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In school and office buildings, the ventilation system has a large contribution to the total energy use. A control strategy that adjusts the operation to the actual demand can significantly reduce the energy use. This is important in rooms with a highly fluctuating occupancy profile, such as classrooms and open offices. However, a standard rule-based control (RBC) strategy is reactive, making the installation 'lag behind' in relation to the demand. As a result, a good indoor climate is not always guaranteed and the actual energy saving potential is lower than predicted. This study focuses on nearly zero energy buildings (nZEB) buildings where the insulation and air tightness of the envelope is high resulting in slower reactions towards disturbances (occupants and solar radiation). Furthermore, internal heat gains have a higher impact on the indoor climate in these type of buildings. A model predictive control (MPC) might be a solution as an MPC takes into account the current situation and the future demand. MPC has already shown savings for hydronic systems in operating buildings as indicated in recent studies which resulted in energy savings of 17-30%.
Previously identified dynamic models for CO2 and temperature prediction are implemented in a linear MPC framework to evaluate the impact on the indoor environmental quality (IEQ) and energy use. The dynamic models are data-driven (RC and ARX models) and identified using measurement data obtained from an operating building. The building consists of two lecture rooms, each with a capacity of 80 students. Balanced mechanical ventilation is provided with a total supply airflow of 4400 m³/h. The airflow rate is controlled by VAV boxes based on measurements of CO2-concentration and operative temperature in each lecture room. For heating purposes, the air is preheated by an air-to-air heat recovery. Additionally, heating coils are integrated in the supply ducts of each zone so it is regarded as an all air HVAC system.
Different strategies (actual number, lecture schedule, motion) for occupancy prediction are analysed and their effect on the operation of the MPC. The MPC framework is first tested in a simulation environment (Modelica). Results will be presented for the effect of MPC on the operation of the all-air system and the energy use for both the fans and the heating coil. Results of the simulations will be used to improve the current RBC control in a test building. This will result in an optimized energy use while at the same time providing a comfortable indoor climate.
The study showed that with a minimal dataset of the following parameters: indoor, supply and outdoor temperature, solar radiation, airflow rate and occupancy an energy efficient MPC could be developed with respect to thermal comfort. The airflow rate is decreased by 47% compared to the measurements while the heating energy for ventilation (Qvent) is decreased by 56% for the complete period of four weeks during the transition season