Air handling unit system (AHU) is one of the series of mechanical systems that regulate and circulate the air through the ducts inside the buildings. In a commercial setting, air handling units accounted for more than 50% of the total energy cost of the building in 2013. The energy efficiency of the system depends on multiple factors. The set points of discharge air temperature and supply air static pressure are important ones. ASHRAE Standard 90.1-2010 requires multi-zone HVAC systems to implement supply air temperature reset. Energy is wasted if the set points are set constant. However, the waste has never been quantified. The objectives of this study were to (1) develop and validate a mathematical model, which can be used to predict the system performance in response to various controls, specifically the set-point control strategies, and associated energy consumption, and (2) to recommend measures for optimizing the AHU performance by optimizing the setting schedules.

In this research, a gray box model was established to evaluate the performance of an AHU. Individual components were modeled using energy and mass balance governing equations that represent the inherent physical processes and interactions with other components. Engineering Equation Solver (EES) was selected for system simulation due to its capabilities of finding the solutions of a large set of complicated equations. The model was validated using two sets of sub hourly real time data. The model performance was evaluated employing Mean Absolute Percentage Error (MAPE) and Root Mean Square Deviation (RMSD). The model was used to create the baseline of energy consumption with constant set points and predict the energy savings using two different reset schedules.

The AHU, which serves the entire basement of a campus building on IUPUI campus, was used for this study. It normally has constant set points of discharge air temperature and supply air static pressure. The AHU was monitored using sensors. The data were filtered and transferred to a Building Automation system. Operation information and design specifications of the AHU were collected. Two reset schedules were investigated to determine the better control strategy to minimize energy consumption of the AHU. Discharge air temperature was reset based on return air temperature (RA-T) with a linear reset schedule from March 4 to March 7. Static pressure of the supply air was reset based on the widest open Variable Air Volume (VAV) box damper position from March 20 to March 23. Additionally, uncertainty propagation method was used to identify the dominant parameters affecting the energy consumption.

Results indicated that 17% energy savings was achieved using discharge air temperature reset while the energy consumption reduced by 7% using static pressure reset. The results also indicated that outside air temperature, supply airflow rate and return air temperature were the key parameters that impact the overall energy consumption.

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