Energy Ttm

Energy-TTM is a domain-specific for energy meter data analytics, pretrained on large-scale real-world smart meter data from the corpus. Built upon IBM Research's architecture, it learns transferable representations of electricity consumption patterns across diverse residential and commercial buildings. The pretrained model is designed for across heterogeneous buildings, regions, and operational contexts, while remaining . It is optimized for , enabling accurate day-ahead energy demand prediction with minimal task-specific adaptation. --- ## Pretraining Dataset Energy-TTM is pretrained on , a large-scale real-world smart meter dataset available on Hugging Face: 👉 The dataset consists of: - - - 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. - Context length: 168 hours - Prediction horizon: 24 hours ### Available Checkpoints | Model Variant | Context | Horizon | Pretrained On | Intended For | |---------------|:-------:|:-------:|---------------|--------------| | | 168 | 24 | energy meter data from diverse residential and commercial buildings | General-purpose energy forecasting | | | 168 | 24 | Synthetic building data () | Commercial buildings | | | 512 | 96 | Synthetic building data () | Commercial buildings | | | 168 | 24 | Synthetic building data () | Residential buildings | | | 512 | 96 | Synthetic building data () | 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) ## 📚 EnergyFM Recipies ### ⚡ Zero-Shot Forecasting with EnergyTTM --- ### ⚡ Fine-Tuning EnergyTTM ## Resources * 👉 * 👉 ## 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: 👉 The leaderboard provides standardized evaluations across forecasting, anomaly detection, and classification tasks, enabling direct comparison under consistent experimental settings. --- ## Limitations and Intended Use EnergyFM is intended for and has been pretrained on electricity consumption data. Performance may degrade when applied to unrelated domains or data with significantly different temporal characteristics. --- ## Citation If you use EnergyFM in your work, please cite: