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AUTOMATED MULTI-ZONE LINEAR PARAMETRIC BLACK BOX MODELING APPROACH FOR BUILDING HVAC SYSTEMS

机译:楼宇暖通空调系统的自动多区域线性参数黑匣子建模方法

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Optimal control algorithms such as distributed model predictive control (DMPC) offer tremendous potential in reducing energy consumption of building operations. Heating, ventilation and air-conditioning (HVAC) systems which form a major part of the building operations contain a large number of interconnected subsystems. One of the challenges associated with implementing DMPC is the development of reliable models of individual subsystems for prediction, especially for large scale systems. In this paper an automated method is proposed to develop linear parametric black box models for individual building HVAC subsystems. The modeling method proposed identifies the significant inputs, and the upstream and downstream neighbors of each subsystem before performing regression analysis to determine the model parameters. Automation of the model development makes the implementation of the model-based control algorithms much more feasible. The modeling method is then verified through an EnergyPLus model, and using data of a real office building.
机译:诸如分布式模型预测控制(DMPC)之类的最佳控制算法在降低建筑运营的能耗方面具有巨大的潜力。构成建筑运营主要部分的供暖,通风和空调(HVAC)系统包含大量相互连接的子系统。与实施DMPC相关的挑战之一是开发用于预测的单个子系统的可靠模型,尤其是对于大型系统。在本文中,提出了一种自动方法来为各个建筑物HVAC子系统开发线性参数黑匣子模型。提出的建模方法在执行回归分析以确定模型参数之前,先确定重要的输入以及每个子系统的上游和下游邻居。模型开发的自动化使基于模型的控制算法的实现更加可行。然后,通过EnergyPLus模型并使用真实办公楼的数据来验证建模方法。

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