  Improving energy efficiency is a necessity in the fight against climate
change. Non Intrusive Load Monitoring (NILM) systems give important information
about the household consumption that can be used by the electric utility or the
end users. In this work the implementation of an end-to-end NILM system is
presented, which comprises a custom high frequency meter and neural-network
based algorithms. The present article presents a novel way to include high
frequency information as input of neural network models by means of
multivariate time series that include carefully selected features. Furthermore,
it provides a detailed assessment of the generalization error and shows that
this class of models generalize well to new instances of seen-in-training
appliances. An evaluation database formed of measurements in two Uruguayan
homes is collected and discussion on general unsupervised approaches is
provided.
