Implementation of MPC for an all-air system in an educational building

The building sector has to significantly reduce the total energy use. A predictive control could be a solution to control an HVAC system more energy efficiently since it takes into account the current measurements and the future demand. In this study a predictive control framework is implemented in an educational building with two lecture rooms. The airflow rate is controlled by VAV boxes based on measurements of CO2 concentration and operative temperature. The dynamic model used for optimization of the control input is a grey-box model, previously identified using measurement data. Weather forecasts and weekly lecture schedules are used as forecasts for the optimization of future control actions. The control actions resulting from the optimization are written to the set points for supply air temperature and VAV damper position using the BACnet interface. Results of the first trial indicate that the predictive control is able to control the room temperature and CO2 concentration, even with uncertainty introduced by the forecasts. Prediction errors observed were 0.17 ˚C for room temperature and 87 ppm for indoor CO2 concentration.