Optimization of Thermally Activated Building System (TABS) Operation in School Building using Model Predictive Control (MPC)のソースを表示

To further reduce operational carbon emissions and achieve more efficient space cooling in buildings, this study develops and optimizes the operational strategies of Thermally Activated Building Systems (TABS) to minimize energy consumption while maintaining indoor thermal comfort. The research consists of three major objectives: (1) performance evaluation and operational optimization of the Pipe-Embedded Wall (PEW) system; (2) condensation risk assessment and development of condensation-free strategies for ceiling TABS during cooling operation; and (3) improvement of dynamic control performance through an adaptive Model Predictive Control (MPC) framework.
First, for the PEW system installed on the exterior walls of a school building, the thermal interaction with low-grade heat sources was analyzed using unsteady Computational Fluid Dynamics (CFD) simulations combined with field measurements. Based on the identified thermal performance, an MPC-based optimized operational mode was proposed to enhance heat utilization efficiency and maintain a stable indoor environment. Second, to address potential condensation risks of ceiling TABS under hot and humid environment, an unsteady CFD analysis considering coupled hygrothermal transfer within the concrete structure was developed. A predictive formula for indoor dew-point temperature and a condensation safety rate were established, and an MPC-based condensation-free operation strategy was proposed to ensure both system reliability and indoor comfort while improving energy efficiency. Finally, to overcome the limitations of conventional MPC—such as dependency on linear time invariant model accuracy and high modeling complexity—an adaptive MPC (AMPC) framework based on a recursive ARX model was developed. This approach enables online parameter updating of the predictive model, thereby improving control performance and energy efficiency under varying operating conditions.
Overall, this study establishes an integrated framework from system performance verification to condensation prevention and adaptive optimization, providing theoretical and methodological foundations for the intelligent and low-carbon operation of TABS in future sustainable buildings.