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Machine learning-based evaluation of dynamic thermal-tempering performance and thermal diversity for 107 Cambridge courtyards

Peng, Zhikai and Debnath, Ramit and Bardhan, Ronita and Steemers, Koen (2023) Machine learning-based evaluation of dynamic thermal-tempering performance and thermal diversity for 107 Cambridge courtyards. Sustainable Cities and Society, 88 . Art. No. 104275. ISSN 2210-6707. doi:10.1016/j.scs.2022.104275.

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The dynamic thermal conditions profoundly impact on the quality of physical, cultural, and social experiences in courtyard spaces. This research aims to identify the microclimatic dissimilarities between courtyards in terms of tempering seasonal–diurnal thermal extremes and enriching ground-level thermal textures. The methodology included field measurements in summer-2021 and winter-2022 in Cambridge, UK; microclimatic simulations of 107 courtyards in ENVI-met and model validations; and machine learning-driven clustering using Super Organising Maps (SuperSOM). The results indicate that the diurnal thermal range of the spatial-UTCI mean in summer (DTR (M) < 24°C) is double that in winter (DTR (M) < 12°C); meanwhile the maximum spatial-UTCI deviation is three times as significant (δ > 3°C at 7:00 BST versus δ > 1°C at 12:00 GMT). SuperSOM analysis was performed using K-means and hierarchical agglomerative clustering to partition all courtyards into seven subclusters on its graph-lattice structure. Clusters Km_I, Hac_I, and Hac_IV feature a positive synergy between the thermal-tempering and thermal-enriching potentials. In contrast, the other four clusters exhibit conflicting scenarios during the day and night across the two seasons analysed. These data-driven outcomes enabled us to optimise spatial and landscape strategies for designing and retrofitting courtyard microclimates, contributing to the current discussions on climate-responsive and sensation-inclusive design in historical urban contexts.

Item Type:Article
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URLURL TypeDescription
Peng, Zhikai0000-0001-6449-6621
Debnath, Ramit0000-0003-0727-5683
Bardhan, Ronita0000-0001-5336-4084
Steemers, Koen0000-0001-8135-158X
Additional Information:We would like to take this opportunity to acknowledge the time and effort devoted by the editors and reviewers to improving the quality of this published work. Additionally, ZP would like to thank Mr. Joseph Racosky from Nielsen-Kellerman Ltd and Mr. David Ellis from Richard Paul Russell Ltd for loaning the Kestrel instrument as well as Churchill College for providing a safe environment for the measurement campaigns in 2021 and 2022. RD would like to thank Quadrature Climate Foundation, Laudes Foundation and Churchill College for the support.
Funding AgencyGrant Number
Quadrature Climate FoundationUNSPECIFIED
Laudes FoundationUNSPECIFIED
Churchill CollegeUNSPECIFIED
Record Number:CaltechAUTHORS:20221122-564647900.14
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117989
Deposited By: Research Services Depository
Deposited On:07 Dec 2022 17:58
Last Modified:07 Dec 2022 17:58

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