Optimal Demand Response Operation Using Adaptive Model Predictive Control for Thermally Activated Building Systems

As the need to reduce use in the building sector increases, thermally activated building systems (TABS) have gained attention for providing both comfort and energy efficiency. Their large thermal mass enables peak load shifting, making them suitable for demand response (DR). Effective DR control requires methods that can flexibly handle dynamic building behavior, disturbances, and varying thermal characteristics. While model predictive control (MPC) is capable of predictive optimization, conventional MPC relies on fixed models and lacks adaptability to time‐varying system conditions. This study introduces an adaptive MPC (AMPC) method, which incorporates online estimation and sequential model updating, to realize a DR‐based control strategy for TABS. The method was evaluated through a co‐simulation framework using Dymola and MATLAB/Simulink. Results show that AMPC can perform effective precooling and stably respond to DR requests. Through multiple case studies, the method was found to leverage the thermal storage capacity of TABS to flexibly shift cooling loads. Under the examined conditions, approximately 90%–100% of peak cooling energy was shifted to off‐peak periods, while ceiling surface temperature errors were maintained within about 0.3°C. Furthermore, PMV remained within ±0.5 in all cases, demonstrating that thermal comfort can be preserved even under restricted cooling operation.