Deep Sparse Coding for Non-Intrusive Load Monitoring

Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation pr...

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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 9; no. 5; pp. 4669 - 4678
Main Authors: Singh, Shikha, Majumdar, Angshul
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.09.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automata
deep learning
Dictionaries
dictionary learning
Electric appliances
Encoding
Energy disaggregation
Finite state machines
Hidden Markov models
Home appliances
Loading
Machine learning
Markov chains
non-intrusive load monitoring
Separation
Signal processing
ISSN:1949-3053, 1949-3061
Online Access:Get full text
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Summary:Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. The traditional way to address this is via stochastic finite state machines (e.g., factorial hidden Markov model). In recent times, dictionary learning-based approaches have shown promise in addressing the disaggregation problem. The usual technique is to learn a dictionary for every device and use the learned dictionaries as basis for blind source separation during disaggregation. Prior studies in this area are shallow learning techniques, i.e., they learn a single layer of dictionary for every device. In this paper, we propose a deep learning approach-instead of learning one level of dictionary, we learn multiple layers of dictionaries for each device. These multi-level dictionaries are used as a basis for source separation during disaggregation. Results on two benchmark datasets and one actual implementation show that our method outperforms state-of-the-art techniques.
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2017.2666220