# Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning

arXiv: https://arxiv.org/abs/2305.08885 · HTML: https://mlsanity.com/paper/2305.08885/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 14 May 2023.

## Builds on (most cited first, 9 of 9)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home](https://mlsanity.com/paper/2201.07387/) (2022)
- [Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach](https://mlsanity.com/paper/2103.04154/) (2021)
- [Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures](https://mlsanity.com/paper/2111.11868/) (2021)
- [EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations](https://mlsanity.com/paper/2108.03762/) (2021)
- [Smart Home Energy Management: Sequence-to-Sequence Load Forecasting and Q-Learning](https://mlsanity.com/paper/2109.12440/) (2021)

## Built on by (most cited first, 9 of 9)

- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction](https://mlsanity.com/paper/2405.02180/) (2024)
- [CENTS: Generating synthetic electricity consumption time series for rare and unseen scenarios](https://mlsanity.com/paper/2501.14426/) (2025)
- [Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles](https://mlsanity.com/paper/2501.15544/) (2025)
- [Fr'echet Power-Scenario Distance: A Metric for Evaluating Generative AI Models across Multiple Time-Scales in Smart Grids](https://mlsanity.com/paper/2505.08082/) (2025)
- [Being Accountable is Smart: Navigating the Technical and Regulatory Landscape of AI-based Services for Power Grid](https://mlsanity.com/paper/2408.01121/) (2024)
- [Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains](https://mlsanity.com/paper/2408.08399/) (2024)
- [The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles](https://mlsanity.com/paper/2409.15750/) (2024)
- [Distributed Reinforcement Learning using Local Smart Meter Data for Voltage Regulation in Distribution Networks](https://mlsanity.com/paper/2512.12803/) (2025)

## Abstract (arXiv)

Recent years have noticed an increasing interest among academia and industry towards analyzing the electrical consumption of residential buildings and employing smart home energy management systems (HEMS) to reduce household energy consumption and costs. HEMS has been developed to simulate the statistical and functional properties of actual smart grids. Access to publicly available datasets is a major challenge in this type of research. The potential of artificial HEMS applications will be further enhanced with the development of time series that represent different operating conditions of the synthetic systems. In this paper, we propose a novel variational auto-encoder-generative adversarial network (VAE-GAN) technique for generating time-series data on energy consumption in smart homes. We also explore how the generative model performs when combined with a Q-learning-based HEMS. We tested the online performance of Q-learning-based HEMS with real-world smart home data. To test the generated dataset, we measure the Kullback-Leibler (KL) divergence, maximum mean discrepancy (MMD), and the Wasserstein distance between the probability distributions of the real and synthetic data. Our experiments show that VAE-GAN-generated synthetic data closely matches the real data distribution. Finally, we show that the generated data allows for the training of a higher-performance Q-learning-based HEMS compared to datasets generated with baseline approaches.