Hierarchical Model Predictive Control in Building Climate Systems for Passive Energy Sources

Abstract

There is an urgent need for technical innovations in the construction industry to meet the European Parliament’s qualifications for reducing the energy footprint of buildings. This thesis is devoted to conducting research on control strategies that optimally manage passive energy sources, e.g., natural ventilation and solar irradiance, in combination with an active energy source. The main objective is to develop a control strategy that is both on-line applicable and maximizes the performance of the building energy management system in terms of passive fraction of energy while maintaining indoor thermal comfort. Energy-saving model predictive control (MPC) structures have been extensively researched in the literature on building energy management systems. These studies usually focus on the energy distribution in multi-zoned buildings and rarely consider optimal control of a single or multiple passive energy sources. In addition, most studied building models are based on general buildings and are established by means of simulation software tools. A more experimental study has not yet been conducted on optimal energy management systems for a building that is maximized in harvesting passive energy. This work investigates five MPC strategies as a way to optimize the operations of four solar blinds, a thermal chimney, and an active energy source. In these strategies, linear and nonlinear MPC are considered in the forms of centralized and hierarchical architectures. White-box modeling and linearization methods are adopted to develop the required linear and nonlinear building models. Thereafter, the proposed modeling methodology is validated by using experimental data. The hierarchical MPC architecture that considers a hybrid structure with a linear MPC agent for solar blind operations, a nonlinear MPC tracker for ventilation, a linear Kalman filter, and separated state-update loops appears to be the best-performing strategy. This control structure is also applied in a case study, in which it is tested on experimental data from the real-case office building, which is controlled by a rule-based control structure. The results show that the developed control structure is able to outperform the rule-based controller in terms of minimizing energy consumption and maintaining thermal comfort.