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Title: Simulation of occupancy in buildings

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Journal Article: Publisher's Accepted Manuscript
Journal Name:
Energy and Buildings
Additional Journal Information:
Journal Volume: 87; Journal Issue: C; Related Information: CHORUS Timestamp: 2016-09-04 14:48:21; Journal ID: ISSN 0378-7788
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Citation Formats

Feng, Xiaohang, Yan, Da, and Hong, Tianzhen. Simulation of occupancy in buildings. Netherlands: N. p., 2015. Web. doi:10.1016/j.enbuild.2014.11.067.
Feng, Xiaohang, Yan, Da, & Hong, Tianzhen. Simulation of occupancy in buildings. Netherlands. doi:10.1016/j.enbuild.2014.11.067.
Feng, Xiaohang, Yan, Da, and Hong, Tianzhen. 2015. "Simulation of occupancy in buildings". Netherlands. doi:10.1016/j.enbuild.2014.11.067.
title = {Simulation of occupancy in buildings},
author = {Feng, Xiaohang and Yan, Da and Hong, Tianzhen},
abstractNote = {},
doi = {10.1016/j.enbuild.2014.11.067},
journal = {Energy and Buildings},
number = C,
volume = 87,
place = {Netherlands},
year = 2015,
month = 1

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.enbuild.2014.11.067

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Cited by: 60works
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  • Three houses of similar floor plan are being compared for energy consumption. The first house is a typical builder house of 2400 ft2 (223 m2) in east Tennessee. The second house contains retrofits available to a home owner such as energy efficient appliances, windows and HVAC, as well as an insulated attic which contains HVAC duct work. The third house was built using optimum-value framing construction with photovoltaic modules and solar water heating. To consume energy researchers have set up appliances, lights, and plug loads to turn on and off automatically according to a schedule based on the Building Americamore » Research Benchmark Definition. As energy efficiency continues to be a focus for protecting the environment and conserving resources, experiments involving whole house energy consumption will be done. In these cases it is important to understand how to simulate occupancy so that data represents only house performance and not human behavior. The process for achieving automated occupancy simulation will be discussed. Data comparing the energy use of each house will be presented and it will be shown that the third house used 66% less and the second house used 36% less energy than the control house in 2010. The authors will discuss how energy prudent living habits can further reduce energy use in the third house by 23% over the average American family living in the same house.« less
  • Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an officemore » building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.« less
  • Energy simulation programs like DOE-2 and EnergyPlus are tools that have been proven to aid with energy calculations to predict energy use in buildings. Some inputs to energy simulation models are relatively easy to find, including building size, orientation, construction materials, and HVAC system size and type. Others vary with time (e.g. weather and occupancy) and some can be a challenge to estimate in order to create an accurate simulation. In this paper, the analysis of occupancy sensor data for a large commercial, multi-tenant office building is presented. It details occupancy diversity factors for private offices and summarizes the samemore » for open offices, hallways, conference rooms, break rooms, and restrooms in order to better inform energy simulation parameters. Long-term data were collected allowing results to be presented to show variations of occupancy diversity factors in private offices for time of day, day of the week, holidays, and month of the year. The diversity factors presented differ as much as 46% from those currently published in ASHRAE 90.1 2004 energy cost method guidelines, a document referenced by energy modelers regarding occupancy diversity factors for simulations. This may result in misleading simulation results and may introduce inefficiencies in the final equipment and systems design.« less