Non-intrusive load monitoring for led light classification: A data-driven machine learning approach

In this study, we propose a novel approach using the oscillation characteristics of the RMS current as the input to machine learning models, combined with the confident learning technique. Using the oscillation characteristics obtained by taking a discrete Fourier transform (DFT) of the RMS current as model input, we aim to reduce the computational requirements of the machine learning models.