A Multi-Task Deep Learning Approach for Non-Intrusive Load Monitoring of Multiple Appliances

This letter proposes a novel deep learning-based multi-task approach for non-intrusive monitoring of home appliances-the first of its kind-where a network can simultaneously estimate the states and disaggregate energies of multiple appliances. An attention-powered encoder-decoder network, comprising...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2024-05, Vol.15 (3), p.3337-3340
Main Authors: Dash, Suryalok, Sahoo, N. C.
Format: Article
Language:English
Subjects:
attention network
Buildings
Data aggregation
Deep learning
Encoders-Decoders
energy disaggregation
Home appliances
Household appliances
Load monitoring
Long short term memory
Monitoring
Multitasking
Non-intrusive appliance load monitoring
Task analysis
Windows
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Description
Summary:This letter proposes a novel deep learning-based multi-task approach for non-intrusive monitoring of home appliances-the first of its kind-where a network can simultaneously estimate the states and disaggregate energies of multiple appliances. An attention-powered encoder-decoder network, comprising a convolutional layer and a long short-term memory, is deployed for the above tasks. Test results from two real-world datasets demonstrate the approach's feasibility, showcasing superior performance and reduced memory requirements.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2024.3373258