Office Building Model Identification and Control Design

This paper presents the identification of a lumped thermal model for one floor of a large office building consisting of partitioned cubicles and conference rooms. The space is instrumented with a large number of interior temperature sensors as well as supply-air temperature and flowrate sensors. Roof top solar radiation data is also available. The first step in the model identification process is to cluster the interior temperature measurements into zones. The zones form a thermal connectivity graph based on the physical location of the clusters. Each zone corresponds to a node in the graph with a corresponding thermal capacitance. The zones connect to adjacent zones and to the ambient via thermal resistances. Due to large windows on two walls and the ceiling, the model also needs to account for the radiant heat input into the space. We identify thermal capacitance, thermal resistance, and radiant heat transfer coefficients in the model by matching the predicted temperature trajectory with the measured data. As validation, the model shows reasonable prediction of temperatures on dates not used for identification. Using the identified model, we also consider the temperature control problem with real-world ambient temperature and solar data. By using our previously reported passivity-based temperature controller, we achieve tighter temperature regulation while consuming less energy as compared with the existing controller.