arXiv:2501. 13703v2 Announce Type: replace-cross Abstract: Transfer Learning (TL) is an emerging field in modeling building thermal dynamics.
By Fabian Raisch, Thomas Krug, Christoph Goebel, Benjamin Tischler
arXiv:2607. 16534v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved strong results in control, yet learned policies remain brittle to changes in dynamics, action spaces, observation spaces, or goals, a critical limitation for real-world deployment.
By Vincent Taboga, Justin Veilleux, Doseok Jang, Anushree Rankawat, Pierre-Luc Bacon
The paper introduces the Estimator from Scratch, a neural network that embeds RC thermal building equations to estimate parameters, and its extension, the Pretrained Estimator, which is pretrained on multiple buildings to avoid initial‑guess dependence. Both methods outperform a genetic‑algorithm estimator and a fully black‑box neural network across simulated and real buildings, achieving superior prediction accuracy and lower, more consistent MPC costs. The Pretrained Estimator is highlighted as a robust, computationally efficient, and initial‑guess‑free alternative for RC parameter estimation, with potential applicability to other control‑oriented dynamical systems.
By Fabian Raisch, Timo Germann, Sang-Woo Ham, J. Nathan Kutz, Christoph Goebel, Benjamin Tischler
arXiv:2510. 01475v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) has demonstrated significant performance improvements over today's control methods for residential Heating, Ventilation, and Air Conditioning (HVAC), but deploying MPC often requires substantial engineering effort.
By Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer, Bingqing Chen, Guannan Qu, Kevin J. Kircher, Mario Berg\'es
arXiv:2606. 02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response, and community energy efficiency efforts.
By Jainam Dhruva, Yousaf Raza, A. B. Siddique, Simone Silvestri
Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions. Optimizing indoor climate systems plays a critical role for urban climate mitigation aligned with UN Sustainable Development Goals 11 and 13.