_How OpenSynth is Using PyTorch to Accelerate Energy Modelling:
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By LF Energy OpenSynth & PyTorch Foundation
Published: May 14, 2025
🌍 Why Energy Data Matters
As the world transitions toward cleaner energy systems, understanding how people use electricity is more important than ever. Researchers, policymakers, and innovators rely on smart meter data to study energy demand patterns and optimize how energy flows across the grid.
But there’s a problem:
Real smart meter data is sensitive and private.
Access is highly restricted due to privacy laws and regulations.
Policymaking and research often fall back on outdated, aggregated datasets that no longer reflect today’s dynamic, real-time energy systems.
That’s where OpenSynth, an LF Energy-hosted open source project, comes in.
⚡ OpenSynth: Democratizing Energy Demand Data
OpenSynth provides the global community with synthetic energy demand data. Instead of lobbying to unlock raw consumer smart meter data, OpenSynth enables data holders to generate synthetic datasets that mimic real demand data—without compromising privacy.
This empowers:
Researchers to study new patterns of energy usage.
Industry innovators to test smarter systems.
Policymakers to model future energy grids.
At the heart of OpenSynth is Faraday, an algorithm originally developed by the Centre for Net Zero.
🧠 Meet Faraday: The Engine Behind OpenSynth
Faraday has two main components:
AutoEncoder Module – learns compact representations of energy demand.
Gaussian Mixture Model (GMM) Module – generates synthetic demand profiles.
The challenge?
The original GMM was built with scikit-learn, which only runs on CPUs.
Large datasets take far too long to process.
No support for GPU acceleration or parallel computation.
This meant OpenSynth needed a faster, more scalable solution.
🚀 Enter PyTorch
To overcome these limitations, OpenSynth reimplemented its GMM module in PyTorch.
Here’s what changed:
✅ GPU Acceleration – PyTorch natively supports GPUs, enabling much faster training.
✅ Distributed Training – users can split massive datasets into smaller chunks, train models in parallel, and then merge results.
✅ Scalability – bigger datasets can now be handled efficiently, opening the door to richer, more realistic synthetic data.
This shift means that tasks that once required long hours on CPUs can now be done in a fraction of the time with GPUs.
💡 Why This Matters for Energy Modelling
With PyTorch powering OpenSynth’s GMM module, the community can now:
Scale models to millions of data points.
Generate synthetic demand datasets faster than ever before.
Support global research and policy work with better, more accessible data.
This directly accelerates innovations in:
Grid balancing
Renewable energy integration
Net zero strategy development
💬 From the OpenSynth Team
“Open source is a powerful catalyst for change. Our open data community, OpenSynth, is democratising global access to synthetic energy demand data – unlocking a diversity of downstream applications that can accelerate the decarbonisation of energy systems. PyTorch has an incredible open source ecosystem that enables us to significantly speed up computation for OpenSynth’s users, using distributed GPUs. Without this open source ecosystem, it would have been impossible to implement this change – and slowed down the efforts of those seeking to affect net zero action.”
— Sheng Chai, Senior Data Scientist, Centre for Net Zero
🔗 Learn More
To explore the project and get involved, visit the LF Energy OpenSynth Website
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