Model-based controllers for indoor climate control in office buildings – Complexity and performance evaluation

Abstract

Model-based controllers are equipped with an integrated control model and utilize information about disturbances that act on the process. It is well established that the performance of building automation systems can be drastically improved by model-based controllers, but, they also lead to a substantial increase of complexity which is an obstacle for large scale implementation. In this work, model-based controllers with different measured disturbances as exogenous inputs and different types of control models were evaluated to explore the possibility of reducing complexity without compromising performance. The work was performed in a simulated environment and focuses on temperature and CO2 concentration control in individual office rooms during periods that are dominated by occupancy. All relevant internal and external disturbances in office environments were considered as both single input and combined inputs to six different control models. The key finding is that controllers with simplified control models and fewer exogenous inputs can perform almost as well as more complex controllers.