Summary of Faraday: Synthetic Smart Meter Generator For the Smart Grid, by Sheng Chai and Gus Chadney
Faraday: Synthetic Smart Meter Generator for the smart grid
by Sheng Chai, Gus Chadney
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to address the limitations of smart meter data availability is proposed in this paper. The authors introduce Faraday, a Variational Auto-encoder (VAE)-based model trained on 300 million smart meter readings from an UK energy supplier. The model generates synthetic household-level load profiles conditioned on property type and low carbon technologies (LCTs) ownership. The synthetic data is compared to actual substation readings, demonstrating its potential for real-world applications in grid modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where we can easily learn about how homes use energy. This would help us create better grids that are powered by renewable energy sources and use low-carbon technologies like electric vehicles and heat pumps. But there’s a problem: most of the data from smart meters is private, so researchers don’t have access to it. Some people think we should change the laws to allow for more sharing, but this could take a long time. A new way to solve this issue is by creating fake data that mimics the real thing. This paper shows how to do just that using a special kind of machine learning model called Faraday. |
Keywords
* Artificial intelligence * Encoder * Machine learning * Synthetic data
