Zakhary, S., Rosser, J., Siebers, P.-O. et al. (2 more authors) (2021) Using unsupervised learning to partition 3D city scenes for distributed building energy microsimulation. Environment and Planning B: Urban Analytics and City Science, 48 (5). pp. 1198-1212. ISSN 2399-8083
Abstract
Microsimulation is a class of Urban Building Energy Modeling techniques in which energetic interactions between buildings are explicitly resolved. Examples include SUNtool and CitySim+, both of which employ a sophisticated radiosity-based algorithm to solve for radiation exchange. The computational cost of this algorithm increases in proportion to the square of the number of surfaces of which an urban scene is comprised. To simulate large scenes, of the order of 10,000 to 1,000,000 surfaces, it is desirable to divide the scene to distribute the simulation task. However, this partitioning is not trivial as the energy-related interactions create uneven inter-dependencies between computing nodes. To this end, we describe in this paper two approaches (K-means and Greedy Community Detection algorithms) for partitioning urban scenes, and subsequently performing building energy microsimulation using CitySim+ on a distributed memory High-Performance Computing Cluster. To compare the performance of these partitioning techniques, we propose two measures evaluating the extent to which the obtained clusters exploit data locality. We show that our approach using Greedy Community Detection performs well in terms of exploiting data locality and reducing inter-dependencies among sub-scenes, but at the expense of a higher data preparation cost and algorithm run-time.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2020. This is an author-produced version of a paper subsequently published in Environment and Planning B: Urban Analytics and City Science. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Hierarchical clustering; greedy community detection; urban scene; partitioning; scalability; building energy; microsimulation |
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: | 27 May 2020 13:51 |
Last Modified: | 16 Nov 2021 16:09 |
Status: | Published |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/2399808320914313 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161221 |