  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.
