  Energy disaggregation, known in the literature as Non-Intrusive Load
Monitoring (NILM), is the task of inferring the power demand of the individual
appliances given the aggregate power demand recorded by a single smart meter
which monitors multiple appliances. In this paper, we propose a deep neural
network that combines a regression subnetwork with a classification subnetwork
for solving the NILM problem. Specifically, we improve the generalization
capability of the overall architecture by including an encoder-decoder with a
tailored attention mechanism in the regression subnetwork. The attention
mechanism is inspired by the temporal attention that has been successfully
applied in neural machine translation, text summarization, and speech
recognition. The experiments conducted on two publicly available datasets--REDD
and UK-DALE--show that our proposed deep neural network outperforms the
state-of-the-art in all the considered experimental conditions. We also show
that modeling attention translates into the network's ability to correctly
detect the turning on or off an appliance and to locate signal sections with
high power consumption, which are of extreme interest in the field of energy
disaggregation.
