Model predictive control of indoor microclimate: Existing building stock comfort improvement

Abstract

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

Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts

“…MPC provides an effective means for proactive control and optimization of building HVAC systems to reduce energy consumption and maximize efficiency, all while enhancing grid stabilization. However, energy consumption and weather forecasts which allow for effective predictive and proactive control are often inaccurate, which leads to sub-optimal control [26,47,48]. In addition to uncertainty in building energy forecasts and weather forecasts, system disturbances can occur which could be unpredicted by the proactive controller [49].…”

Modeling, Simulation, and Temperature Control of a Thermal Zone with Sliding Modes Strategy

“…In Figure 6, a reduction of the second order circuit is presented, with the internal gain I L driven by the SC to handle the internal temperature in the thermal zone. The state variables defined by the controller are the temperature error x 1 and the heat flux x 2 shown in Equations (11) and (12). Here the desired temperature for the closed room is called reference temperature T re f , and the switch u represents the internal gain state.…”

“…[10] uses the Zokolov mathematical model, which is based on heat balance with quasi-steady-state approximations to determine the average internal temperature. For more detailed models, it is possible to include different thermal phenomena such as infiltration and thermal inertia, as in [11], where the mass and energy conservation principle was used. However, in the majority of research it is acceptable to use reduced order models.…”

Sliding Modes Control for Heat Transfer in Geodesic Domes

“…Another important issue in thermal comfort is the inclusion and selection of a controller for the HVAC system. Predictive control [16] and fuzzy control [17] are methods that have been widely used for this purpose, but it is still necessary to explore alternative controllers, such as sliding modes. This kind of comparative study would help to select the appropriate method in non-conventional thermal zones [18].…”