# Wavenilm: A causal neural network for power disaggregation from the complex power signal

## TL;DR

This paper introduces Wavenilm, a causal neural network based on WaveNet architecture, for real-time power disaggregation in NILM, demonstrating improved accuracy and convergence by utilizing all components of complex power signals.

## Contribution

The paper presents a novel causal neural network model for NILM that effectively uses all four components of complex power signals, achieving superior performance over existing methods.

## Key findings

- Faster convergence with all four power components.
- Higher accuracy than state-of-the-art NILM methods.
- Effective real-time disaggregation in low-frequency data.

## 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.