Electrical Appliance Classification using Deep Convolutional Neural Networks on High Frequency Current Measurements

Abstract

Monitoring the energy demand of appliances can raise consumer awareness and therefore reduce energy consumption. Using a single-point measurement of mains energy consumption can keep costs and hardware complexity to a minimum. This data stream of raw voltage and current measurements can be used in machine learning tasks to extract information. We apply Deep Convolutional Neural Networks on an electrical appliance classification task, using raw high frequency start up events from two datasets. We further present Data Augmentation techniques to improve the model performance and evaluate different data normalization techniques. We achieve a perfect classification on WHITED and a Fl-Score of 0.69 on PLAID.