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

TL;DR

This paper introduces a multi-label sparse representation classification method for non-intrusive load monitoring, demonstrating significant improvements over existing techniques on benchmark datasets with limited training data.

Contribution

It extends sparse representation classification to multi-label problems in NILM, providing a novel approach that enhances accuracy with less training data.

Findings

Outperforms state-of-the-art NILM methods

Effective with small training datasets

Achieves significant accuracy improvements

Abstract

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.