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Journal Articles Electric Power Systems Research Year : 2021

Multi-label LSTM autoencoder for non-intrusive appliance load monitoring

Abstract

This work follows the multi-label classification based paradigm for non-intrusive load monitoring (NILM). Power consumption signals used for NILM are inherently time varying. However prior multi-label classification techniques could not model this dynamical behaviour. They used off-the-shelf algorithms for classifying static signals on NILM problems. This is the first work that shows how to account for the temporal variability of input signals in a multi-label classification framework. Results on benchmark datasets like REDD and Pecan Street show considerable improvement over several state-of-the-art shallow and deep techniques.
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Dates and versions

hal-03294549 , version 1 (21-07-2021)

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Sagar Verma, Shikha Singh, Angshul Majumdar. Multi-label LSTM autoencoder for non-intrusive appliance load monitoring. Electric Power Systems Research, 2021, 199, pp.107414. ⟨10.1016/j.epsr.2021.107414⟩. ⟨hal-03294549⟩
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