Building models for model predictive control of office buildings with concrete core activation

Model Predictive Control (MPC) is a good candidate to exploit the energy cost savings potential of Concrete Core Activation (CCA), while guaranteeing thermal comfort. A bottleneck for practical implementation is the selection and identification of the building control model. Using grey box models, this paper studies the impact of model structure and identification data set on the MPC control performance for an office building with CCA. Results for a one-year simulation show: (1) a 2nd-order model achieves equal control performance as a 4th-order one, (2) inclusion of solar or internal gains in the identification data set improves the model accuracy in general, especially for the 4th-order models, (3) MPC with a 2nd-order model reduces electricity consumption by 15\% compared to a reference controller, hereby deploying information about past operative temperature prediction errors and this without the need for solar or internal gains predictions.