Occupancy is highly unpredictable and depends on occupants' schedules and their interactions with building systems. Occupancy sensor systems have been deployed in buildings for many years, and many research studies have been conducted that use a range of sensor modalities for occupancy sensing and counting. However, no comprehensive review of occupancy sensor system reliability has been compiled. In addition, there is not currently a universal methodology and metrics to evaluate and report occupancy sensor systems' reliability. There have also been increasing studies on the implementation of occupant-based controls, especially energy savings evaluation in typical office building models. However, although there are many universities throughout the U.S., prototypical academic building models do not currently exist for use in evaluating energy saving potential.To address these research gaps, in this research, a review of the literature on occupancy sensor systems was completed to develop a comprehensive list of influential variables that may impact occupancy sensor system reliability. Next a survey was developed and distributed to a diversity of stakeholders to obtain a list of the most important factors that may influence occupancy sensor system performance. Then, a methodology was developed to assess the reliability of occupancy sensor systems in residential buildings in a controlled laboratory environment. This includes both "typical" testing, evaluating how reliable and accurate an occupancy sensor system is over time in a typical residential building environment, and "failure" testing, identifying individual influential variables that impact performance. The developed methodology was then implemented to evaluate a novel occupancy detection sensor system's reliability. For typical testing, results show that on average, the overall accuracy of the tested sensor system ranged from 62.4% to 76.4%. For the failure testing, the number of occupants, presence of large objects, presence of interior light sources, and number of doors were identified as not influential, while lighting level, location of occupants, additional door in the entry/exit area, and having the TV on are variables determined to impact the sensor system performance. Furthermore, the U.S. DOE reference medium office building model was used as the basis to develop typical academic building models. The model was rezoned to add new spaces based on the space type and functional use data collected from 293 academic buildings across five U.S. universities of different sizes. Four types of typical academic building models were then identified using clustering methods. These include typical "Office-dominated", "Laboratory-dominated", "Study room-dominated", and "Mixed-use" academic building models. Occupant-based controls were then added to the model to evaluate the potential energy savings of these developed models. Results show that among all these four typical academic building models, in ASHRAE Climate Zone 5, the total annual HVAC energy savings ranges from 35% to 51% under "Occupancy presence" scenarios, and a further energy saving increase (3-9%) from "Occupancy presence" scenarios to "Occupancy counting" scenarios. The proposed methodology for evaluating the reliability of occupancy sensor systems presents an opportunity for use as a standardized method to evaluate residential occupancy sensor systems that currently does not exist. This work also provides typical academic building models with integrated occupancy schedules which can be used to evaluate energy saving measures, and aid building designers and operators in making informed decisions in applying appropriate control strategies to optimize building energy systems, as well as predict energy use and demand.
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