From the 1 of 8 linked papers with an AI index.
6 citations · 6 across the 5 of their papers we have counts for
8 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…
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…
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…
Robust and Interpretable Graph Neural Networks for Power Systems State Estimation
Arbel Yaniv, Kilian Golinski, Christoph Goebel
This study analyzes Graph Neural Networks (GNNs) for distribution system state estimation (DSSE) by employing an interpretable Graph Neural Additive Network (GNAN) and by utilizing…
BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
Ehimare Okoyomon, Christoph Goebel
Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with gen…
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…