  Home retrofitting provides a means to improve the basic energy and comfort
characteristics of a building stock, which cannot be renewed because of
prohibitive costs. We analyze how model predictive control (MPC) applied to
indoor microclimate control can provide energy-efficient solutions to the
problem of occupants' comfort in a variety of situations principally imposed by
external weather and room occupancy. 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 characteristics
and no dedicated ventilation system. Our model includes various physical
effects such as air infiltration and indoor thermal "inertia mass" (inner
walls, floor, ceiling, and furniture), and also accounts for the impact of
human presence essentially as heat and CO$_2$ sources. The influence on the
numerical results of forecast horizons and of uncertainties due to inaccuracies
in the weather and room occupancy forecasts, are analyzed. As we solve
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,
we see in light of our simulation results, that a supply ventilation system
installation provides a solution for significant air quality improvement.

Optimization and Control