EnergyFM

Pretrained Time Series Foundation Models for Energy Meter Data Analytics

Quick Links Website • Paper • Hugging Face • Leaderboard • Energy-TTM • Energy-TSPulse

---

📖 Introduction

EnergyFM is a suite of pretrained time series foundation models (TSFMs) for energy meter data analytics. It provides pretrained models, example pipelines, and tutorial notebooks for short-term load forecasting, anomaly detection, and appliance classification on large-scale smart meter datasets.

EnergyFM is designed to support:

• Zero-shot inference on unseen buildings and regions
• Fine-tuning for downstream energy analytics tasks
• Reproducible benchmarking across forecasting and representation-learning tasks

This repository accompanies the paper:

EnergyFM: Pretrained Models for Energy Meter Data Analytics ACM e-Energy 2026

Important

EnergyFM has moved.
This project is now maintained under the EnergyFM GitHub organization. For the latest code, model releases, documentation, and updates, please visit:
github.com/energyfms

---

⚡ Models

EnergyFM currently includes two pretrained models, each targeting a different class of energy analytics tasks:

Model	Architecture	Primary Use	Supported Tasks
Energy-TTM	Tiny Time Mixer (TTM)	Forecasting	Short-term load forecasting
Energy-TSPulse	TSPulse	Representation learning	Anomaly detection, appliance classification

---

🌍 Energy Benchmark

To compare EnergyFM against other state-of-the-art time series foundation models for energy analytics, visit our public benchmark leaderboard:

👉 Energy Benchmark Leaderboard

The leaderboard provides standardized evaluations across multiple energy forecasting, anomaly detection, and classification tasks, allowing direct comparison between EnergyFM and other TSFMs under consistent evaluation settings.

---

🚀 Getting Started

Energy-TTM

• Zero-Shot Forecasting Open Notebook

• Fine-Tuning for Forecasting Open Notebook

Energy-TSPulse

• Zero-Shot Anomaly Detection Open Notebook

• Fine-Tuning for Anomaly Detection Open Notebook

• Appliance Classification (Fine-Tuning) Open Notebook

---

🤗 Pretrained Models

Pretrained checkpoints and model cards are hosted on Hugging Face:

• Energy-TTM — forecasting model for short-term load forecasting View Model Card

• Energy-TSPulse — representation model for anomaly detection and appliance classification View Model Card

---

🔧 Dependencies

EnergyFM builds on IBM’s Granite Time Series Foundation Models (TSFM) ecosystem, which provides the core model implementations and Hugging Face integration.

• Granite TSFM GitHub: ibm-granite/granite-tsfm
• Granite TSFM Wiki: Granite TSFM Wiki

---

📝 Citation

If you use EnergyFM in your research, please cite:

@inproceedings{energyfm,
  author    = {Arjunan, Pandarasamy and Srivastava, Naman and Kumar, Kajeeth and Jati, Arindam and Ekambaram, Vijay and Dayama, Pankaj},
  title     = {EnergyFM: Pretrained Models for Energy Meter Data Analytics},
  year      = {2026},
  isbn      = {9798400720116},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3744255.3798119},
  doi       = {10.1145/3744255.3798119},
  booktitle = {Proceedings of the 17th ACM International Conference on Future and Sustainable Energy Systems},
  pages     = {556--568},
  series    = {E-Energy '26}
}

---

❓ Issues and Support

Please report bugs, feature requests, or questions via GitHub Issues:

https://github.com/EdgeIntelligenceLab/energyfm/issues