Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification

Singh, Shikha, Majumdar, Angshul Machine Learning 

This work follows the approach of multi - label classification for non - intrusive load monitoring (NILM) . We modify the popu lar sparse representation based classification (SRC) approach (developed for single label classification) to solve multi - label classification problems. Results on benchmark REDD and Pecan Street dataset shows significant improvement over state - of - the - art t echniques with small volume of training data . N non - intrusive load monitoring (NILM) the technical goal is to estimate the power consumption of different appliances given the aggregate smart - meter readings [1] . The broader social objective is to feedback this information to the household so that they can reduce power consumption and thereby save energy.

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