Non-intrusive load monitoring systems that are based on deep learning methods produce
high accuracy end-use detection, but are mainly designed with the one vs one strategy. This strategy
dictates that one model is trained to disaggregate only one appliance, which is sub-optimal in
production. Due to the high number of parameters and the different models, training and inference
can be very costly. A promising solution to this problem is the design of a NILM system where all the
target appliances can be recognized by only one model. This paper suggests a novel multi-appliance
power disaggregation model. The proposed architecture is a multi-target regression neural network
which consists of two main parts. The first part is a variational encoder with convolutional layers
and the second part has multiple regression heads, which share the encoder’s parameters. Given
the total consumption of an installation, the multi-regressor outputs the individual consumption of
all the target appliances simultaneously. The experimental setup includes a comparative analysis
against other multi and single-target state-of-the-art models.
