Automated Hybrid Ventilation Control in Complex Buildings

Complex buildings such as hospitals and laboratories require intensive ventilation in order to meet operational demands. This research focuses on reducing cooling and ventilation loads in these types of buildings by incorporating hybrid ventilation in public spaces that do not require intensive air conditioning. Our research departs from previous works by justifying an optimal control strategy through experimentation rather than simulation. Ideally, designers would incorporate modeling to optimize building design and control, but these models are often not economical if created post-construction, and rarely result in accurate implementation of hybrid ventilation strategies. The method establishes a number of generic hybrid ventilation strategies to be tested in the public space of a complex building. The paper describes a multiple regression analysis of data to determine which controls and setpoints are best for maintaining comfort while optimizing energy use. These controls and set-points are inputs for a building automation program developed to automatically determine whether a space should be naturally or mechanically cooled and actuate the analogous commands. It also calculates the expected energy savings associated with each strategy. This automated hybrid ventilation control is illustrated through a case study example, which has shown public space ventilation energy reduction of up to 56 percent. INTRODUCTION Buildings consume over 40 percent of the total energy produced in the United States each year and commercial buildings in particular account for 19 percent of the total energy consumption of the US (EIA 2010). Heating, ventilation and cooling (HVAC) most significantly impact building energy use, accounting for 51 percent of total building energy usage (EIA 2010). Due to high HVAC energy demands, this system is often the target for energy savings initiatives, especially in complex commercial buildings such as laboratories and hospitals that require high energy loads to meet code standards (Federspiel et al. 2000). A study of commercial buildings with high process loads shows that these buildings use up to five times more energy than typical commercial buildings due to high HVAC loads (Federspiel et al. 2000). One way to reduce the high HVAC energy demand in these buildings is to incorporate hybrid ventilation (HV) in their public spaces (Short and Lomas 2007). Hybrid ventilation integrates natural ventilation (NV) and mechanical cooling when a building’s ventilation needs cannot be met naturally. Natural ventilation (NV) is often harnessed through two natural phenomena, cross-flow and buoyancy driven ventilation (Roth et al. 2006). Cross-flow ventilation refers to air-flow from one side of the building to the other caused by wind, and buoyancy, or stack effect ventilation, is bulk movement of air due to temperature stratification in spaces with high ceilings (e.g. atria). These two NV phenomena have the potential to significantly reduce HVAC energy consumption (Roth et al. 2006). NV is often overlooked as a viable option for ventilating complex buildings as building codes do not facilitate its acceptance and a majority of climates do not allow these buildings to operate solely through NV (ASHRAE 2010). Complex buildings in these types of climates do, however, have the potential for HVAC energy savings through the incorporation of mixed mode cooling, or HV. These systems provide flexibility for energy savings and comfort, but typically require robust automated control to operate effectively in spaces that have transient occupants. Due to the complexity and size of mechanical systems in high load buildings, utilizing HV in non-critical areas (e.g. public spaces) may provide a means of lowcost energy savings. This research focuses on reducing HVAC loads in complex buildings by incorporating HV in spaces that do not require intensive airconditioning. The research departs from previous works by justifying an optimal control strategy through experimentation rather than simulation. The method establishes a number of generic HV strategies to be tested in the public space of a complex building to track performance criteria with the aim of selecting the most effective strategy. The research describes a multiple regression analysis of energy data to determine which controls and set-points are best for maintaining comfort while optimizing energy use. These controls, set-points, and the building automation system (BAS) data are inputs for an automation program developed to determine whether a space should be allowed to run HV and actuate the analogous commands. This experimental approach to optimizing HV and developing an automated control is illustrated through a case study example of the Wisconsin Institutes for Discovery on the University of Wisconsin-Madison Campus. OBJECTIVES AND METHODOLOGY The proposed framework is a means of measuring, analyzing and optimizing HV in public spaces of complex buildings in order to create an automated control for the strategy. The framework guides the testing of different HV strategies in existing buildings by tracking performance criteria: energy savings, thermal comfort, and IAQ. The objective is to test and monitor the building to rate the performance of the installed HV system under different control strategies. The best strategy was selected and an automated control was developed to operate it. Figure 1 outlines the framework by establishing Testing Setup (I), Data Collection (II), Data Analysis (III), and Automated Control Development (IV) where the primary focus of this paper is Phase IV as the first three phases are discussed at length in a previous study (Taylor and Menassa 2012). This figure describes what was done to develop the automation control and model. The focus of Phase I is to determine what HV components and strategies to test, how those tests will be implemented through a testing matrix, and how each control will be developed. To track tests developed in the experimental matrix, Phase II (completed in parallel to Phase I) describes a means for collecting data through COMPUTING IN CIVIL ENGINEERING 595 Automated Hybrid Ventilation Control in Complex Buildings N. Taylor, C. C. Menassa, J. S. Nelson 1 Department of Civil and Environmental Engineering, University of Wisconsin-Madison, 1415 Engineering Drive; email: {njtaylor2, menassa}@wisc.edu