Use of model predictive control and weather forecasts for energy efficient building climate control

• Published in: Energy and buildings 2012-02, Vol.45, p.15-27
• Main Authors: Oldewurtel, Frauke, Parisio, Alessandra, Jones, Colin N., Gyalistras, Dimitrios, Gwerder, Markus, Stauch, Vanessa, Lehmann, Beat, Morari, Manfred
• Format: Article
• Language: English
• Subjects: Applied sciences; Building climate control; Building technical equipments; Buildings; Buildings. Public works; Chance-constrained control; Climate; Climatology; Climatology and bioclimatics for buildings; Comfort; Computation methods. Tables. Charts; Energy efficiency; Environmental engineering; Exact sciences and technology; Lighting; Luminance; Mathematical models; Predictive control; Space heating; Stochastic model predictive control; Structural analysis. Stresses; Weather
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2011.09.022

► Energy savings potential in buildings by using Model Predictive Control (MPC). ► New stochastic MPC strategy takes into account uncertainty in weather predictions. ► Stochastic MPC outperforms current control practice in terms of energy and comfort. ► For using stochastic MPC the quality of weather predictions is important. ► Stochastic MPC can be easily tuned by changing one parameter. 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 CO 2 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.