Self-triggered model predictive control for the thermal comfort and energy saving of office buildings

In this article, we aim to develop an intelligent and optimized control system for the indoor thermal environment of office buildings to guarantee thermal comfort for indoor staff while reducing energy consumption . Model predictive control (MPC) has confirmed its ability to improve thermal comfort while reducing energy consumption. However, traditional MPC methods require high computing power for the controller to solve each control input, resulting in excessive computing resource waste. To this end, we propose a self-triggered mechanism (STM) that optimizes the solution only when the triggering rule is satisfied. The proposed STM is based on the P-norm to obtain the next solution moment in advance as a way to reduce computational burden. In addition, the indoor and outdoor heat gains are also solved by expressing the thermophysical equations and further combined with the resistance–capacitance (RC) model to make it more accurate. Based on the established model, a self-triggered model predictive control (ST-MPC) system is developed, and the feasibility and convergence of the system are analyzed through rigorous mathematical proof. The results show that the proposed ST-MPC can reduce the computational burden by 95% compared to the traditional MPC and improve thermal comfort by 16.1%. • A high-precision RC model and low-order PMV equation are established. • A new MPC strategy utilizing the self-triggered mechanism is introduced. • Designing an error-norm-based triggering threshold and analyzing the Zeno phenomenon. • The feasibility and stability of the proposed method are demonstrated theoretically. • A model verification and identification based on a real office building.