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

Alireza Rahimpour, Hairong Qi, David Fugate, Teja Kuruganti

Research output: Contribution to journalArticlepeer-review

106 Scopus citations

Abstract

Energy disaggregation or non-intrusive load monitoring 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 heating, ventilating, and air conditioning components energy monitoring in an industrial building testbed maintained at the Oak Ridge National Laboratory. Extensive experimental results demonstrate the superior performance of S2K-NMF as compared to state-of-the-art decomposition-based disaggregation algorithms.

Original languageEnglish
Article number7835299
Pages (from-to)4430-4441
Number of pages12
JournalIEEE Transactions on Power Systems
Volume32
Issue number6
DOIs
StatePublished - Nov 2017

Keywords

  • Energy disaggregation
  • HVAC
  • non-negative matrix factorization
  • sparse constraint
  • sum-to-k constraint

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