Development of Control-Oriented Models for Model Predictive Control in Buildings

Model Predictive Control (MPC) has gained attention in recent years for application to building automation and controls because of significant potential for energy consumption and/or energy cyst savings. MPC utilities dynamic building and HVAC equipment models and input forecasts to estimate future energy usage and employs optimization to determine control inputs that minimize an integrated cost function for a specified prediction horizon. A dynamic model with reasonable prediction performance (e.g., accuracy and simulation speed) is crucial for a practical implementation of MPC. One modeling approach is to use whole-building energy simulation programs such as EnergyPlus, TRNSYS and ESP-r, etc. However, the computational and set up costs for these models are significant and they do not appear to be suitable for on-line implementation. This paper presents the development of control-oriented models for the thermal zones in buildings. Low-order state-space models are identified from the designed input-output responses of thermal zones with disturbances from ambient conditions and internal heat gains. A high-fidelity, TRNSYS model of an office building was used as a virtual testbed to generate data for system identification, parameter estimation, and validation of the proposed model structures. This paper concludes with evaluations of the state-space model in terms of model accuracy for predictive control design.