Abstract:
This research focuses on identifying a new
implementation of a machine learning approach for Nonintrusive
load monitoring (NILM). We mathematically
superimpose current profiles of individual appliances and
compare against the actual combinational current profiles. This
simple yet effective method is tested on combinations of 6
household devices in a typical low voltage residential
installation and the high accuracy of correct identification
confirms the proposed method is feasible. The proposed method
eases the burden of the training phase which is considered as an
inherent limitation of all supervised deep learning NILM
models. We deploy the method on a Raspberry Pi 3 providing a
solution to increase the scalability of NILM
