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

arXiv: https://arxiv.org/abs/1704.07308 · HTML: https://mlsanity.com/paper/1704.07308/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

## Built on by (most cited first, 6 of 6)

- [Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2207.02009/) (2022)
- [MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data](https://mlsanity.com/paper/2307.14778/) (2023)
- [Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.09158/) (2021)
- [Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances](https://mlsanity.com/paper/2307.09244/) (2023)
- [Robust Matrix Factorization with Grouping Effect](https://mlsanity.com/paper/2106.13681/) (2021)
- [Recent Results of Energy Disaggregation with Behind-the-Meter Solar Generation](https://mlsanity.com/paper/2207.03490/) (2022)

## Abstract (arXiv)

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.