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A study of occupancy-based smart building controls in commercial buildings
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Occupant behavior has a significant influence on energy consumption in buildings because HVAC, lighting, equipment, and ventilation operations are often tied to occupancy- based controls. However, currently, the traditional methods for the prediction of occupant behavior using a building energy modeling approach has begun to face difficulties due to the complex nature of occupant behavior and the introduction of the new technologies (i.e., occupancy sensors) in new and renovated construction. Research in the previous studies revealed that actual occupancy rates in office buildings were quite different compared to typical simulation schedules used in the analysis of building codes and standards. Therefore, large potential energy use reductions are expected when occupancy-based controls are used in building operations. In addition, many workers are recently encouraged to work more at home, which may cause larger unoccupied periods for a significant portion of time at a commercial office building. This fact further increases the need to better understand various occupancy schedules and usage trends in building energy simulations. However, currently, the U.S. commercial building energy codes and standards (i.e., ASHRAE Standard 90.1) do not fully support building energy modeling for occupancy-based controls for code-compliance. Performance paths (i.e., Appendix G method) in Standard 90.1- 2016 offer only partial credits for occupancy-based lighting controls, which tend to underestimate the potential reduction from the use of occupancy-based controls. Also, the requirements of the ASHRAE Standard 90.1 performance path require the mandatory use of identical schedules for the baseline and the proposed design models, which do not present the calculation of reduction from occupancy-based controls. Therefore, this study seeks to analyze occupancy-based controls to determine how varying factors may impact energy use reduction predictions in commercial office buildings. These factors include: different building types (i.e., lightweight versus heavyweight), with different system types (e.g., variable air volume versus packaged single-zone systems) by orientation (i.e., N,S,E,W) in different climates (e.g., cold and hot climates). To achieve the goal of this study, a reference office building was analyzed based on the prototype office building model that was developed by the U.S. DOE and PNNL for small office building for Standard 90.1-2016. Using this model, different thermal zoning models were developed for single-zone and five-zone models to evaluate the impact of occupancy-based controls in the prototype office building. The impact of occupancy-based controls was then evaluated using simulation to study the influence of occupant behavior on HVAC, lighting, equipment, and ventilation system energy use. A sensitivity analysis of each occupancy control schedule (i.e., occupancy, lighting, equipment) was performed in 100%-0% variations to determine interactions between occupancy variables. In addition, simulations for a set of specific occupancy control schedules (i.e., occupancy, lighting, equipment) were conducted in hot-humid and cold-humid climate zones with different building designs (i.e., a raised floor lightweight building and a heavyweight building with varying window-to-wall ratios) and different HVAC system types (i.e., packaged variable air volume versus packaged single-zone systems) to identify potential energy use reduction of occupancy-based building controls on annual energy consumption. The results showed substantial energy reduction potential from varying factors related to occupancy-based controls in commercial office buildings. The evaluation in two climate zones showed a range of energy reduction in Houston and Chicago due to the weather-dependent loads (i.e., heating, cooling, ventilation). Heavyweight material models showed higher percent energy use reduction potential ratios and less energy use compared to the reference building and lightweight models. Also, smaller window-to-wall models represented less total energy use than higher window-to-wall models, which led to higher energy use reduction ratios for smaller window-to-wall ratios. The PVAV systems had higher total load reduction ratios and less total energy use than PSZ systems in Houston and Chicago, especially for heating loads. Whole-building occupancy-based controls revealed more energy use reduction potential ratios in Houston compared to Chicago. The impact of orientation was different depending on thermal zone locations. However, the impact was not fully analyzed because this study did not evaluate combined occupancy sensor controls, daylight controls, and daylighting-based schedules. The largest energy use reduction contributors to occupancy modeling were the internal load factors (e.g., lighting, equipment). The outcome of this study should help guide the development of a guideline for evaluating how occupancy-based building controls can be better incorporated in different building types for different climate zones to reach compliance with ASHRAE Standard 90.1- 2016.
Kim, Chul (2020). A study of occupancy-based smart building controls in commercial buildings. Available electronically from