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Non-intrusive Occupant Load Monitoring in Commercial Buildings
Commercial buildings consume more than 20 percent of total energy use in the United States and they have the highest energy-use intensity and growth rate compared to other major sectors. Promoting energy-saving behaviors among occupants has recently been considered as the most cost-effective approach for reducing commercial building energy consumption, especially for reducing energy consumption of miscellaneous electric loads (MELs) due to direct control of occupants over MELs. Therefore, tracking MELs consumption and linking it with occupants’ energy-saving behavior is critical in intervening occupants’ energy-use behaviors. Currently, individual plug-load meters at an individual’s workspace are mainly used for tracking MELs in a commercial building. However, the implementation of this approach for full scale adoption requires a large initial investment on the part of the business. In addition, such an approach cannot assess occupants’ use on shared resources (e.g., lighting, shared office electronics). On the other hand, non-intrusive load-monitoring is considered a cost-effective and feasible tool to disaggregate building-level data for estimating appliance-specific energy consumption. Previous studies have suggested that adding occupancy sensing data to a load disaggregation process can help in economically estimating occupant-specific energy consumption. However, there is still a gap in properly linking appliance-specific energy consumption to occupants’ energy-use behaviors in commercial buildings. In response, this dissertation proposes an approach which tracks occupant-specific energy-use right after they enter to a building (entry event) and right before they leave a building (departure event); occupants’ behaviors at these events have a large impact on a building’s energy consumption. By utilizing density-based clustering and discriminant analysis, the approach couples occupancy information collected from Wi-Fi infrastructures with aggregated energy-load data to disaggregate load data down to the level of individual occupants. This critically helps understanding individual occupants’ energy-use behaviors in an economic manner and particularly allows to deliver tailored information through personalized feedback to an occupant who follows non-energy-saving behavior, to modify her energy actions to energy efficient behavior.
Rafsanjani, Hamed Nabizadeh, "Non-intrusive Occupant Load Monitoring in Commercial Buildings" (2018). ETD collection for University of Nebraska - Lincoln. AAI10838394.