Identification of multi-zone building thermal interaction model from data

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.