# Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning

arXiv: https://arxiv.org/abs/2109.12440 · HTML: https://mlsanity.com/paper/2109.12440/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning](https://mlsanity.com/paper/2106.15348/) (2021)
- [Correlated Deep Q-learning based Microgrid Energy Management](https://mlsanity.com/paper/2103.04152/) (2021)

## Built on by (most cited first, 3 of 3)

- [Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning](https://mlsanity.com/paper/2305.08885/) (2023)
- [Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home](https://mlsanity.com/paper/2201.07387/) (2022)
- [Multistep Multiappliance Load Prediction](https://mlsanity.com/paper/2212.09426/) (2022)

## Abstract (arXiv)

A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and consumption patterns. In this paper, we propose a sequence to sequence (Seq2Seq) learning-based supply and load prediction along with reinforcement learning-based HEMS control. We investigate how the prediction method affects the HEMS operation. First, we use Seq2Seq learning to predict photovoltaic (PV) power and home devices' load. We then apply Q-learning for offline optimization of HEMS based on the prediction results. Finally, we test the online performance of the trained Q-learning scheme with actual PV and load data. The Seq2Seq learning is compared with VARMA, SVR, and LSTM in both prediction and operation levels. The simulation results show that Seq2Seq performs better with a lower prediction error and online operation performance.