# Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home

arXiv: https://arxiv.org/abs/2201.07387 · HTML: https://mlsanity.com/paper/2201.07387/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [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)
- [Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach](https://mlsanity.com/paper/2103.04154/) (2021)
- [Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning](https://mlsanity.com/paper/2106.15348/) (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, 8 of 8)

- [Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning](https://mlsanity.com/paper/2305.08885/) (2023)
- [GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces](https://mlsanity.com/paper/2303.01621/) (2023)
- [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)
- [Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation](https://mlsanity.com/paper/2509.19405/) (2025)
- [A Proper Scoring Rule for Virtual Staining](https://mlsanity.com/paper/2602.23305/) (2026)
- [Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap](https://mlsanity.com/paper/2603.13570/) (2026)
- [On Applicability of Synthetic Datasets for Facial Expression Recognition](https://mlsanity.com/paper/2605.17483/) (2026)
- [Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2609.17808/) (2026)

## Abstract (arXiv)

Data is the fuel of data science and machine learning techniques for smart grid applications, similar to many other fields. However, the availability of data can be an issue due to privacy concerns, data size, data quality, and so on. To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase.We compared the Kullback-Leibler (KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance between the synthetic data (electrical load and PV production) distribution generated by the proposed model, vanilla GAN network, and the real data distribution, to evaluate the performance of our model. Furthermore, we used five key statistical parameters to describe the smart grid data distribution and compared them between synthetic data generated by both models and real data. Experiments indicate that the proposed synthetic data generative model outperforms the vanilla GAN network. The distribution of VAE-GAN synthetic data is the most comparable to that of real data.