用于动态暖通空调控制的基于自适应模型的迁移学习
Adaptive Model-Based Transfer Learning for Dynamic HVAC Control
AI总结：
研究如何自动调整HVAC系统中AHU设定点，提出基于自适应模型的迁移学习方法，利用模拟软件生成训练数据，通过物理规则嵌入和长期感知设定点选择策略提升性能，实现相似实际建筑间知识转移，加速新建筑部署。
AI中文摘要：
本文旨在实现暖通空调（HVAC）系统中空气处理机组（AHU）设定点的自动调整，以将室内温度维持在用户指定水平。关键挑战在于从实际建筑获取足够高质量的传感器数据。为此，探索迁移学习并利用模拟软件生成训练数据。提出基于自适应模型的迁移学习方法用于动态HVAC控制，消除定义数据生成计划对特定目标知识的大量需求并降低收集无关样本风险。在控制层面，通过物理规则嵌入和长期感知设定点选择策略提升性能。最后，实现相似实际建筑间直接知识转移，减少重复构建虚拟源域需求以加速并稳定新建筑中的部署。
英文摘要：
In this paper, we aim to automate the adjustment of air handling unit (AHU) setpoints within heating, ventilation, and air conditioning (HVAC) systems to maintain indoor temperatures at user-specified levels. A key challenge lies in obtaining sufficient high-quality sensor data from real buildings. To address this, we explore transfer learning and leverage simulation software to generate training data. We propose an adaptive model-based transfer learning approach for dynamic HVAC control, where the agent directly controls the source domain under conditions identical to the target domain. This eliminates the need for extensive target-specific knowledge to define data generation schedules and reduces the risk of collecting irrelevant samples, while also providing greater flexibility during learning. At the control level, we enhance performance through physics rule embedding, which ensures physical consistency, and long-term-aware setpoint selection strategy, which mitigates abrupt setpoint changes. Finally, to accelerate and stabilize deployment in new buildings, we enable knowledge transfer directly between similar real-world buildings, reducing the need to construct virtual source domains repeatedly.
