Stacking Ensemble Learning-Based Load Identification Considering Feature Fusion by Cyber-Physical Approach
Author:
Affiliation:
1. School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
2. Department of Energy Technology, Aalborg University, Aalborg, Denmark
Funder
111 Project of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10068826/10036373.pdf?arnumber=10036373
Reference44 articles.
1. Non-invasive load identification based on LSTM-BP neural network
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3. A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances
4. Application of stacking ensemble learning model in quantitative analysis of biomaterial activity
5. Non-intrusive load monitoring algorithm based on features of V–I trajectory
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