EnergyFM/energy-ttm

EnergyFM: Pretrained Models for Energy Meter Data Analytics

EnergyFM is a family of domain-specific Time Series Foundation Models (TSFMs) pretrained on large-scale real-world smart meter data for energy forecasting, anomaly detection, and classification.

EnergyFM is designed to provide strong zero-shot and transfer learning performance across heterogeneous buildings, regions, and operational contexts, while remaining lightweight and computationally efficient.

Energy-TTM adapts IBM’s Tiny Time Mixers (TTM) architecture to the energy domain, optimized for short-term load forecasting.

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Pretraining Dataset

EnergyFM is pretrained on EnergyBench, a large-scale real-world smart meter dataset available on Hugging Face:

👉 https://huggingface.co/datasets/ai-iot/EnergyBench

The dataset consists of:

• 76,217 residential and commercial buildings
• 1.26 billion hourly electricity consumption readings
• Multiple countries and climate zones
• Diverse building types and operational patterns

The scale and diversity of EnergyBench enable EnergyFM to learn daily, weekly, and seasonal consumption patterns and to generalize robustly to unseen buildings and regions.

Models

Energy-TTM

Energy-TTM is adapted from the Tiny Time Mixers (TTM) architecture and is designed for short-term load forecasting.

Default configuration:

• Context length: 168 hours
• Prediction horizon: 24 hours

Available Checkpoints

Model Variant	Context	Horizon	Domain
Energy-TTM-168-24-comm	168	24	Commercial
Energy-TTM-512-96-comm	512	96	Commercial
Energy-TTM-168-24-res	168	24	Residential
Energy-TTM-512-96-res	512	96	Residential

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Supported Tasks

Load Forecasting

Energy-TTM supports short-term electricity load forecasting under both zero-shot and fine-tuning regimes. It demonstrates strong generalization across residential and commercial buildings and outperforms traditional machine learning baselines and generic TSFMs in out-of-distribution settings.

Loading Pretrained Models

🟢 Energy-TTM (Load Forecasting)

import torch
from tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction

device = "cuda" if torch.cuda.is_available() else "cpu"

model = TinyTimeMixerForPrediction.from_pretrained(
    "EnergyFM/energy-ttm",   # Hugging Face model repository
    revision="main",           # Loads Energy-TTM weights
    num_input_channels=1,
).to(device)

Tutorial Notebooks

• Zero Shot Forecasting with Energy-TTM
  Open Notebook

• Fine Tuning Energy-TTM Open Notebook

Resources

• GitHub Repository 👉 https://github.com/energyfms

• Pretraining Dataset (EnergyBench) 👉 https://huggingface.co/datasets/ai-iot/EnergyBench

Energy Benchmark Leaderboard

To compare EnergyFM against other state-of-the-art Time Series Foundation Models for energy analytics tasks, please visit our public benchmark leaderboard:

👉 Energy Benchmark Leaderboard
https://huggingface.co/spaces/EnergyFM/Leaderboard

The leaderboard provides standardized evaluations across forecasting, anomaly detection, and classification tasks, enabling direct comparison under consistent experimental settings.

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Limitations and Intended Use

EnergyFM is intended for energy meter analytics and has been pretrained on electricity consumption data. Performance may degrade when applied to unrelated domains or data with significantly different temporal characteristics.

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Citation

If you use EnergyFM in your work, please cite:

@article{energyfm2026,
  title   = {EnergyFM: Pretrained Models for Energy Meter Data Analytics},
  author  = {Arjunan, Pandarasamy and Srivastava, Naman and Kumar, Kajeeth
             and Jati, Arindam and Ekambaram, Vijay and Dayama, Pankaj},
  journal = {ACM e-Energy},
  year    = {2026}
}