Multi-label LSTM autoencoder for non-intrusive appliance load monitoring - Verma, Sagar; Singh, Shikha; Majumdar, Angshul (Electric power systems research, 2021)
•Proposed a new approach to NILM.•LSTM autoencoder fuses dynamic and static modeling paradigms.•Improves over the state-of-the-art algorithms. 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.