PowerFM is an open-source repository for foundation models in the power and energy domain. It both maintains original projects and collects community-contributed open-source projects, featuring fine-tuned and domain-trained models for tasks like load forecasting, fault detection, grid simulation, and agent control.
Explore the Dataset and Benchmark for Power System Foundation Models Across Multiple Scales and Topologies
OpenPowerBench is a first-of-its-kind open-source, multi-task, cross-temporal dataset designed to support training and evaluation of foundation models in power systems. OpenPowerBench includes both topology-dependent tasks (e.g., power flow, optimal power flow, contingency analysis) and topology-independent tasks (e.g., load forecasting, price prediction), supported by a modular data generation pipeline for scalable benchmarking across synthetic and real-world scenarios.
GridFM Community The GridFM project pioneers the concept of FMs for the electric power grid to be trained on grid data – as opposed to text data – with the overarching goal to develop the underlying technology to cope with the increasing complexity and uncertainties of a faster growing grid (e.g., due to hyperscalar data centers, crypto mining etc.). More information about GridFM Community can be found here
Datacenter Siting Assistant: Solvtra is a tool leverages RAG by incorporating datacenter-specific data, including local regulations, environmental reports, and infrastructure details. As a result, it can provide detailed information for potential datacenter locations, such as land and electricity prices, and generate a map illustrating existing datacenter sites and relevant infrastructure.
We welcome contributions! Please see our Contributing Guidelines for details.
This project is licensed under the MIT License - see the LICENSE file for details.
- All contributors who help make this project better
- The Power and AI Initiative (PAI) at Harvard SEAS