PowerFM

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.

🚀 Getting Started with PowerFM

OpenPowerBench: Transformer-based Foundation Models

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: Graph Neural Network (GNN)-based Foundation Models

Explore the Dataset and Graph for GridFM

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

mAIEnergy: Multimodal Foundation Models for Energy

Explore the mAIEnergy Dataset and Workflow

mAIEnergy is a unified, open-access multimodal corpus designed to support Continual Pre-Training (CPT) of Large Language Models (LLMs) and the development of Retrieval-Augmented Generation (RAG) systems in the energy domain. The dataset integrates heterogeneous energy-related information into structured, machine-readable formats, enabling seamless integration into modern AI and multimodal learning pipelines.

The corpus is organized into four top-level modules—textual, numerical, geospatial, and imagery—each accompanied by structured metadata and reproducible processing workflows. In total, the dataset contains approximately 50,000 textual documents, 20,000 images, 25 million numerical time-series records, and 2 million geospatial and relational data entries, primarily focusing on the European energy system.

Detailed datasets and fine-tuned models can be found below:

• Dataset: Zenodo Repository
• Trained/Fine-tuned Models: Hugging Face Model Hub

RAG-based Foundation Models

Explore the Datacenter Siting Assistant: Solvtra

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.

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Core Team

Shaohuai Liu, Alban Puech, Costas Mylonas, Magda Foti, Qian Zhang

Special Thanks

• All contributors who help make this project better
• The Power and AI Initiative (PAI) at Harvard SEAS