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Title: Performance evaluation of an agent-based occupancy simulation model

Abstract

Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. Formore » future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.« less

Authors:
 [1];  [2];  [3]; ORCiD logo [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Carnegie Mellon Univ., Pittsburgh, PA (United States)
  2. Carnegie Mellon Univ., Pittsburgh, PA (United States)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1436624
Alternate Identifier(s):
OSTI ID: 1412554
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Building and Environment
Additional Journal Information:
Journal Volume: 115; Journal Issue: C; Journal ID: ISSN 0360-1323
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats

Luo, Xuan, Lam, Khee Poh, Chen, Yixing, and Hong, Tianzhen. Performance evaluation of an agent-based occupancy simulation model. United States: N. p., 2017. Web. doi:10.1016/j.buildenv.2017.01.015.
Luo, Xuan, Lam, Khee Poh, Chen, Yixing, & Hong, Tianzhen. Performance evaluation of an agent-based occupancy simulation model. United States. doi:10.1016/j.buildenv.2017.01.015.
Luo, Xuan, Lam, Khee Poh, Chen, Yixing, and Hong, Tianzhen. Tue . "Performance evaluation of an agent-based occupancy simulation model". United States. doi:10.1016/j.buildenv.2017.01.015. https://www.osti.gov/servlets/purl/1436624.
@article{osti_1436624,
title = {Performance evaluation of an agent-based occupancy simulation model},
author = {Luo, Xuan and Lam, Khee Poh and Chen, Yixing and Hong, Tianzhen},
abstractNote = {Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.},
doi = {10.1016/j.buildenv.2017.01.015},
journal = {Building and Environment},
number = C,
volume = 115,
place = {United States},
year = {Tue Jan 17 00:00:00 EST 2017},
month = {Tue Jan 17 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 11works
Citation information provided by
Web of Science

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  • Cited by 6
  • 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 work 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 distributionmore » 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.« less
    Cited by 1
  • An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. The models are translations of basic elements of an established system dynamics model of diabetes. In this model hierarchy, which mimics diabetes progression over an aggregated U.S. population, was dis-aggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. Moreover, the four estimated models attempted to replicate stock counts representing disease statesmore » in the system dynamics model, while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. In all three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model. The performances of the baseline agent-based model and its extensions illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.« less
  • The widespread use of organophosphorus compounds (OP) as pesticides and the repeated misuse of highly toxic OP as chemical warfare agents (nerve agents) emphasize the necessity for the development of effective medical countermeasures. Standard treatment with atropine and the established acetylcholinesterase (AChE) reactivators, obidoxime and pralidoxime, is considered to be ineffective with certain nerve agents due to low oxime effectiveness. From obvious ethical reasons only animal experiments can be used to evaluate new oximes as nerve agent antidotes. However, the extrapolation of data from animal to humans is hampered by marked species differences. Since reactivation of OP-inhibited AChE is consideredmore » to be the main mechanism of action of oximes, human erythrocyte AChE can be exploited to test the efficacy of new oximes. By combining enzyme kinetics (inhibition, reactivation, aging) with OP toxicokinetics and oxime pharmacokinetics a dynamic in vitro model was developed which allows the calculation of AChE activities at different scenarios. This model was validated with data from pesticide-poisoned patients and simulations were performed for intravenous and percutaneous nerve agent exposure and intramuscular oxime treatment using published data. The model presented may serve as a tool for defining effective oxime concentrations and for optimizing oxime treatment. In addition, this model can be useful for the development of meaningful therapeutic animal models.« less