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.
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.
arXiv:2608. 09998v1 Announce Type: new Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks.
arXiv:2608. 19804v1 Announce Type: new Abstract: Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions.
arXiv:2509. 05364v2 Announce Type: replace-cross Abstract: Residential buildings contribute significantly to energy use, health outcomes, and carbon emissions.
The paper presents a zero‑shot model predictive control (MPC) approach for buildings that uses excitation‑based generalized transfer learning models. By pretraining on purposefully probed operational data from multiple source buildings, the authors demonstrate that these models achieve superior control performance in 32 simulated target buildings, outperforming both an online linear MPC and a PI controller. This method eliminates the need for target‑specific data, reducing setup cost and facilitating broader deployment of energy‑efficient MPC in the building sector.
arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.
arXiv:2501. 13703v2 Announce Type: replace-cross Abstract: Transfer Learning (TL) is an emerging field in modeling building thermal dynamics.
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.
arXiv:2607. 11959v1 Announce Type: new Abstract: Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone.
arXiv:2609.00847v1 Announce Type: cross Abstract: As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth p...
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.