6 citations · 6 across the 9 of their papers we have counts for
11 papers
Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models
Fabian Raisch, Felix Koch, Zack Xuereb Conti +2
The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for ind…
Real-world and simulated thermal data from 960 residential multi-zone buildings in Central Europe
Fabian Raisch, Matthias Kersken, Markus Male +1
This paper presents the ThermBuild dataset, which comprises real-world measurements from two single-family homes and simulations of 958 TRNSYS building models. The buildings cover…
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…
Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models
Jan Marco Ruiz de Vargas, Fabian Raisch, Zoltan Nagy +2
Model-based reinforcement learning (MBRL) offers a promising approach for data-efficient energy management in buildings, combining the strengths of predictive modeling and reinforc…
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…
Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control
Fabian Raisch, Timo Germann, J. Nathan Kutz +3
Gray-box RC models are widely used to enable energy-efficient model predictive control (MPC) in buildings. However, estimating RC parameters remains difficult, as conventional opti…
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…
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…