Thermal-GEMs: Generalized Models for Building Thermal Dynamics
Data-driven models for building thermal dynamics are a scalable approach for enabling energy-efficient operation through fault detection & diagnosis or advanced control. To obtain accurate models, measurement data from a target building spanning months to years are required. Transfer Learning (TL) mitigates this challenge by employing pretrained models based on single or multiple source buildings. General multi-source TL models promise to outperform single-source TL, but alternative multi-source modeling architectures remain to be explored, and evaluation on real-world data is missing. Moreover, time series foundation models (TSFM) have emerged as candidates for the best-performing genera...
| Authors | Felix Koch, Fabian Raisch, Benjamin Tischler |
|---|---|
| Primary category | eess.SY |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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