CONVERGING REDUNDANT SENSOR NETWORK INFORMATION FOR IMPROVED BUILDING CONTROL

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Description

Knowing how many people occupy a building, and where they are located, is a key component of building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, however, current sensor technology and control algorithms limit the effectiveness of both energy management and security systems. This topical report describes results from the first phase of a project to design, implement, validate, and prototype new technologies to monitor occupancy, control indoor environment services, and promote security in buildings. Phase I of the project focused on instrumentation and data collection. In this project phase a new ... continued below

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30 pages

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Tiller, Dale K. & Henze, Gregor P. November 1, 2004.

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Description

Knowing how many people occupy a building, and where they are located, is a key component of building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, however, current sensor technology and control algorithms limit the effectiveness of both energy management and security systems. This topical report describes results from the first phase of a project to design, implement, validate, and prototype new technologies to monitor occupancy, control indoor environment services, and promote security in buildings. Phase I of the project focused on instrumentation and data collection. In this project phase a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Analysis tools based on Bayesian probability theory were applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice.

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30 pages

Notes

OSTI as DE00835520

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  • Other Information: PBD: 1 Nov 2004

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  • Report No.: NONE
  • Grant Number: FC26-04NT41971
  • DOI: 10.2172/835520 | External Link
  • Office of Scientific & Technical Information Report Number: 835520
  • Archival Resource Key: ark:/67531/metadc779840

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • November 1, 2004

Added to The UNT Digital Library

  • Dec. 3, 2015, 9:30 a.m.

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  • Jan. 3, 2017, 1:51 p.m.

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Tiller, Dale K. & Henze, Gregor P. CONVERGING REDUNDANT SENSOR NETWORK INFORMATION FOR IMPROVED BUILDING CONTROL, report, November 1, 2004; United States. (digital.library.unt.edu/ark:/67531/metadc779840/: accessed June 24, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.