A Learnable Image-Based Load Signature Construction Approach in NILM for Appliances Identification
Author:
Affiliation:
1. School of Control Science and Engineering, Shandong University, Jinan, China
2. General Office Department, Northern Suburbs Branch of Jinan Thermo Electron Group Company Ltd., Jinan, China
Funder
National Natural Science Foundation of China
Shandong Natural Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Computer Science
Link
http://xplorestaging.ieee.org/ielx7/5165411/10226523/10025855.pdf?arnumber=10025855
Reference45 articles.
1. An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring
2. Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring
3. User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings
4. Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks
5. Non-Intrusive Load Monitoring Method Based on Improved Differential Evolution Algorithm
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