# Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning

arXiv: https://arxiv.org/abs/2106.15348 · HTML: https://mlsanity.com/paper/2106.15348/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 26 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)

## Built on by (most cited first, 4 of 4)

- [Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home](https://mlsanity.com/paper/2201.07387/) (2022)
- [Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning](https://mlsanity.com/paper/2109.12440/) (2021)
- [Multistep Multiappliance Load Prediction](https://mlsanity.com/paper/2212.09426/) (2022)
- [GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances](https://mlsanity.com/paper/2604.24766/) (2026)

## Abstract (arXiv)

Appliance-level load forecasting plays a critical role in residential energy management, besides having significant importance for ancillary services performed by the utilities. In this paper, we propose to use an LSTM-based sequence-to-sequence (seq2seq) learning model that can capture the load profiles of appliances. We use a real dataset collected fromfour residential buildings and compare our proposed schemewith three other techniques, namely VARMA, Dilated One Dimensional Convolutional Neural Network, and an LSTM model.The results show that the proposed LSTM-based seq2seq model outperforms other techniques in terms of prediction error in most cases.