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

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.

Automated summary

This work introduces a multi-label classification based paradigm for NILM and demonstrates how to consider the temporal variability of input signals in a classification framework. Results on benchmark datasets such as REDD and Pecan Street show considerable improvement over several state-of-the-art shallow and deep techniques.