modal Integration
by: YAO Gang, et al.
Published: (2023-02-01) -
Improved method of non-intrusive load monitoring based on compressed sensing
by: Bo Yuan, et al.
Published: (2025-09-01) -
A non-intrusive load monitoring algorithm based on real-time feature extraction and deep learning model
by: Behrooz Taheri, et al.
Published: (2025-07-01) -
Conditional Tabular GAN-Based Two-Stage Data Generation Scheme for Short-Term Load Forecasting
by: Jaeuk Moon, et al.
Published: (2020-01-01) -
Quantum-enhanced beetle swarm optimized ELM for high-dimensional smart grid intrusion detection
by: Na Cheng, et al.
Published: (2025-07-01)