  Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical
usage signal into appliance-specific power consumption and it amounts to a
classical example of blind source separation tasks. Leveraging recent progress
on deep learning techniques, we design a new neural NILM model Multi-State Dual
CNN (MSDC). Different from previous models, MSDC explicitly extracts
information about the appliance's multiple states and state transitions, which
in turn regulates the prediction of signals for appliances. More specifically,
we employ a dual-CNN architecture: one CNN for outputting state distributions
and the other for predicting the power of each state. A new technique is
invented that utilizes conditional random fields (CRF) to capture state
transitions. Experiments on two real-world datasets REDD and UK-DALE
demonstrate that our model significantly outperform state-of-the-art models
while having good generalization capacity, achieving 6%-10% MAE gain and
33%-51% SAE gain to unseen appliances.
