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Aggregation and data driven identification of building thermal dynamic model and unmeasured disturbance

机译:构建热动力学模型的聚合和数据驱动识别和未测量干扰

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An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment. This paper deals with the problem of simultaneously identifying an aggregate thermal dynamic model and unknown disturbances from input-output data of multi-zone buildings. The unknown disturbance is a key challenge since it is not measurable but non-negligible. We first present a principled method to aggregate a multi-zone building model into a single zone model, and show the aggregation is not as trivial as it has been assumed in the prior art. We then provide a method to identify the parameters of the model and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data collected from a multi-zone building testbed in Oak Ridge National Laboratory. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain, even in the case of a specially designed test in which the disturbances affecting each zone are known (as the case of our experimental testbed). This insight is used to provide a heuristic that can be used to assess when the identification results are likely to have high or low accuracy. (C) 2020 Elsevier B.V. All rights reserved.
机译:聚合模型是多区建筑的单个区域等同物,可用于许多目的,包括基于模型的大加热,通风和空调(HVAC)设备的控制。本文涉及同时识别来自多区建筑物的输入输出数据的聚集热动态模型和未知干扰的问题。未知的干扰是一个关键挑战,因为它不是可衡量但不可忽视的。我们首先介绍一个原理的方法来将多区建筑模型聚合到一个区域模型中,并且显示聚合并不像现有技术中假设的那样微不足道。然后,我们提供一种方法来识别模型的参数和该聚合(单区域)模型的未知干扰。最后,我们将所提出的识别算法测试到从橡树岭国家实验室的多区域建筑物收集的数据。通过聚合方法提供的关键洞察力允许我们在什么条件下识别干扰信号的估计必然差而不确定,即使在特殊设计的测试的情况下,其中已知影响每个区域的干扰(视为壳体)我们的实验试验用过)。这种洞察力用于提供一种启发式,可以用于评估识别结果可能具有高或低精度。 (c)2020 Elsevier B.v.保留所有权利。

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