Non-Intrusive Electric Load identification using Wavelet Transform

Abstract

This paper shows the development of a decision tree for the classification of loads in a non-intrusive load monitoring (NILM) system implemented in a simple board computer (Raspberry Pi 3). The decision tree uses the total energy value of the power signal of an equipment, which is generated using a discrete wavelet transform and Parseval’s theorem. The power consumption data of different types of equipment were obtained from a public access database for NILM applications. The best split point for the design of the decision tree was determined using the weighted average Gini index. The tree was validated using loads available in the same public access database.