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Revealing occupancy patterns in an office building through the use of occupancy sensor data
Graphical abstract
Highlights PIR motion sensor data was analyzed to identify occupant behavior. A total of 629 sensors were analyzed in the large commercial office building. Private offices were analyzed in detail to identify changes throughout the year. A 46% difference was found between private offices and ASHRAE reference occupancy.
Abstract 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 same 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.
Revealing occupancy patterns in an office building through the use of occupancy sensor data
Graphical abstract
Highlights PIR motion sensor data was analyzed to identify occupant behavior. A total of 629 sensors were analyzed in the large commercial office building. Private offices were analyzed in detail to identify changes throughout the year. A 46% difference was found between private offices and ASHRAE reference occupancy.
Abstract 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 same 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.
Revealing occupancy patterns in an office building through the use of occupancy sensor data
Duarte, Carlos (author) / Van Den Wymelenberg, Kevin (author) / Rieger, Craig (author)
Energy and Buildings ; 67 ; 587-595
2013-08-27
9 pages
Article (Journal)
Electronic Resource
English
Revealing occupancy patterns in an office building through the use of occupancy sensor data
| Online Contents | 2013
Building occupancy detection through sensor belief networks
| Elsevier | 2005