Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building 2010
DOI: 10.1145/1878431.1878433
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Occupancy-driven energy management for smart building automation

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Cited by 433 publications
(248 citation statements)
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“…Detection technologies typically include cameras [47], CO2 sensors [48], cellular phone control-channel traffic sensors [49], humidity sensors [50], infrared (IR) sensors [51], light sensors [52], motion sensors [53], radio frequency identification (RFID) [54], sound sensors [55], switch door sensors [56], telephone sensors [57], temperature sensors [50], ultra-wideband (UWB) [58], wireless sensor networks (WSN) [59], and Wi-Fi infrastructures [60]. These detection technologies can be divided to two main groups [61]: (1) precise technologies with incomplete coverage (e.g., cameras); and (2) imprecise technologies with full coverage (e.g., Wi-Fi infrastructures).…”
Section: Monitoring Occupant-specific Energy Consumptionmentioning
confidence: 99%
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“…Detection technologies typically include cameras [47], CO2 sensors [48], cellular phone control-channel traffic sensors [49], humidity sensors [50], infrared (IR) sensors [51], light sensors [52], motion sensors [53], radio frequency identification (RFID) [54], sound sensors [55], switch door sensors [56], telephone sensors [57], temperature sensors [50], ultra-wideband (UWB) [58], wireless sensor networks (WSN) [59], and Wi-Fi infrastructures [60]. These detection technologies can be divided to two main groups [61]: (1) precise technologies with incomplete coverage (e.g., cameras); and (2) imprecise technologies with full coverage (e.g., Wi-Fi infrastructures).…”
Section: Monitoring Occupant-specific Energy Consumptionmentioning
confidence: 99%
“…Furthermore, in addition to traditional software, traditional building management systems also have limitations with real-time inputs of occupancy-related dynamic factors, such as the number of occupants and their preferences, actions, and decisions [63]. This limitation is problematic since the inputs of real-time occupancy information can reduce HVAC and lighting energy consumption by up to 20 and 30 percent, respectively [56,66,67,75]. In response to these limitations in modeling occupants' energy-use behaviors, a number of studies have recently worked on various simulation techniques to attempt to overcome these particular limitations.…”
Section: Simulating Occupant Energy-consuming Behaviorsmentioning
confidence: 99%
“…For energy, there is a recent movement towards automated building controls to more efficiently manage building energy (e.g., Agarwal et al 2010, Klein et al 2012. From an overall building design point of view, improvements in the building envelope, e.g., window-to-wall ratios, insulation of the walls and roof, air tightness, and window characteristics, are most effective in reducing building energy use (Harvey 2009).…”
Section: Current Building Design At the Few Nexusmentioning
confidence: 99%
“…avoid "soft" tiles). This then allows for accurate real-time occupancy mapping and occupancy driven smart building system control, which will lead to substantial energy savings (Agarwal et al 2010;Nguyen and Aiello 2013;Weng and Agarwal 2012). Most importantly, these savings are realized without using sensors which consume power themselves, as STEP Tech derives its energy from human motion.…”
Section: Outmentioning
confidence: 99%
“…The concept of the smart building, or a building that is aware of its occupancy and power consumption, can help in reducing the overall power draw of buildings (Nguyen and Aiello 2013). Specifically, significant improvements can be made in the highest power consuming building functions, such as HVAC and lighting, by controlling them in real-time based upon building occupancy information (Agarwal et al 2010;Weng and Agarwal 2012). However, the increased number of sensors necessary to realize the smart building concept can also lead to extra power draw and complexity in wiring.…”
Section: Introductionmentioning
confidence: 99%