  Energy disaggregation refers to the decomposition of energy use time series
data into its constituent loads. This paper decomposes daily use data of a
household unit into fixed loads and one or more classes of shiftable loads. The
latter is characterized by ON OFF duty cycles. A novel algorithm based on
nonnegative matrix factorization NMF for energy disaggregation is proposed,
where fixed loads are represented in terms of real-valued basis vectors,
whereas shiftable loads are divided into binary signals. This binary
decomposition approach directly applies L0 norm constraints on individual
shiftable loads. The new approach obviates the need for more computationally
intensive methods e.g. spectral decomposition or mean field annealing that have
been used in earlier research for these constraints. A probabilistic framework
for the proposed approach has been addressed. The proposed approach s
effectiveness has been demonstrated with real consumer energy data.
