论文标题
非线性移动视野估计和模型的预测控制，该建筑物的HVAC动力学未知
Nonlinear Moving Horizon Estimation and Model Predictive Control for Buildings with Unknown HVAC Dynamics
论文作者
论文摘要
我们提出了用于建模，通风和空调（HVAC）控制的解决方案和在线识别。我们的方法包括：（a）基于推导区域温度动力学的一阶能量平衡的电阻电容（RC）模型，以及（b）用于建模HVAC动力学的神经网络。使用非线性移动地平线估计（MHE）同时执行状态估计和模型识别，并具有对系统状态的物理约束。我们利用模型预测控制（MPC）中确定的模型来获得乘员舒适满意度和HVAC能量节省，并使用模拟验证该方法。我们的系统仅依靠建筑管理系统数据，不需要大量的数据存储，并且不需要详细的构建模型。这可以极大地帮助大规模采用MPC，以使未来以乘员为中心的网格相互作用建筑物的控制。
We present a solution for modeling and online identification for heating, ventilation, and air conditioning (HVAC) control in buildings. Our approach comprises: (a) a resistance-capacitance (RC) model based on first order energy balance for deriving the zone temperature dynamics, and (b) a neural network for modeling HVAC dynamics. State estimation and model identification are simultaneously performed using nonlinear moving horizon estimation (MHE) with physical constraints for system states. We leverage the identified model in model predictive control (MPC) for occupant comfort satisfaction and HVAC energy savings and verify the approach using simulations. Our system relies only on building management system data, does not require extensive data storage, and does not require a detailed building model. This can significantly aid the large scale adoption of MPC for future occupant-centric control of grid-interactive buildings.
