Chapman, J., Siebers, P.O. and Robinson, D. (2018) On the multi-agent stochastic simulation of occupants in buildings. Journal of Building Performance Simulation, 11 (5). pp. 604-621. ISSN 1940-1493
Abstract
This paper introduces a new general platform for the simulation of occupants' presence and behaviours. Called No-MASS (Nottingham Multi-Agent Stochastic Simulation), this generates a synthetic population of agents, predicts their presence and, in the case of residences also their activities and inferred locations, as well as their use of windows, lights and blinds. Using the Functional Mockup Interface, No-MASS is coupled with EnergyPlus: EnergyPlus parses environmental parameters to No-MASS which in turn parses back the energetic consequences of agents' behaviours. After describing the architecture of No-MASS and the form of the integrated models, we demonstrate its utility through two use cases: a house and an office. We close by outlining how No-MASS has been extended to more comprehensively simulate the behaviours of agents occupying multiple buildings, including behaviours for which data is scarce, social interactions between agents, and a generalization of No-MASS to simulate electrical devices and their interactions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 International Building Performance Simulation Association (IBPSA). This is an author produced version of a paper subsequently published in Journal of Building Performance Simulation. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | multi-agent; stochastic; behaviour; energy; simulation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Architecture (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Feb 2018 12:06 |
Last Modified: | 14 Jul 2020 08:04 |
Published Version: | https://doi.org/10.1080/19401493.2017.1417483 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/19401493.2017.1417483 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127460 |