Sequence-to-Sequence-Based Beta-VAE Combined With IECA Attention Mechanism for Energy Disaggregation Algorithm
Author:
Affiliation:
1. School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
2. ZTE Communication Company Ltd., Shenzhen, China
3. Academy for Engineering and Technology, Fudan University, Shanghai, China
Funder
Fudan Institute on Networking Systems of Artificial Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10304314.pdf?arnumber=10304314
Reference35 articles.
1. Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks
2. A comparison of performance metrics for event classification in Non-Intrusive Load Monitoring
3. Denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation
4. NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring
5. Real-Time Energy Disaggregation Algorithm Based on Multi-Channels DCNN and Autoregressive Model
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2. Non-Intrusive Load Identification Based on Retrainable Siamese Network;Sensors;2024-04-17
3. Load Energy Decomposition Algorithm Based on Improved Bidirectional Transformer Combined With Time-Sensing Self-Attention;IEEE Access;2024
4. Synthetic-to-Real Domain Adaptation for Nonintrusive Load Monitoring via Reconstruction-Based Transfer Learning;IEEE Transactions on Instrumentation and Measurement;2024
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