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Predictive control oriented subspace identification based on building energy simulation tools

机译:基于建筑能量仿真工具的预测控制面向子空间识别

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Even though modern control has emerged in numerous control applications, a building automation is still a field where the position of the classical control is almost exclusive. The main reason is that for the synthesis of a predictive controller a decent model for control is needed. In the field of building climate control, it is still problem to obtain a model of large building in an explicit form suitable for control. Most of the approaches either use building modeling software to get detailed model, which is unfortunately in implicit form; or the model is built-up as a first principle model, which usually ends-up as an extreme simplification of the reality. In this paper, a building model identification procedure is presented, wherein the building model is built-up as a first-principle model using a simulation software (detailed, precise, however in implicit form), and then a state-space model is identified by means of subspace identification methods. The main focus of the paper lays on a case study of a large office building, and the entire process of its identification.
机译:尽管在许多控制应用中出现了现代控制,但楼宇自动化仍然是经典控制的位置几乎独占的领域。主要原因是,对于合成预测控制器,需要一种用于控制的体面模型。在建筑气候控制领域,以适合对照的明确形式获得大型建筑模型仍然存在问题。大多数方法都使用建筑建模软件来获得详细的模型,这是不幸的形式;或者模型是一个作为第一个原理模型,通常最终以极端简化现实。在本文中,提出了一种建筑模型识别过程,其中使用模拟软件(详细,精确的形式)构建建筑模型作为第一原理模型,然后识别出状态空间模型通过子空间识别方法。纸张的主要重点在于对大型办公楼的案例研究,以及其识别的整个过程。

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