Buildings are among the largest consumers of energy in the world. A significant part of this energy can be attributed to Heating, Ventilation and Air Conditioning (HVAC) systems, which play an important role in maintaining acceptable thermal and air quality conditions in common building. For this reason, improving energy eciency in buildings is today a primary objective for the building industry, as well as for the society in general. However, in order to successfully control buildings, control systems must continuously adapt the operation of the building to various uncertainties (external air temperature, occupants' activities, etc.) while making sure that energy eciency does not compromise occupant's comfort and well-being. Several promising approaches have been proposed; among them, Model Predictive Control has received particular attention, since it can naturally achieve systematic integration of several factors, such as weather forecasts, occupancy predictions, comfort ranges and actuation constraints. This advanced technique has been shown to bring signicant improvements in energy savings. Model Predictive Control employs a model of the system and solves an on-line optimization problem to obtain optimal control inputs. The on-line computation, as well as the modelling eort, can lead to diculties in the practical integration into a building management system. To cope with this problem, another possibility is to obtain o-line the optimal control prole as a piecewise ane and continuous function of the initial state. By doing so, the computation associated with Model Predictive Control becomes a simple function evaluation, which can be performed eciently on a simple and cheap hardware. In this thesis, an implicit and an explicit formulation of Model Predictive Control for HVAC systems are developed and compared, showing the practical advantages of the explicit formulation.
2014. , 90 p.