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. 19804v1 Announce Type: new Abstract: Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions.
The paper introduces NOMAD‑RL, a reinforcement learning controller for HVAC systems that learns to adapt across diverse thermal zones via a universal thermostat interface. It employs an adaptive domain randomization scheme using physics‑informed normalizing flows to generate realistic, multimodal training data, enabling the recurrent policy to handle partial observability. Experiments show NOMAD‑RL outperforms constant‑setpoint PID and non‑randomized RL, and rivals well‑tuned model predictive control, especially in multi‑zone scenarios.
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.
arXiv:2610.18511v1 Announce Type: new Abstract: Buildings cause nearly 40\% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60\% and acco...
NeuralBES is a differentiable emulator that blends a physics‑based resistance–capacitance thermal model with a shared neural encoder to predict building energy use. By mapping building metadata to physically bounded model coefficients, it solves a scalar linear recurrence in log‑space and uses a predictor–corrector loop to handle thermostat nonlinearity while maintaining full‑horizon gradient flow. Trained on the ResStock dataset, NeuralBES accurately models heterogeneous buildings across climate zones, achieving physics‑valid predictions with fewer parameters than transformer and recurrent baselines and outperforming grey‑box RC models in mean absolute percentage error.
arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.
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.
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: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.
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:2607.27914v2 Announce Type: replace Abstract: Heating, ventilation, and air-conditioning systems account for a large share of building energy use. High-performance control often requires buildi...