Dimension reduction for NILM classification based on principle component analysis

Abstract

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on readings of a centralized meter. Usually, NILM techniques are shown to be improved when various power features and additional power quality parameters are included. However, adding power features leads to increased time complexity which is a disadvantage to real-time operation. Therefore, in this work we offer a process based on principal component analysis (PCA) which reduces the dimension of NILM power features. The suggested method can be used with any NILM classification technique, and shows good performance in terms of standard measures and time complexity when tested on popular datasets.