Model predictive control of indoor microclimate: existing building stock comfort improvement.

We analyze how model predictive control (MPC) applied to microclimate control in buildings that cannot be substantially modified, can provide energy-efficient solutions to the problem of occupants' comfort in a variety of situations principally imposed by external weather. For this purpose we define an objective function for the energy consumption, and we consider two illustrative cases: one building designed and built in recent times with modern HVAC equipment, and one designed and built several decades ago with poor thermal and ventilation characteristics. Our model includes various physical effects such as, e.g., air infiltration and indoor "inertia mass", and also accounts for the impact of human presence essentially as heat and CO$_2$ sources. Further, the influence of forecast horizons and that of uncertainties due to inaccuracies in the weather and room occupancy forecasts on the numerical results are analyzed. We solve the non-convex optimization problems using a linear and a nonlinear optimizer. Full MPC performance is compared to both linearized MPC and a standard on/off controller. The main advantage of MPC is its ability to provide satisfactory solutions for microclimate control at the least possible energy cost for both modern and old buildings. As old buildings are usually not properly ventilated, an inflow ventilation system installation is proposed as a quite advantageous solution for significant air quality improvement in light of our simulation results.