Building occupancy detection through sensor belief networks

Dodier, Robert H.; Henze, Gregor P.; Tiller, Dale K.; Guo, Xiaoting

doi:10.1016/j.enbuild.2005.12.001

Cited by 244 publications

(127 citation statements)

References 3 publications

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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%

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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Abstract:Buildings currently account for 30-40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States' energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants' energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings' energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants' energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants' energy-consuming behaviors in order to assess and predict such characteristics' impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change OPEN ACCESSEnergies 2015, 8 10997 occupants' energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

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Section: Monitoring Occupant-specific Energy Consumptionmentioning

confidence: 99%

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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“…The second focus of this research relates to the analysis of the data stream produced by the sensor network. We have investigated the application of a class of graphical probability models, called belief networks (Dodier, 1999), for the purposes of prediction, diagnosis, and calculation of the value of information in building control systems (Dodier, Henze, Tiller & Guo, 2005). While this work showed that belief network analysis provides a sophisticated paradigm for determining occupancy from a sensor network, it also showed that the belief network was computationally intensive, and may not be practical for application in this area at this time.…”

Section: Discussionmentioning

confidence: 99%

“…A technical paper describing the results of this work has been prepared (Dodier, Henze, Tiller & Guo, 2005), submitted to the journal Energy and Buildings, and is currently under review. While this work showed that belief network analysis provides a sophisticated paradigm for determining occupancy from a sensor network, it also showed that the belief network was computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

Converging Redundant Sensor Network Information for Improved Building Control

Tiller¹,

Henze²

2005

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“…In addition, the control of building function through traditional occupancy sensors has proven inadequate, requiring sophisticated data analysis and redundant sensor measurements to achieve a higher certainty of occupancy detection (Dodier et al 2006). Consequently, occupancy measurements are often left under-sampled, and rely on sensor timeouts to decide when to turn lights off, as anyone who has ever had to get up and wave their arms around to turn the lights back on in a motion detector driven lighting controlled room has experienced.…”

Section: Outmentioning

confidence: 99%

Floor Tile Energy Harvester for Self-Powered Wireless Occupancy Sensing

Sharpes

Vučković

Priya

2015

Energy Harvesting and Systems

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We investigate a concept that can reduce the overall power requirement of a smart building through improvements in the real-time control of HVAC and indoor lighting based on the building occupancy. The increased number of embedded sensors necessary to realize the smart building concept results in a complex wiring and power structure. We demonstrate a floor tile energy harvester for creating a wireless and self-powered occupancy sensor. This sensor termed as "Smart Tile Energy Production Technology (STEP Tech)" can be used to control automation in smart buildings such as lighting and climate control based upon the real-time building occupancy mapping. The sensor comprises of piezoelectric transducer, energy harvesting circuit and wireless communication. Modeling and optimization procedure for the piezoelectric cymbal transducer is described within the framework of tiles. The design and selection of a packaging technique and construction of a durable floor tile enclosure aimed at protecting the bulk piezoceramic is discussed within the constraint that the deflection of the tile should be minimal such that it is not readily perceivable by humans, thus not disturbing their gait. Experimental results demonstrate that the piezoelectric tile could provide a promising solution for wireless occupancy sensing.

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Building occupancy detection through sensor belief networks

Cited by 244 publications

References 3 publications

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Converging Redundant Sensor Network Information for Improved Building Control

Floor Tile Energy Harvester for Self-Powered Wireless Occupancy Sensing

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