Abstract The paper deals with an identification (calibration) of a building thermal model as a crucial part of modern control algorithms. It describes a modelling issue and parameter identification approach. There is presented an identification approach using a so called dual decomposition method which decomposes a large optimization problem into smaller local ones which are then solved by local agents. The local models are found using a grey-box calibration and by a coordination of agents’ mutual shared parameters a global consistency is obtained. The proposed method is tested on two examples; one is basically trivial, in the latter one, more complex, a model of a building simulated in the EnergyPlus program is obtained and offers promising results for (predictive) control-oriented purposes.

中文翻译：

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基于分布式代理的建筑灰盒模型识别

摘要 本文将建筑热模型的识别（校准）作为现代控制算法的关键部分。它描述了建模问题和参数识别方法。提出了一种使用所谓的对偶分解方法的识别方法，该方法将大型优化问题分解为较小的局部优化问题，然后由局部代理解决。使用灰盒校准找到局部模型，并通过代理相互共享参数的协调获得全局一致性。所提出的方法在两个例子上进行了测试；一个基本上是微不足道的，在后一个中，更复杂的是，获得了在 EnergyPlus 程序中模拟的建筑物模型，并为（预测）面向控制的目的提供了有希望的结果。