An adaptive robust model predictive control for indoor climate optimization and uncertainties handling in buildings

Abstract Model predictive control (MPC) in building automation and control (BAC) applications is challenged by difficulties in constructing accurate building models and handling uncertain disturbances. An adaptive robust model predictive control (ARMPC) is proposed to refine building models and handle uncertainty of disturbances. A model adaptation function is incorporated to perform online estimation of uncertain parameters of the building model using online measured building operation data, as the MPC controller is in operation. An additive uncertainty model to represent uncertainties of disturbances is integrated with the building model for robust optimization. The control performance of the ARMPC is compared with MPC controllers without adaptive modelling and robust optimization, as well as a conventional thermostat through simulation constructed based on a test building. When an energy-saving-biased setting is applied, ARMPC achieves the best thermal comfort performance among the tested controllers. The energy savings achieved by the ARMPC vary from ≈20% to ≈15%, compared to the thermostat, as uncertainty level of internal load increases from 0% to 60%. MPC controllers without adaptive modelling and robust optimization maintain ≈20% energy savings as the uncertainty level increases but at the expense of compromising thermal comfort. When a thermal-comfort-biased setting is applied, the MPC controllers maintain the indoor predicted mean vote (PMV) within a narrow range around thermal neutrality while achieving energy savings of around 10%, compared to the thermostat. The adaptive modelling and robust optimization of the ARMPC prevent the indoor condition from violating the constrains due to model inaccuracy and uncertainties in measured disturbances.