# Non-Intrusive Energy Disaggregation Using Non-negative Matrix Factorization with Sum-to-k Constraint

## TL;DR

This paper introduces a novel non-negative matrix factorization method with a sum-to-k constraint for energy disaggregation, effectively extracting device-level consumption from aggregated signals in smart homes and industrial settings.

## Contribution

It proposes S2K-NMF, a new constrained matrix factorization approach that improves source separation in energy disaggregation tasks over existing methods.

## Key findings

- S2K-NMF outperforms state-of-the-art algorithms in disaggregation accuracy.
- Effective in residential and industrial energy monitoring scenarios.
- Provides publicly available code and dataset for further research.

## Abstract

Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the issue of extracting device-level energy consumption information by monitoring the aggregated signal at one single measurement point without installing meters on each individual device. Energy disaggregation can be formulated as a source separation problem where the aggregated signal is expressed as linear combination of basis vectors in a matrix factorization framework. In this paper, an approach based on Sum-to-k constrained Non-negative Matrix Factorization (S2K-NMF) is proposed. By imposing the sum-to-k constraint and the non-negative constraint, S2K-NMF is able to effectively extract perceptually meaningful sources from complex mixtures. The strength of the proposed algorithm is demonstrated through two sets of experiments: Energy disaggregation in a residential smart home, and HVAC components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory (ORNL). Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms. The source code and our collected data (HVORUT) for studying NILM for HVAC units can be found at https://bitbucket.org/aicip/nonintrusive-load-monitoring.