This App simulates occupant movement in a building using the Markov-chain model [1, 2].
The App takes high level input on occupants, spaces and events, then simulates occupant movement and generates
occupant schedules for each space. The generated schedules capture diversity and stochastic nature of occupant
activities. These schedules can be downloaded and used for building simulation.
The detailed algorithms are introduced in Chen, et al. [3], and performance evaluation of the model is presented in Xuan, et al.[4] .
The development of the App is sponsored by Department of Energy, under the U.S.-China
Clean Energy Research Center on Building Energy Efficiency
(CERC-BEE).
It is also a research activity under the
IEA EBC Annex 66:
Definition and simulation of occupant behavior in buildings.
Other occupant behavior research work conducted by Tianzhen Hong and his team is described at
Behavior.lbl.gov.
Getting Started Tutorial: PDF.
[1] C. Wang, D. Yan, Y. Jiang (2011). A novel approach for building occupancy simulation. Building Simulation, 4(2): 149-167, 2011.
[2] X. Feng, D. Yan, T. Hong (2015). Simulation of occupancy in buildings. Energy and Buildings, 87: 348-359, 2015.
[3] Y. Chen, T. Hong, X. Luo (2017). An Agent-Based Stochastic Occupancy Simulator, Building Simulation, May 2017, DOI: 10.1007/s12273-017-0379-7.
[4] X. Luo, K.P. Lam, Y. Chen, T. Hong (2017). Performance Evaluation of an Agent-based Occupancy Simulation Model. Building and Environment, January 2017, DOI: 10.1016/j.buildenv.2017.01.015.
[5] Y. Chen, X. Liang, T. Hong, X. Luo (2017). Simulation and visualization of energy-related occupant behavior in office buildings. Building Simulation, Feb, 2017, DOI: 10.1007/s12273-017-0355-2.
The Occupancy Simulator in current form works for Commercial Buildings only.
There are limitations in the current implementation of the Occupancy Simulator, including:
There are limitations in the current implementation of the Occupancy Simulator, including:
- Not supporting building types of residential, hotel and school.
- Not considering occupants’ walking time or path from one space to another.
- Handling public holidays at the building level without considering personal absent workdays (e.g., sick leaves, vacations, work from home days, or business trips).
- Occupants’ presence and movement are assumed to be independent of indoor space environmental conditions (i.e., not influenced by space temperature, humidity, or indoor air quality).
- The model of the meeting room is based on google-calendar schedule data of a single meeting room, which can be improved in future with more ground truth occupancy data for more meeting rooms.