4 papers · 1 filter
Thermal-GEMs: Generalized Models for Building Thermal Dynamics
Felix Koch, Fabian Raisch, Benjamin Tischler
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…
Transfer Learning for Neural Parameter Estimation applied to Building RC Models
Fabian Raisch, Timo Germann, J. Nathan Kutz +2
Parameter estimation for dynamical systems remains challenging due to non-convexity and sensitivity to initial parameter guesses. Recent deep learning approaches enable accurate an…
A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research
Thomas Krug, Fabian Raisch, Dominik Aimer +5
Data-driven modeling of building thermal dynamics is emerging as an increasingly important field of research for large-scale intelligent building control. However, research in data…
Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts
Fabian Raisch, Max Langtry, Felix Koch +3
Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine…