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Demand controlled ventilation (DCV) refers to a ventilation system with air flow rates that are controlled based on a measurement of an indoor air quality (IAQ) and/or on a thermal comfort parameter. DCV operates at reduced air flow rates during a large amount of the operation time and thus consumes less energy for fan operation and heating/cooling the supply air. The aim of the present research is to assess the IAQ, ventilation efficiency, and the operation and energy efficiency of real operating DCV systems in moderate climates.Measurements are carried out for at least two weeks in autumn and winter 2015-2016. The following parameters were monitored: CO 2 concentrations and air temperatures at different positions in the room and at the extract air grill, position of the variable air volume (VAV) boxes, supply and extract air flow rates and the occupancy of the room. Four case studies with large and varying occupancy rate and with different use and ventilation systems are selected. Two classrooms and three landscaped offices were evaluated.The results show that a DCV is interesting in rooms with a large and varying occupancy rate such as lecture rooms and landscaped offices. A good IAQ is measured in all cases studied even at reduced air flow rates. The effect of the reduced air flow on the ventilation efficiency is negligible. The VAV boxes react well to predefined set points for CO 2 concentration. During the measurement period, the reduction for fan energy ranges from 25 to 55% and ventilation heat losses 25-32% compared to a constant air volume system (CAV) with a design airflow rate of 29 m 3 /(h.pers), i.e., IDA3 in EN 13779. However, commissioning of the DCV is necessary to obtain and maintain these performances.
Demand-controlled-ventilation (DCV) refers to a ventilation system with controlled air flow rate based on indoor air quality. DCV operates at reduced air flow rates during most of the operation time. Therefore, less energy is required for fan operation, compared to a constant-air-volume (CAV) ventilation system. Typically, DCV has a two-layer control with variable-air-volume (VAV) valves, and a fan speed control to maintain a constant-static-pressure in the duct system based on a pressure set point. However, this nominal design based fan pressure setpoint is higher than required when all VAV-valves are closed to a certain extent. Therefore, advanced pressure-reset (PR) control-resetting the pressure set point-potentially reduces fan energy use even further. This paper assesses the impact of fan control on both fan energy use and ventilation performance in DCV system of a densely occupied office. Results of a simulation model and realistic measurement setup are compared. The PR control, using air flow rate and VAV-valve position measurements as feedback, causes significantly reduction on fan energy use. These reductions compared to conventional DCV and CAV respectively, are approximately 10% and 72% in case of high occupancy and 50% and 93% in case of low occupancy. Largely, DCV with PR control is strongly considered, especially when occupancy is often expected to deviate significantly from the nominal conditions. Moreover, both simulations and measurements show that there is a clear trade-off between air flow rate deficit and fan energy use, when the fan energy use is lower, the air flow rate deficit is higher and vice versa. Furthermore, the results show that VAV-valve accuracy, characteristics and its lower operational limit hamper the ventilation system in achieving the predicted performance as in the simulation.
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. However, a standard rule-based control is reactive, making the installation ‘lag behind’ in relation to the demand. A model predictive control (MPC) might be a solution. To implement an MPC control first a suitable model must be identified for reliable predictions of room temperature and CO2 concentration. For CO2 predictions three scenarios are proposed respectively counting camera, lecture schedule and motion sensor. Two model identification techniques are evaluated: ARX and RC models. For identifying the heating dynamics of the case study building the 3 state RC model showed a good performance, a 5 step ahead prediction on a 15 minute time interval indicated a RMSE of approximately 0.60 °C. The 3rd order ARX model indicated similar results, however the cross validation demonstrated that the RC model outperforms the ARX model. For CO2 predictions the counting scenario resulted in the most accurate n-step ahead predictions. The RMSE found for the RC model is at maximum 90 ppm while 140 ppm for the ARX model. RC models are recommended for modelling all-air HVAC systems attributed by the higher prediction accuracy over ARX models. In addition, these models still contain physical parameters compared to ARX models.
The test lecture rooms on Katholieke Universiteit Leuven (KU Leuven) Ghent Technology Campus (Belgium) are a demonstration case of Annex 62: Ventilative Cooling of the International Energy Agency’s Energy in Buildings and Communities programme (IEA EBC). The building is cooled by natural night ventilation and indirect evaporative cooling (IEC). Thermal comfort and the performances of ventilative cooling are evaluated. Long-term measurements of internal temperatures, occupancy, opening of windows and IEC were carried out in the cooling season of 2017. The airflow rates through the windows in cross- and single-sided ventilation mode were measured by both tracer gas concentration decay and air velocity measurements. In addition, the air flow pattern is visualized by measuring air temperatures in the room. The results show that good thermal summer comfort was measured except during heat waves and/or periods with high occupancy. Both nighttime ventilation and IEC operate very well. IEC can lower the supply temperature by day significantly compared to the outdoor temperature. The Air Changes Rates (ACR) of the night ventilation greatly depends on wind direction and velocity. The air temperature profile showed that the air is cooled down in the whole lecture but more in the upper zone. The extensive data monitoring system was important to detect malfunctions and to optimize the whole building performance.
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