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

arXiv: https://arxiv.org/abs/1902.08736 · HTML: https://mlsanity.com/paper/1902.08736/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2207.02009/) (2022)
- [Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection](https://mlsanity.com/paper/2105.11085/) (2021)
- [MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data](https://mlsanity.com/paper/2307.14778/) (2023)
- [Energy Disaggregation using Variational Autoencoders](https://mlsanity.com/paper/2103.12177/) (2021)
- [Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2306.05012/) (2023)
- [A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks](https://mlsanity.com/paper/2104.07809/) (2021)
- [Conv-NILM-Net, a causal and multi-appliance model for energy source separation](https://mlsanity.com/paper/2208.02173/) (2022)

## Abstract (arXiv)

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.