# Conv-NILM-Net, a causal and multi-appliance model for energy source separation

arXiv: https://arxiv.org/abs/2208.02173 · HTML: https://mlsanity.com/paper/2208.02173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [TasNet: time-domain audio separation network for real-time, single-channel speech separation](https://mlsanity.com/paper/1711.00541/) (2017)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [A Review of Deep Reinforcement Learning for Smart Building Energy Management](https://mlsanity.com/paper/2008.05074/) (2020)
- [An empirical study of Conv-TasNet](https://mlsanity.com/paper/2002.08688/) (2020)
- [Convolutional Sequence to Sequence Non-intrusive Load Monitoring](https://mlsanity.com/paper/1806.02078/) (2018)
- [Wavenilm: A causal neural network for power disaggregation from the complex power signal](https://mlsanity.com/paper/1902.08736/) (2019)
- [Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring](https://mlsanity.com/paper/2006.00250/) (2020)

## Abstract (arXiv)

Non-Intrusive Load Monitoring (NILM) seeks to save energy 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 most used models are used for Load Identification rather than online Source Separation. Among source separation models, most use a single-task learning approach in which a neural network is trained exclusively for each appliance. This strategy is computationally expensive and ignores the fact that multiple appliances can be active simultaneously and dependencies between them. The rest of models are not causal, which is important for real-time application. Inspired by Convtas-Net, a model for speech separation, we propose Conv-NILM-net, a fully convolutional framework for end-to-end NILM. Conv-NILM-net is a causal model for multi appliance source separation. Our model is tested on two real datasets REDD and UK-DALE and clearly outperforms the state of the art while keeping a significantly smaller size than the competing models.