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

arXiv: https://arxiv.org/abs/1912.07360 · HTML: https://mlsanity.com/paper/1912.07360/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2207.02009/) (2022)
- [Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances](https://mlsanity.com/paper/2307.09244/) (2023)

## Abstract (arXiv)

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.