  Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a
single-channel blind source separation problem, aims to decompose the mains
which records the whole house electricity consumption into appliance-wise
readings. This problem is difficult because it is inherently unidentifiable.
Recent approaches have shown that the identifiability problem could be reduced
by introducing domain knowledge into the model. Deep neural networks have been
shown to be a promising approach for these problems, but sliding windows are
necessary to handle the long sequences which arise in signal processing
problems, which raises issues about how to combine predictions from different
sliding windows. In this paper, we propose sequence-to-point learning, where
the input is a window of the mains and the output is a single point of the
target appliance. We use convolutional neural networks to train the model.
Interestingly, we systematically show that the convolutional neural networks
can inherently learn the signatures of the target appliances, which are
automatically added into the model to reduce the identifiability problem. We
applied the proposed neural network approaches to real-world household energy
data, and show that the methods achieve state-of-the-art performance, improving
two standard error measures by 84% and 92%.
