Model predictive control of building energy systems with balanced model reduction

This paper presents a model reduction method based on balanced realization for thermal and power models of buildings. System identification is firstly performed to obtain high-order state-space models. The purpose of model reduction is to simplify the model structure while preserving the major input-output relations, so as to lower the computational cost in the subsequent model predictive control (MPC) scheme. An economic objective function is designed to minimize the energy and demand charges of building energy systems. The effectiveness of the presented method is shown by simulation, and it is shown that the control performance is not significantly affected by using reduced models.