4 papers
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…
State-Space Models for Tabular Prior-Data Fitted Networks
Felix Koch, Marcel Wever, Fabian Raisch +1
Recent advancements in foundation models for tabular data, such as TabPFN, demonstrated that pretrained Transformer architectures can approximate Bayesian inference with high predi…
BUILDA: A Thermal Building Data Generation Framework for Transfer Learning
Thomas Krug, Fabian Raisch, Dominik Aimer +4
Transfer learning (TL) can improve data-driven modeling of building thermal dynamics. Therefore, many new TL research areas emerge in the field, such as selecting the right source…
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…