Discrete wavelet transform and classifiers for appliances recognition

Abstract

Recognition of appliances’ signatures is an important task in energy disaggregation applications. To save and manage energy, load signatures provided by appliances can be used to detect which appliance is used. In this study, we use a low frequency database to identify appliances based on discrete wavelet transform for features extraction and data dimensionality reduction. Further that, the accuracy of several classifiers is investigated. This paper aims to prove the effectiveness of DWT in load signatures recognition. Then, the best classifier for this studied task is selected.