# Faraday: Synthetic Smart Meter Generator for the smart grid

arXiv: https://arxiv.org/abs/2404.04314 · HTML: https://mlsanity.com/paper/2404.04314/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 5 Apr 2024.

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

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Membership Inference Attacks against Synthetic Data through Overfitting Detection](https://mlsanity.com/paper/2302.12580/) (2023)
- [Regularizing Transformers With Deep Probabilistic Layers](https://mlsanity.com/paper/2108.10764/) (2021)

## Built on by (most cited first, 6 of 6)

- [EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models](https://mlsanity.com/paper/2407.13538/) (2024)
- [Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data](https://mlsanity.com/paper/2407.11785/) (2024)
- [A synthetic dataset of French electric load curves with temperature conditioning](https://mlsanity.com/paper/2504.14046/) (2025)
- [Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators](https://mlsanity.com/paper/2409.19713/) (2024)
- [Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data](https://mlsanity.com/paper/2506.11026/) (2025)
- [Synthetic Electric Vehicle Charging Session Generation Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2609.17808/) (2026)

## Abstract (arXiv)

Access to smart meter data is essential to rapid and successful transitions to electrified grids, underpinned by flexibility delivered by low carbon technologies, such as electric vehicles (EV) and heat pumps, and powered by renewable energy. Yet little of this data is available for research and modelling purposes due consumer privacy protections. Whilst many are calling for raw datasets to be unlocked through regulatory changes, we believe this approach will take too long. Synthetic data addresses these challenges directly by overcoming privacy issues. In this paper, we present Faraday, a Variational Auto-encoder (VAE)-based model trained over 300 million smart meter data readings from an energy supplier in the UK, with information such as property type and low carbon technologies (LCTs) ownership. The model produces household-level synthetic load profiles conditioned on these labels, and we compare its outputs against actual substation readings to show how the model can be used for real-world applications by grid modellers interested in modelling energy grids of the future.