Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization
Abstract In the research of non-intrusive load monitoring (NILM), the temporal characteristics of V–I trajectories are often overlooked, and using a single feature for identification may lead to insignificant differences between similar loads. Based on this, this paper proposes a non-intrusive load...
Saved in:
Main Authors: | Tie Chen, Yimin Yuan, Jiaqi Gao, Shinan Guo, Pingping Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-02-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-89191-x |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Load recognition method based on convolutional neural network and attention mechanism
by: ZHAO Yitao, et al.
Published: (2025-01-01) -
Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania
by: Ngong'Ho Bujiku Sende, et al.
Published: (2025-01-01) -
Differential affection of the visual information sub-streams in a patient with visual agnosia
by: Kirstin Lederer, et al.
Published: (2025-02-01) -
A Comprehensive Approach to Intrusion Detection in IoT Environments Using Hybrid Feature Selection and Multi-Stage Classification Techniques
by: G. Logeswari, et al.
Published: (2025-01-01) -
Caste, local governance effectiveness, and multidimensional poverty in rural India: some evidences
by: Amarachi Onyeyirichi Ogbonna, et al.
Published: (2025-02-01)