  Non-Intrusive Load Monitoring (NILM) is a computational technique to estimate
the power loads' appliance-by-appliance from the whole consumption measured by
a single meter. In this paper, we propose a conditional density estimation
model, based on deep neural networks, that joins a Conditional Variational
Autoencoder with a Conditional Invertible Normalizing Flow model to estimate
the individual appliance's power demand. The resulting model is called Prior
Flow Variational Autoencoder or, for simplicity PFVAE. Thus, instead of having
one model per appliance, the resulting model is responsible for estimating the
power demand, appliance-by-appliance, at once. We train and evaluate our
proposed model in a publicly available dataset composed of power demand
measures from a poultry feed factory located in Brazil. The proposed model's
quality is evaluated by comparing the obtained normalized disaggregation error
(NDE) and signal aggregated error (SAE) with the previous work values on the
same dataset. Our proposal achieves highly competitive results, and for six of
the eight machines belonging to the dataset, we observe consistent improvements
that go from 28% up to 81% in NDE and from 27% up to 86% in SAE.
