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Occupancy

About: Occupancy is a research topic. Over the lifetime, 2757 publications have been published within this topic receiving 68288 citations.


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Journal ArticleDOI
TL;DR: There is a need and an opportunity for both industry and universities to increase their efforts with respect to building physics education, and if this is done, a part of the performance gap could be rapidly closed.
Abstract: One of the most discussed issues in the design community is the performance gap. In this research, we investigate for the first time whether part of the gap might be caused by the modelling literacy of design teams. A total of 108 building modellers were asked to comment on the importance of obtaining and using accurate values for 21 common modelling input variables, from U-values to occupancy schedules when using dynamic simulation to estimate annual energy demand. The questioning was based on a real building for which high-resolution energy, occupancy and temperature data were recorded. A sensitivity analysis was then conducted using a model of the building (based on the measured data) by perturbing one parameter in each simulation. The effect of each perturbation on the annual energy consumption given by the model was found and a ranked list generated. The order of this list was then compared to that given by the modellers for the same changes in the parameters. A correlation analysis indicated little ...

95 citations

21 Apr 2014
TL;DR: In this paper, the authors used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices.
Abstract: Occupancy profile is one of the driving factors behind discrepancies between the measured and simulated energy consumption of buildings. The frequencies of occupants leaving their offices and the corresponding durations of absences have significant impact on energy use and the operational controls of buildings. This study used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices. Five typical occupancy patterns were identified based on the average daily 24-hour profiles of the presence of occupants in their cubicles. These statistical patterns were represented by a one-square curve, a one-valley curve, a two-valley curve, a variable curve, and a flat curve. The key parameters that define the occupancy model are the average occupancy profile together with probability distributions of absence duration, and the number of times an occupant is absent from the cubicle. The statistical results also reveal that the number of absence occurrences decreases as total daily presence hours decrease, and the duration of absence from the cubicle decreases as the frequency of absence increases. The developed occupancy model captures the stochastic nature of occupants moving in and out of cubicles, and can be used to generate a more realistic occupancy schedule. This is crucial for improving the evaluation of the energy saving potential of occupancy based technologies and controls using building simulations. Finally, to demonstrate the use of the occupancy model, weekday occupant schedules were generated and discussed.

95 citations

Journal ArticleDOI
TL;DR: In this article, the authors present an Occupancy Simulator that simulates the stochastic behavior of occupant presence and movement in buildings, capturing the spatial and temporal occupancy diversity, each occupant and each space in the building are explicitly simulated as an agent with their profiles of stochastically behaviors.
Abstract: Occupancy has significant impacts on building performance. However, in current building performance simulation programs, occupancy inputs are static and lack diversity, contributing to discrepancies between the simulated and actual building performance. This paper presents an Occupancy Simulator that simulates the stochastic behavior of occupant presence and movement in buildings, capturing the spatial and temporal occupancy diversity. Each occupant and each space in the building are explicitly simulated as an agent with their profiles of stochastic behaviors. The occupancy behaviors are represented with three types of models: (1) the status transition events (e.g., first arrival in office) simulated with probability distribution model, (2) the random moving events (e.g., from one office to another) simulated with a homogeneous Markov chain model, and (3) the meeting events simulated with a new stochastic model. A hierarchical data model was developed for the Occupancy Simulator, which reduces the amount of data input by using the concepts of occupant types and space types. Finally, a case study of a small office building is presented to demonstrate the use of the Simulator to generate detailed annual sub-hourly occupant schedules for individual spaces and the whole building. The Simulator is a web application freely available to the public and capable of performing a detailed stochastic simulation of occupant presence and movement in buildings. Future work includes enhancements in the meeting event model, consideration of personal absent days, verification and validation of the simulated occupancy results, and expansion for use with residential buildings.

94 citations

Journal ArticleDOI
TL;DR: In this paper, a hierarchical Bayes multi-scale occupancy model was proposed to simultaneously estimate site occupancy, use, and detectability, while accounting for spatial dependence through a state-space approach based on repeated samples at multiple spatial or temporal scales.
Abstract: Summary 1. Understanding the distribution and ecology of episodic or mobile species requires us to address multiple potential biases, including spatial clustering of survey locations, imperfect detectability and partial availability for detection. These challenges have been addressed individually by previous modelling approaches, but there is currently no extension of the occupancy modelling framework that accounts for all three problems while estimating occupancy (ψ), availability for detection (i.e. use; θ) and detectability (P). 2. We describe a hierarchical Bayes multi-scale occupancy model that simultaneously estimates site occupancy, use, and detectability, while accounting for spatial dependence through a state-space approach based on repeated samples at multiple spatial or temporal scales. As an example application, we analyse the spatiotemporal distribution of the Louisiana waterthrush Seiurus motacilla with respect to catchment size and availability of potential prey based on data collected along Appalachian streams of southern West Virginia, USA. In spring 2009, single observers recorded detections of Louisiana waterthrush (henceforth, waterthrush) within 75 m of point-count stations (i.e. sites) during four 5-min surveys per site, with each survey broken into 1-min intervals. 3. Waterthrushes were widely distributed (ψ range: 0·6–1·0) and were regularly using (θ range: 0·4–0·6) count circles along forested mountain streams. While accounting for detection biases and spatial dependence among nearby sampling sites, waterthrushes became more common as catchment area increased, and they became more available for detection as the per cent of the benthic macroinvertebrates that were of the orders Ephemeroptera, Plecoptera or Trichoptera (EPT) increased. These results lend some support to the hypothesis that waterthrushes are influenced by instream conditions as mediated by watershed size and benthic macroinvertebrate community composition. 4. Synthesis and applications. Although several available modelling techniques provide estimates of occupancy at one scale, hierarchical Bayes multi-scale occupancy modelling provides estimates of distribution at two scales simultaneously while accounting for detection biases and spatial dependencies. Hierarchical Bayes multi-scale occupancy models therefore hold significant potential for addressing complex conservation threats that operate at a landscape scale (e.g. climate change) and probably influence species distributions over multiple scales.

93 citations

Journal ArticleDOI
TL;DR: In this paper, a virtual reality (VR) integrated approach was developed to improve occupancy information integrity in the building energy design process in order to close the performance gap, which leads to inappropriate design, unnecessary consumption and operation misconducts of occupants.

93 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023669
20221,420
2021234
2020217
2019236
2018209