  Being able to track appliances energy usage without the need of sensors can
help occupants reduce their energy consumption to help save the environment all
while saving money. Non-intrusive load monitoring (NILM) tries to do just that.
One of the hardest problems NILM faces is the ability to run unsupervised --
discovering appliances without prior knowledge -- and to run independent of the
differences in appliance mixes and operational characteristics found in various
countries and regions. We propose a solution that can do this with the use of
an advanced filter pipeline to preprocess the data, a Gaussian appliance model
with a probabilistic knapsack algorithm to disaggregate the aggregate smart
meter signal, and partition maps to label which appliances were found and how
much energy they use no matter the country/region. Experimental results show
that relatively complex appliance signals can be tracked accounting for 93.7%
of the total aggregate energy consumed.
