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Abstract: State-feedback model predictive control (MPC) of constrained discrete-time periodic affine systems is considered. The periodic systems’ states and inputs are subject to periodically time-dependent, hard, polyhedral constraints. Disturbances are additive, bounded and subject to periodically time-dependent bounds. The objective is to design MPC laws that robustly enforce constraint satisfaction in a manner that is least-restrictive, i.e., have the largest possible domain. The proposed design method is demonstrated on a building climate control example. The proposed method is directly applicable to time-invariant MPC. [Copyright &y& Elsevier]

Abstract: This paper investigates the potential of using occupancy information to realize a more energy efficient building climate control. The study focuses on Swiss office buildings equipped with Integrated Room Automation (IRA), i.e. the integrated control of Heating, Ventilation, Air Conditioning (HVAC) as well as lighting and blind positioning of a building zone or room. To evaluate the energy savings potential, different types of occupancy information are used in a Model Predictive Control (MPC) framework, which is well-suited for this study due to its ability to readily include occupancy information in the control. An MPC controller, which controls the building based on a standard fixed occupancy schedule, is used as a benchmark. The energy use of this benchmark is compared with three other control strategies: first, the same MPC controller which uses the same schedule for control as the benchmark, but turns off the lighting in case of (an instantaneous measurement of) vacancy; second, the same MPC controller which uses the same schedule as the benchmark for control, but turns off lighting and ventilation in case of (an instantaneous measurement of) vacancy; and third, the same MPC controller as the benchmark but using a perfect prediction about the upcoming occupancy. This comparison is carried out for different buildings, HVAC systems, seasons and occupancy patterns in order to determine their influence on the energy savings potential. [Copyright &y& Elsevier]

Abstract: This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account the uncertainty due to the use of weather predictions. As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weather predictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice. [Copyright &y& Elsevier]