We propose a method that simultaneously identifies a sparse transfer matrix and a disturbance signal for a multizone building’s temperature dynamics from the measurements of inputs and outputs. The proposed method is based on solving a convex optimization problem whose cost function involves an $\ell _{1}$ -penalty to promote a sparse solution. The method ensures that the transfer matrix is sparse, so that only dominant interactions among zones are retained in the model. The disturbance, which is mostly occupant-induced, is assumed to be a piecewise-constant signal, which aids in identification, since the derivative of a piecewise-constant signal is a sparse signal. We test our method on data from a virtual building (a simulation model) and a real building. Results from the virtual building show that the proposed method can accurately identify a sparse network model and a transformed disturbance. Results from the real building data—that does not have a ground truth—show that the method produces sensible results.

中文翻译：

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识别多区域建筑物的网络动力学和干扰

我们提出了一种方法，该方法可以根据输入和输出的测量结果，同时为多区域建筑物的温度动态识别稀疏传递矩阵和干扰信号。所提出的方法是基于解决凸优化问题的，该凸优化问题的成本函数涉及 $ \ ell _ {1} $ -惩罚以提倡稀疏解决方案。该方法可确保传递矩阵稀疏，从而在模型中仅保留区域之间的主导相互作用。假定大部分由乘员引起的扰动是分段恒定信号，由于分段恒定信号的导数是稀疏信号，因此有助于识别。我们对来自虚拟建筑物（模拟模型）和真实建筑物的数据测试我们的方法。虚拟建筑物的结果表明，该方法可以准确地识别稀疏网络模型和转换后的干扰。来自真实建筑数据的结果（没有基本事实）表明该方法产生了明智的结果。