From the 1 of 9 linked papers with an AI index.
6 citations · 6 across the 5 of their papers we have counts for
9 papers
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Fabian Raisch, Thomas Krug, Christoph Goebel +1
The paper introduces GenTL, a pretrained LSTM model that can be fine‑tuned for many single‑family houses, removing the need for source‑building selection and cutting prediction err…
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…
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…