# Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring

arXiv: https://arxiv.org/abs/2306.05012 · HTML: https://mlsanity.com/paper/2306.05012/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [Subtask Gated Networks for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1811.06692/) (2018)
- [Wavenilm: A causal neural network for power disaggregation from the complex power signal](https://mlsanity.com/paper/1902.08736/) (2019)
- [COLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/2106.02352/) (2021)
- [Energy Disaggregation using Variational Autoencoders](https://mlsanity.com/paper/2103.12177/) (2021)

## Built on by (most cited first, 1 of 1)

- [Survey of Video Diffusion Models: Foundations, Implementations, and Applications](https://mlsanity.com/paper/2504.16081/) (2025)

## Abstract (arXiv)

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency.