A design model for building occupancy detection using sensor fusion

Ekwevugbe, Tobore; Brown, Neil; Fan, Denis

doi:10.1109/dest.2012.6227924

Cited by 35 publications

(20 citation statements)

References 14 publications

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“…Oldewurtel et al [61] x x Ekwevugbe et al [62] x x RH, light, VOC, CO2, sound, pressure mats Dodier et al [46] x x Hay and Rice [63] x Kushki et al [64] x Chintalapudi et al [65] x Balaji et al [15] x Kim et al [66] x Circuit power monitor Jin et al [67] x Plug load sensor, sonar, accelerometer Ruiz-Ruiz et al [36] x Zhao et al [68] x x x x x Chair sensors Dalton and Ellis [69] x Huang et al [70] x Harris and Cahill [71] x x Conte et al [72] x x Dong et al [56] PC activity…”

Section: Overviewmentioning

confidence: 99%

“…including Wireless Sensor Networks [19,56,57,71,74,77,80,[95][96][97][98], sensor arrays [29,85], RFID (Radio-frequency identification) [11,41,73,91], different motion sensors [19,57,62,74], and other dedicated sensors like chair sensors [74,76], image processing occupancy sensors [93], RH and temperature sensors etc. [29,85,86].…”

Section: Explicit or Implicit Occupancy Sensingmentioning

confidence: 99%

“…Fuzzy Inference approach by Ekwevugbe et al[62]; -Belief Network based approaches by Dodier et al[46] and by Tiller et al[48];-Bayesian Networks by Harris and Cahill[71] and Zhao et al[68];-Support Vector Machine (SVM) by Zhen et al[73];…”

mentioning

confidence: 99%

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Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review

Shen

Newsham

Gunay

2017

Advanced Engineering Informatics

102

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A primary strategy for the energy-efficient operation of commercial office buildings is to deliver building services, including lighting, heating, ventilating, and air conditioning (HVAC), only when and where they are needed, in the amount that they are needed. Since such building services are usually delivered to provide occupants with satisfactory indoor conditions, it is important to accurately determine the occupancy of building spaces in real time as an input to optimal control. This paper first discusses the concepts of building occupancy resolution and accuracy and briefly reviews conventional (explicit) occupancy detection approaches. The focus of this paper is to review and classify emerging, potentially low-cost approaches to leveraging existing data streams that may be related to occupancy, usually referred to as implicit / ambient / soft sensing approaches. Based on a review and a comparison of related projects / systems (in terms of occupancy sensing type, resolution, accuracy, ground truth data collection method, demonstration scale, data fusion and control strategies) the paper presents the state-of-the-art of leveraging existing occupancy-related data for optimal control of commercial office buildings. It also briefly discusses technology trends, challenges, and future research directions.

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Section: Overviewmentioning

confidence: 99%

Section: Explicit or Implicit Occupancy Sensingmentioning

confidence: 99%

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Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review

Shen

Newsham

Gunay

2017

Advanced Engineering Informatics

102

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“…These locations are the doors of the building, as well as the doors of all building spaces. Moreover two Pressure Mats Sensors [28] are placed next to each other, separated by a small space in order to detect movement direction and PIR Motion Sensors [29] which were already installed through the Alarm system installation. When movement is detected, an activation event is sent by the sensor and after a specified period (configured to a few seconds) of no movement detection, a deactivation event is sent.…”

Section: Occupancy Extraction Approachmentioning

confidence: 99%

Building Multi-occupancy Analysis and Visualization Through Data Intensive Processing

Ioannidis

Tropios

Krinidis

et al. 2016

IFIP Advances in Information and Communication Technology

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A novel Building Multi-Occupancy Analysis & Visualization through Data Intensive Processing techniques is going to be presented in this paper. Building occupancy monitoring plays an important role in increasing energy efficiency and provides useful semantic information about the usage of different spaces and building performance generally. In this paper the occupancy extraction subsystem is constituted by a collection of depth image cameras and a multi-sensorial cloud (utilizing big data from various sensor types) in order to extract the occupancy per space. Furthermore, a number of novel visual analytics techniques allow the end-users to process big data in different temporal resolutions in a compact and comprehensive way taking into account properties of human cognition and perception, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed building occupancy analysis system has been tested and applied to various spaces of CERTH premises with different characteristics in a real-life testbed environment.

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“…Dounis et al [2] developed a fuzzy logic controller for the visual comfort. Dounis, RuiYang [3] develop a Multi-agent control system with information fusion based comfort model. This paper developed a novel multi-agent system model for intelligent building energy and comfort management based on occupant behaviors, but it does not establish a common information fusion model.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent system with information fusion for intelligent lighting control

Liangzhou¹,

Wang²,

Jony³

2014

2014 International Conference on Automatic Control Theory and Application

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-Intelligent lighting system is the core component of the smart home. In order to achieve user-centric intelligent control, improve indoor visual comfort level and saving energy at the same time, we have designed a multi-agent intelligent lighting system based on multi-source information fusion. Multi-agent systems able to improve the interactivity of the system, so that the subsystem concurrent processing of data and information for decision making distributions to achieve a closed-loop control. An intelligent data fusion model is proposed to fuse ambient light, occupancy, energy limitations, user preferences and other information for decision making. We use ANFIS modeling algorithm for environmental control of occupancy estimated. Finally, through FIS algorithm, we can implements the entire lighting control subsystem.

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A design model for building occupancy detection using sensor fusion

Cited by 35 publications

References 14 publications

Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review

Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review

Building Multi-occupancy Analysis and Visualization Through Data Intensive Processing

Multi-agent system with information fusion for intelligent lighting control

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