Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations

Li, Nan; Çalış, Gülben; Becerik-Gerber, Burçin

doi:10.1016/j.autcon.2012.02.013

Cited by 284 publications

(134 citation statements)

References 18 publications

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“…A number of developed human detection and tracking solutions can be found in the literature, including PIR (passive infrared) sensor (Dodier et al 2006, Duarte et al 2013, Kim et al 2013, RFID (radio-frequency identification) (Ni et al 2004, Zhou & Shi 2011, Li et al 2012) and CO2 sensor (Sun et al 2011, Shan et al 2012. While each of these methods has advantages, it also has its own limitations.…”

Section: Occupancy Information For Coolingmentioning

confidence: 99%

A tracking cooling fan using geofence and camera-based indoor localization

Liu

Yin

et al. 2017

Building and Environment

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Highlights Human positions are tracked accurately by a camera-based indoor tracking system  Air flow is directed to the desired direction by using virtual geofences  Desired air speed is provided based on the PMV-SET thermal comfort model  Fan speed setting is adjusted by a calibrated mapping algorithm based on the estimation of occupant-fan distance  The proposed system can be used for thermal comfort improvement and energy conservation AbstractCompressor-based cooling systems have a large impact on energy consumption in modern buildings, particularly in the tropics. Elevated air movement by electric fans is a cost-effective cooling method for both energy saving and thermal comfort improvement. In this paper, a tracking cooling fan using geofence and camera-based indoor localization is proposed. Personal cooling service is provided based on the detection of the occupant in the area bounded by virtual geofences. The proposed camera-based indoor tracking system is able to accurately locate the positions of the occupant, determine the direction of air flow, and calculate the occupant-fan distance. The tracking fan is able to provide the needed air speed determined by PMV-SET thermal comfort model using a calibrated mapping algorithm. The effectiveness of the proposed system has been verified through experiments which show that the system is able to operate with low power while improving thermal comfort. The system can be used in an air-conditioned environment with higher temperature setpoint to save energy or in a naturally conditioned environment for thermal comfort enhancement.

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Section: Occupancy Information For Coolingmentioning

confidence: 99%

A tracking cooling fan using geofence and camera-based indoor localization

Liu

Yin

et al. 2017

Building and Environment

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“…Unlike PIR, RFID does not require a direct line of sight for occupant detection, thus it can detect stationary and moving occupants [31]. Li et al [30] investigated the use of RFID for demand driven HVAC operation in office buildings. The study found a 100% detection rate for both stationary and mobile occupants and an accuracy level of 88% and 62% respectively, suggesting RFID as a suitable occupancy detection mechanism for demanddriven control of HVAC systems in large academic buildings.…”

Section: Network-based Solutions With Active or Passive Access Badgesmentioning

confidence: 99%

“…RFID is an object detection technology based on signal detection and data transmittance through a radio frequency [29]. An RFID system consists of an antenna, a reader, and tags; the tag contains information that can be detected when it is within the reader's detection range [29,30]. Unlike PIR, RFID does not require a direct line of sight for occupant detection, thus it can detect stationary and moving occupants [31].…”

Section: Network-based Solutions With Active or Passive Access Badgesmentioning

confidence: 99%

Effectiveness of using WiFi technologies to detect and predict building occupancy

et al. 2017

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Abstract. This paper presents findings of a case-study demonstrating the effectiveness of using WiFi networks to detect occupancy as opposed to CO 2 sensors, commonly used for demand-controlled heating, ventilation and air conditioning (HVAC) systems. The study took place in one building at the University of Manitoba Fort Garry campus in Canada. In a classroom, the number of WiFi connections was collected on an hourly basis over one-week, simultaneously with CO 2 concentration levels at 10-min intervals. The number of occupants in this classroom was also counted on an hourly basis over the same study period. Data analysis showed that WiFi counts predicted actual occupancy levels more accurately than CO 2 concentration levels, thus validating the use of this technology to track occupancy. This study was the first to use both CO 2 concentration and WiFi counts simultaneously as indicators for occupancy. Results demonstrated the possibility of using WiFi counts in large buildings for controlling HVAC systems at a higher accuracy and lower cost than other sensor technologies. Implications and influences: Given the large contribution of HVAC systems to overall buildings' energy consumption, this study presents a new method for efficiently operating HVAC systems. Results highlighted the accuracy of using WiFi connections as predictors for occupancy patterns to be used for controlling HVAC systems instead of CO 2 sensors. These findings provide a foundation for further research on using WiFi networks to manage and operate HVAC systems in new buildings. Efficient operation of these systems based on real-time occupancy as opposed to static schedules provides facility managers with an opportunity for significant energy savings at a relatively low cost.Keywords: buildings energy management / occupancy and energy consumption / smart buildings systems / green buildings / sensor-based HVAC systems

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“…It can produce the occupants' movements through the building's spaces based on the execution of the daily business processes' activities and the equipment/resources involved, and as a consequence the occupancy of each space. Furthermore, the simulation should additionally provide information about the energy consumption of the building based on the occupancy of each space and the equipment/resources used by the business processes' activities (Page 2007;Mahdavi & Pröglhöf 2009;Li et al 2012). The connection and the correlation of the energy consumption of a building with the business processes occurring in it should deliver accurate simulation results of the building's occupancy and its energy consumption.…”

Section: General Approachmentioning

confidence: 99%

Architectural simulation of the integration of Building Information Modelling (BIM) & Business Process Modelling (BPM)

Eguaras-Martínez¹,

Martín-Gómez²,

Vidaurre-Arbizu³

et al. 2014

eWork and eBusiness in Architecture, Engineering and Construction

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The current methods of building energy simulation that designers and engineers (D&E) use in order to find the energy performance of a building do not take into account the real behavior and daily activities of the people who will use the building. The main aim of this paper is to demonstrate that a system for building simulation, that produces data about the activity behaviour of occupants as members of an enterprise structure and framework, can significantly improve the relevance and performance of building simulation tools, through the study of a real building in daily operation. Furthermore, data (BIM, BPM and occupancy data) has been performed exploiting Open Reference Data Modelling methodology in order to be reusable.

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Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations

Cited by 284 publications

References 18 publications

A tracking cooling fan using geofence and camera-based indoor localization

A tracking cooling fan using geofence and camera-based indoor localization

Effectiveness of using WiFi technologies to detect and predict building occupancy

Architectural simulation of the integration of Building Information Modelling (BIM) & Business Process Modelling (BPM)

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