泛化暖通空调控制：基于域随机化的强化学习
Generalizing HVAC Control With Domain Randomized Reinforcement Learning
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中文总结 AI 辅助
本文提出NOMAD-RL，一种基于物理信息归一化流自适应域随机化的强化学习控制器，通过通用恒温器接口实现跨建筑热区迁移，在多区场景中优于PID和无随机化RL基线，接近MPC性能。
中文摘要 AI 辅助
大规模部署先进的暖通空调（HVAC，供暖、通风与空调）控制器仍然困难，因为其性能往往依赖于精确的建筑模型或需要针对每个站点进行重新调参。我们提出了NOMAD-RL（神经在线元适应动力学），一种通用强化学习（RL）控制器，旨在通过通用的、非侵入式恒温器接口在不同热区之间进行迁移。该控制器基于区域测量和预测对温度设定点进行动作，同时循环策略支持在部分可观测条件下的在线适应。我们的主要贡献是一种基于物理信息归一化流的自适应域随机化方案，该方案对热区参数的相关且多模态分布进行建模，同时保持物理合理性和可控性。这产生了一个逼真且逐步自适应的训练课程，改善了跨建筑的迁移能力。我们将NOMAD-RL与恒定设定点PID控制器、无域随机化的RL以及MPC在单区和多区设置中进行了评估。NOMAD-RL始终优于PID和无随机化的RL基线，并接近调优良好的MPC的性能，尤其是在更具挑战性的多区案例中。这些结果凸显了自适应、物理信息域随机化在鲁棒且可迁移的HVAC控制中的潜力。
英文摘要
Deploying advanced HVAC (Heating, Ventilation and Air Conditioning) controllers at scale remains difficult because performance often depends on accurate building models or per-site retuning. We propose NOMAD-RL (Neural Online Meta-Adaptation for Dynamics), a general-purpose Reinforcement Learning (RL) controller designed to transfer across heterogeneous thermal zones through a universal, non-invasive thermostat interface. The controller acts on temperature setpoints from zone measurements and forecasts, while a recurrent policy supports online adaptation under partial observability. Our main contribution is an adaptive domain randomization scheme based on physics-informed normalizing flows, which models correlated and multimodal distributions of thermal-zone parameters while maintaining physical plausibility and controllability. This produces a realistic and progressively adaptive training curriculum that improves transfer across buildings. We evaluate NOMAD-RL against a constant-setpoint PID controller, RL without domain randomization, and MPC in single- and multi-zone settings. NOMAD-RL consistently outperforms the PID and non-randomized RL baselines, and approaches the performance of a well-tuned MPC, especially in the more challenging multi-zone case. These results highlight the potential of adaptive, physics-informed domain randomization for robust and transferable HVAC control.
发表机构
- École de technologie supérieure (ÉTS)（蒙特利尔高等技术学院）
机构由 AI 辅助整理，请以论文原文为准。
