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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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Proceedings ArticleDOI
24 Oct 2016
TL;DR: It is demonstrated that it is highly effective to estimate building occupancy information using building environment data, and then to further use the information on occupancy to provide traffic predictions in the proximate area.
Abstract: Traffic prediction, particularly in urban regions, is an important application of tremendous practical value. In this paper, we report a novel and interesting case study of urban traffic prediction in Central, Hong Kong, one of the densest urban areas in the world. The novelty of our study is that we make good second use of inexpensive big data collected from the Hong Kong International Commerce Centre (ICC), a 118-story building in Hong Kong where more than 10,000 people work. As building environment data are much cheaper to obtain than traffic data, we demonstrate that it is highly effective to estimate building occupancy information using building environment data, and then to further use the information on occupancy to provide traffic predictions in the proximate area. Scientifically, we investigate how and to what extent building data can complement traffic data in predicting traffic. In general, this study sheds new light on the development of accurate data mining applications through the second use of inexpensive big data.

25 citations

Journal ArticleDOI
15 Oct 2020-Energies
TL;DR: Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort.
Abstract: Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control.

25 citations

Journal ArticleDOI
TL;DR: Evaluating the effect of habitat connectivity on the patch occupancy of 13 habitat specialists from three different insect orders in fragmented calcareous grasslands found that functional connectivity measures provided better results than the non-functional approach and future metapopulations should use functional rather than Euclidean distances whenever possible.

25 citations

Journal ArticleDOI
TL;DR: This article conducted call-broadcast surveys for raptors at a set of 155 survey points within the business park landscape of the St. Louis area to answer the question of what landscape characteristics of business parks are predictors of presence of raptors.
Abstract: Various species of raptors, or birds of prey, have been found living and hunting in all parts of the urban environment, including complexes of business and light industrial facilities, commonly known as business parks. Conservation in business parks is a growing concern due to the amount of land they occupy and their pattern of development of formerly vacant land on the fringes of urban areas. We conducted call-broadcast surveys for raptors at a set of 155 survey points within the business park landscape of the St. Louis area to answer the question of what landscape characteristics of business parks are predictors of presence of raptors. The detection data from these surveys was used to model the occupancy probability of the target species of raptor at each survey site and to determine the effect of landscape variables at each site on occupancy probabilities. We detected nine species of raptors at business parks. The red-tailed hawk (Buteo jamaicensis), the American kestrel (Falco sparverius), and the Cooper’s hawk (Accipiter cooperii) were the most commonly detected species. The probability of raptor occupation was negatively associated with lawn cover. Occupancy probabilities of red-tailed hawks and Cooper’s hawks were positively associated with woodland cover, and American kestrel occupancy probability was positively associated with grassland cover. Based on this study, we would recommend that businesses concerned with the conservation of raptors in and around their properties plan to develop less lawn area, preserve or plant more native grassland, and preserve woodlots.

25 citations

Journal ArticleDOI
TL;DR: It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%, and the importance of including occupant-related data in residential archetypes is demonstrated by applying the methodology to the UK national building stock.

25 citations


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