Improved occupancy monitoring in non-domestic buildings

Ekwevugbe, Tobore; Brown, Neil; Pakka, Vijayanarasimha Hindupur; Fan, Denis

doi:10.1016/j.scs.2017.01.003

Cited by 45 publications

(21 citation statements)

References 32 publications

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“…They obtained better results with sensor fusion in both cases. In accordance with other studies, also in the study of Ekwevugbe et al [44] , where the occupancy was determined by means of both infrared camera and manually observations, better results were obtained.…”

Section: Measurementssupporting

confidence: 91%

Occupant behavior: a â€œnewâ€ factor in energy performance of buildings. Methods for its detection in houses and in offices.

Simone

Fajilla

2019

JWA

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The role that occupants have on energy consumption and performance of buildings is known, but still requires a great deal of research. In this paper, the most common techniques to detect occupancy and occupant behavior in buildings are categorized with their advantages and disadvantages. Being the buildings characterized by different energy usage, the presentation of the studies that applied surveys and monitoring campaigns is conducted with differentiation between residential and office buildings.

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Section: Measurementssupporting

confidence: 91%

Occupant behavior: a â€œnewâ€ factor in energy performance of buildings. Methods for its detection in houses and in offices.

Simone

Fajilla

2019

JWA

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“…Furthermore, a significant number of papers make use of multiple sensing methods. In applications that optimize building services, researchers make use of sensors that measure aspects of the indoor environment, such as temperature, CO 2 , luminance, humidity, acoustics (Ekwevugbe et al, 2017;Ioannidis et al, 2017;Saralegui et al, 2019;Schwee et al, 2019). In two recent studies, these are combined with the collection of user feedback (Dave et al, 2018;Romero Herrera et al, 2018).…”

Section: Literature Studymentioning

confidence: 99%

Towards a smart campus: supporting campus decisions with Internet of Things applications

Valks

Arkesteijn

Koutamanis

et al. 2020

Building Research & Information

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At universities worldwide, creating a 'smart campus' is gaining significance. This is a response to the increasingly dynamic use of the campus and the pressure on resources: energy, financial and human resources. The university community has become more mobile, student numbers more unpredictable and funding more uncertain. Consequently, campus strategies focus on resource efficiency and sharing space, requiring investment in management information to improve decision making. The Internet of Things (IoT) can help to provide big data about use patterns: by collecting real-time data on space utilization, users can make better use of current spaces and real estate managers can make better decisions about long-term demand. Furthermore, space utilisation data can be complemented with user feedback data and environmental variables, e.g. noise levels, luminance. In this research we identify the capabilities of existing IoT applications through a literature study. Literature also suggests that information from IoT applications is not utilized in organisational decision-making processes. Through four case studies we analyse these decision-making processes and identify the process-level requirements to make strategic decisions in campus management. Then we show how information from the IoT can be directly connected to these processes, thus providing a valuable addition of real-time data as input.

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“…For large scale occupancy detection, an array of carbon dioxide sensors would need to be implemented and the potentially time varying constants for each sensor estimated. Ekwevugbe et al evaluated the ability of environmental data, using added sensors measuring sound, VOC, lighting, temperature, relative humidity, and CO 2 measurements, to reflect occupancy count for an office of 6 people [26]. The data were combined and transformed using a neural network to reflect occupancy counts.…”

Section: Techniques For Measuring Occupancymentioning

confidence: 99%

Implicit Sensing of Building Occupancy Count with Information and Communication Technology Data Sets

Howard

Acha

Shah

et al. 2019

Building and Environment

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Occupancy count, i.e., the number of people in a space or building, is becoming an increasingly important measurement to model, predict, and minimize operational energy consumption. Explicit, hardware-based, occupancy counters have been proposed but wide scale adoption is limited due to the cost and invasiveness of system implementation. As an alternative approach, researchers propose using data from existing information and communication technology (ICT) systems to infer occupancy counts. In the reported work, three different data streams, security access data, wireless connectivity data, and computer activity data, from ICT systems in a medium sized office building were collected and compared to the counts of a commercially available occupancy counter over 59 working days. The occupancy counts from the ICT systems are compared to the commercial counter with and without calibration to determine the ability of the data sets to measure occupancy. Various transformations were explored as calibration techniques for the ICT data sets. Training sets of 24, 48, and 120 hours were employed to determine how long an external calibration system would need to be installed. The analysis found that calibration is required to provide accurate counts. While each ICT data set provides similar magnitudes and time series behavior, incorporating all three data streams in a two layer neural network with 1 week of training data provides the most accurate estimates against 5 performance metrics. Whilst 1 week of data provides the best results, 24 hours is sufficient to develop similar levels of performance.

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Improved occupancy monitoring in non-domestic buildings

Cited by 45 publications

References 32 publications

Occupant behavior: a â€œnewâ€ factor in energy performance of buildings. Methods for its detection in houses and in offices.

Occupant behavior: a â€œnewâ€ factor in energy performance of buildings. Methods for its detection in houses and in offices.

Towards a smart campus: supporting campus decisions with Internet of Things applications

Implicit Sensing of Building Occupancy Count with Information and Communication Technology Data Sets

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Improved occupancy monitoring in non-domestic buildings

Cited by 45 publications

References 32 publications

Occupant behavior: a â€œnewâ€ factor in energy performance of buildings. Methods for its detection in houses and in offices.

Occupant behavior: a â€œnewâ€ factor in energy performance of buildings. Methods for its detection in houses and in offices.

Towards a smart campus: supporting campus decisions with Internet of Things applications

Implicit Sensing of Building Occupancy Count with Information and Communication Technology Data Sets

Contact Info

Product

Resources

About

Occupant behavior: a â€œnewâ€ factor in energy performance of buildings. Methods for its detection in houses and in offices.

Occupant behavior: a â€œnewâ€ factor in energy performance of buildings. Methods for its detection in houses and in offices.