3.5.4. Supervisory Control Scheme

MPC is a receding horizon control process, which can be briefly described as follows: at each sampling interval, system states are measured or estimated and fed back to the controller model to update the model states. With the updated information, the controller predicts the system behavior based on the built-in model within the control horizon (e.g., the next 6 h) along with disturbance forecast such as weather and occupancy. The optimization algorithm then tries to find an optimal solution by minimizing the objective function subject to the latest system constraints. The first control action is implemented and then the MPC engine relaunches a new optimization at the next control interval. The SPO optimization engine follows this approach, but it is designed to be a supervisory controller, i.e., it does not replace but rather interacts with local controllers such as thermostats. The optimizer periodically (e.g., every 5 min) sends optimal setpoints to the local controllers, and they control the equipment through traditional control loops at a finer time scale (e.g., 1 s).
