EnergyFM/energy-tspulse

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-TSPulse

Energy-TSPulse is an energy-domain adaptation of IBM Research's TSPulse architecture, pretrained on large-scale smart meter data from the EnergyBench corpus.

The model learns rich temporal representations from diverse residential and commercial electricity consumption patterns, enabling effective deployment across multiple energy analytics tasks.

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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.

Available Variants

Variant	Description
main	Pretrained on the full EnergyBench dataset containing real-world residential and commercial buildings. Recommended checkpoint for most use cases.
512-comm	Pretrained exclusively on commercial building energy consumption data.
512-res	Pretrained exclusively on residential building energy consumption data.

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Supported Tasks

Anomaly Detection

Energy-TSPulse detects point and contextual anomalies in smart meter data using joint time–frequency representations.

Classification

Energy-TSPulse supports appliance usage classification using transfer learning on low-frequency smart meter data, achieving competitive or superior performance compared to strong feature-based classifiers.

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🔵 Energy-TSPulse (Anomaly Detection / Reconstruction)

import torch
from tsfm_public.models.tspulse import TSPulseForReconstruction

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

model = TSPulseForReconstruction.from_pretrained(
    "EnergyFM/energy-tspulse",
    revision="main",       # Loads Energy-TSPulse weights
    num_input_channels=1
).to(device)

🟣 Energy-TSPulse (Classification)

import torch
from tsfm_public.models.tspulse import TSPulseForClassification

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

model = TSPulseForClassification.from_pretrained(
    "EnergyFM/energy-tspulse",
    revision="main"        # Change to 512-res or 512-comm to access commercial or residential specific variant
).to(device)

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Tutorial Notebooks

• Zero Shot Anomaly Detection with Energy-TSPulse
  Open Notebook

• Appliance Classification with Energy-TSPulse
  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}
}