  Non-intrusive load monitoring (NILM) is a technique that uses a single sensor
to measure the total power consumption of a building. Using an energy
disaggregation method, the consumption of individual appliances can be
estimated from the aggregate measurement. Recent disaggregation algorithms have
significantly improved the performance of NILM systems. However, the
generalization capability of these methods to different houses as well as the
disaggregation of multi-state appliances are still major challenges. In this
paper we address these issues and propose an energy disaggregation approach
based on the variational autoencoders framework. The probabilistic encoder
makes this approach an efficient model for encoding information relevant to the
reconstruction of the target appliance consumption. In particular, the proposed
model accurately generates more complex load profiles, thus improving the power
signal reconstruction of multi-state appliances. Moreover, its regularized
latent space improves the generalization capabilities of the model across
different houses. The proposed model is compared to state-of-the-art NILM
approaches on the UK-DALE and REFIT datasets, and yields competitive results.
The mean absolute error reduces by 18% on average across all appliances
compared to the state-of-the-art. The F1-Score increases by more than 11%,
showing improvements for the detection of the target appliance in the aggregate
measurement.
