Non-intrusive Load Monitoring via Multi-label Sparse Representation based Classification
This addresses energy disaggregation for smart grid applications, but it is incremental as it modifies an existing method for a known bottleneck.
The paper tackled non-intrusive load monitoring by adapting sparse representation based classification for multi-label classification, achieving significant improvement over state-of-the-art techniques on benchmark datasets with small training data.
This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM). We modify the popular 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 techniques with small volume of training data.