6 citations · 6 across the 4 of their papers we have counts for
5 papers
BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
Felix Koch, Thomas Krug, Fabian Raisch +2
Machine learning (ML) is increasingly used for data-driven modeling of buildings to enable downstream tasks such as fault detection and diagnosis, and energy-efficient control. Whi…
Reinforcement Learning with Symbolic Reward Machines
Thomas Krug, Daniel Neider
Reward Machines (RMs) are an established mechanism in Reinforcement Learning (RL) to represent and learn sparse, temporally extended tasks with non-Markovian rewards. RMs rely on h…
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…
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…
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Fabian Raisch, Thomas Krug, Christoph Goebel +1
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging…