Identification of multi-zone building thermal interaction model from data
Identification of multi-zone building thermal interaction model from data
复制标题
从数据中识别多区域建筑热相互作用模型
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
P. Barooah
中科院分区：
文献类型：
--
作者：
Siddharth Goyal;Chenda Liao;P. Barooah
建立多区域建筑的热动力学模型需要模拟墙体的热传导以及区域之间的空气流动引起的对流。关于RC网络的低阶传导模型已经建立得很好，而目前模拟对流的唯一方法是通过CFD(计算流体动力学)。这会将对流模型限制为建筑中的单个分区或少量分区。本文提出了一种从实测空间温度数据中识别多区域建筑物降阶热力模型的新方法。该方法包括首先识别底层网络结构，特别是对应于建筑物图的边的区域之间的对流相互作用的路径。以类似于传导模型的方式，将一对区域之间的对流相互作用模拟为RC网络。该方法的第二步包括估计对流边缘的RC网络模型的参数。识别的对流边缘以及相关的R和C值用于增强最初仅为模拟传导而构建的建筑的热动力学模型。将增强模型和纯传导模型的预测与佛罗里达大学校园内一栋多区域建筑的空间温度测量进行了比较。已识别的模型被认为比纯传导模型更准确地预测温度。
Constructing a model of thermal dynamics of a multi-zone building requires modeling heat conduction through walls as well as convection due to air-flows among the zones. Reduced order models of conduction in terms of RC-networks are well established, while currently the only way to model convection is through CFD (Computational Fluid Dynamics). This limits convection models to a single zone or a small number of zones in a building. In this paper we present a novel method of identifying a reduced order thermal model of a multi-zone building from measured space temperature data. The method consists of first identifying the underlying network structure, in particular, the paths of convective interaction among zones, which corresponds to edges of a building graph. Convective interaction among a pair of zones is modeled as a RC network, in a manner analogous to conduction models. The second step of the proposed method involves estimating the parameters of the RC network model for the convection edges. The identified convection edges, along with the associated R and C values, are used to augment a thermal dynamics model of a building that is originally constructed to model only conduction. Predictions by the augmented model and the conduction-only model are compared with space temperatures measured in a multi-zone building in the University of Florida campus. The identified model is seen to predict the temperatures more accurately than a conduction-only model.