arXiv:2608. 19804v1 Announce Type: new Abstract: Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions.
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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.
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.
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: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...
arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.