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Simulation of control strategies for ventilation systems in commercial buildings

Bart Merema, Hilde Breesch, Dirk Saelens, 2018
building energy simulation | ventilation | indoor air quality | control strategies | commercial buildings
Bibliographic info: 39th AIVC Conference "Smart Ventilation for Buildings", Antibes Juan-Les-Pins, France, 18-19 September 2018
Languages: English Pages (count): 10

By the end of 2020 all newly constructed buildings have to be nearly zero energy buildings (nZEB). In school and office buildings the ventilation system has a large contribution to the total energy use. A smart control strategy that adjusts the operation of the ventilation to the actual demand can significantly reduce this energy use. Consequently, control systems are becoming an important part of the ventilation system in these nZEB buildings. To make an accurate prediction of the operation and energy consumption, building energy simulation (BES) programs need to include detailed control strategies. In most simulation programs the dynamics of the HVAC systems are difficult to implement since idealized controllers are used in the component model. The aim of this paper is to study the implementation of control strategies in different BES environments and to identify the effect of control of the ventilation system on the energy use and indoor climate (i.e. temperature and CO2).  



For this research, an all air ventilation system in an existing university building is studied. 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 temperature in each lecture room. In this building, a building monitoring system (BMS) logs the data for all the parameters, e.g. room temperature, air flow, CO2 and energy use, on a one-minute time interval. Furthermore, global horizontal solar radiation and outdoor temperature data from the weather station on the existing building is used as input data.   



The BES model is set up in Modelica. The results of the simulation model is compared to an already calibrated model created with Designbuilder/EnergyPlus and measurement data. The implementation of the control strategy in both Modelica and EnergyPlus is compared to show the differences and possible limitations. This comparison shows that both Modelica and EnergyPlus enable the functionality to implement a smart control strategy for ventilation systems commonly found in commercial buildings. The results show that the IEQ and indoor temperature could be controlled as measured in the case study building. However, Modelica is a useful simulation tool to include a more complex control for HVAC systems compared to EnergyPlus. In contrast, EnergyPlus does not include detailed control for P, PI and PID control. In order to simulate these effects the BES tool needs to include the dynamics for these components. Since the impact of control in nZEB buildings on the total energy consumption is more significant. Modelica is a good option to include a more complex control for HVAC systems compared to EnergyPlus, since it allows to control the dynamics of the dampers and valves. However, EnergyPlus is still a useful and powerful and accurate BES software to calculate energy consumption.  


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