Publication | Closed Access
Occupancy-driven energy management for smart building automation
557
Citations
16
References
2010
Year
Unknown Venue
EngineeringEnergy EfficiencySmart CityPresence Sensor PlatformGreen BuildingSmart EnvironmentBuilding Energy ConservationEnergy MonitoringBuilt EnvironmentSensor NetworksBuilding AutomationSystems EngineeringSmart Building AutomationPresence SensorSmart BuildingComputer EngineeringMobile ComputingPresence NodeEnergy ManagementSmart Buildings
Buildings are major electricity consumers, with HVAC systems accounting for a large share, yet most operate on fixed schedules without fine‑grained occupancy‑based control. This work designs and implements a presence‑sensor platform for accurate, office‑level occupancy detection. The low‑cost, wireless sensor is incrementally deployable and was piloted in ten offices over two weeks to capture occupancy patterns. The sensor achieved over five‑year battery life and, when coupled with building simulations, demonstrated 10–15 % potential energy savings.
Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.
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