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

End-to-end NILM System Using High Frequency Data and Neural Networks