Energy-TTM

Energy-TTM is a domain-specific Time Series Foundation Model (TSFM) for energy meter data analytics, pretrained on large-scale real-world smart meter data from the EnergyBench corpus. Built upon IBM Research's Tiny Time Mixer (TTM) architecture, it learns transferable representations of electricity consumption patterns across diverse residential and commercial buildings.

The pretrained model is designed for zero-shot and transfer learning across heterogeneous buildings, regions, and operational contexts, while remaining lightweight and computationally efficient. It is optimized for short-term load forecasting, enabling accurate day-ahead energy demand prediction with minimal task-specific adaptation.

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Pretraining Dataset

Energy-TTM 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.

Default configuration:

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

Available Checkpoints

Model Variant	Context	Horizon	Pretrained On	Intended For
Energy-TTM-168-24 (Default)	168	24	Real-world energy meter data from diverse residential and commercial buildings	General-purpose energy forecasting
Energy-TTM-168-24-comm	168	24	Synthetic commercial building data (ComStock)	Commercial buildings
Energy-TTM-512-96-comm	512	96	Synthetic commercial building data (ComStock)	Commercial buildings
Energy-TTM-168-24-res	168	24	Synthetic residential building data (ResStock)	Residential buildings
Energy-TTM-512-96-res	512	96	Synthetic residential building data (ResStock)	Residential buildings

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)

📚 EnergyFM Recipies

⚡ Zero-Shot Forecasting with EnergyTTM

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⚡ Fine-Tuning EnergyTTM

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:

@inproceedings{energyfm2026,
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},
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}
}