Wavenilm: A Causal Neural Network for Power Disaggregation from the Complex Power Signal
Alon Harell, Stephen Makonin, Ivan V. Bajic
Abstract
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems; however, many of them are not causal which is important for real-time application. We present a causal 1-D convolutional neural network inspired by WaveNet for NILM on low-frequency data. We also study using various components of the complex power signal for NILM, and demonstrate that using all four components available in a popular NILM dataset (current, active power, reactive power, and apparent power) we achieve faster convergence and higher performance than state-of-the-art results for the same dataset.
BibTeX
@inproceedings{icassp2019_wavenilmacausaln,
title = {Wavenilm: A Causal Neural Network for Power Disaggregation from the Complex Power Signal},
author = {Alon Harell and Stephen Makonin and Ivan V. Bajic},
booktitle = {ICASSP 2019},
year = {2019}
}