论文标题
建筑热动态模型和未测量干扰的聚合和数据驱动的识别
Aggregation and Data Driven Identification of Building Thermal Dynamic Model and Unmeasured Disturbance
论文作者
论文摘要
聚合模型是一个相当于多区域建筑物的单区域,对于许多目的,包括基于模型的大加热,通风和空调(HVAC)设备的控制。本文介绍了同时识别骨料热动态模型和输入输出数据的未知干扰的问题。未知的干扰是一个关键挑战,因为它是不可测量的,而是不可忽略的。我们首先提出了一种将多区域构建模型汇总到单个区域模型中的原则性方法,并表明聚合并不像以前的ART中所假设的那么微不足道。然后,我们提供了一种方法来识别该模型的参数以及该骨料(单区)模型的未知干扰。最后,我们将提出的识别算法测试到从橡树岭国家实验室中的多区建筑物中收集的数据。通过聚合方法提供的关键见解使我们能够在什么条件下认识到干扰信号的估计肯定且不确定,即使在经过专门设计的测试的情况下,在该测试中,影响每个区域的干扰被称为(我们的实验性测试台)。该洞察力用于提供一种启发式,该启发式方法可用于评估识别结果何时可能具有较高或低的精度。
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. 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.