A Methodology for Daylight Optimisation of High-Rise Buildings in the Dense Urban District Using Overhang Length and Glazing Type Variables With Surrogate Modelling

dc.contributor.author Ekici, Berk
dc.contributor.author Kazanasmaz, Zehra Tuğçe
dc.contributor.author Turrin, Michela
dc.contributor.author Taşgetiren, M. Fatih
dc.contributor.author Sarıyıldız, I. Sevil
dc.coverage.doi 10.1088/1742-6596/1343/1/012133
dc.date.accessioned 2020-07-18T03:35:14Z
dc.date.available 2020-07-18T03:35:14Z
dc.date.issued 2019
dc.description International Conference on Climate Resilient Cities - Energy Efficiency and Renewables in the Digital Era 2019, CISBAT 2019 -- 4 September 2019 through 6 September 2019 en_US
dc.description.abstract Urbanization and population growth lead to the construction of higher buildings in the 21st century. This causes an increment on energy consumption as the amount of constructed floor areas is rising steadily. Integrating daylight performance in building design supports reducing the energy consumption and satisfying occupants' comfort. This study presents a methodology to optimise the daylight performance of a high-rise building located in a dense urban district. The purpose is to deal with optimisation problems by dividing the high-rise building into five zones from the ground level to the sky level, to achieve better daylight performance. Therefore, the study covers five optimization problems. Overhang length and glazing type are considered to optimise spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). A total of 500 samples in each zone are collected to develop surrogate models. A self-adaptive differential evolution algorithm is used to obtain near-optimal results for each zone. The developed surrogate models can estimate the metrics with minimum 98.25% R2 which is calculated from neural network prediction and Diva simulations. In the case study, the proposed methodology improves daylight performance of the high-rise building, decreasing ASE by approx. 27.6% and increasing the sDA values by around 88.2% in the dense urban district. © Published under licence by IOP Publishing Ltd. en_US
dc.identifier.doi 10.1088/1742-6596/1343/1/012133 en_US
dc.identifier.issn 1742-6588
dc.identifier.issn 1742-6596
dc.identifier.scopus 2-s2.0-85076257792
dc.identifier.uri https://doi.org/10.1088/1742-6596/1343/1/012133
dc.identifier.uri https://hdl.handle.net/11147/7837
dc.language.iso en en_US
dc.publisher Iop Publishing Ltd en_US
dc.relation.ispartof Journal of Physics: Conference Series en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.title A Methodology for Daylight Optimisation of High-Rise Buildings in the Dense Urban District Using Overhang Length and Glazing Type Variables With Surrogate Modelling en_US
dc.type Conference Object en_US
dspace.entity.type Publication
gdc.author.institutional Kazanasmaz, Zehra Tuğçe
gdc.bip.impulseclass C4
gdc.bip.influenceclass C5
gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::conference output
gdc.collaboration.industrial false
gdc.description.department İzmir Institute of Technology. Architecture en_US
gdc.description.issue 1 en_US
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q3
gdc.description.volume 1343 en_US
gdc.description.wosquality N/A
gdc.identifier.openalex W2991028448
gdc.identifier.wos WOS:000561852800133
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gdc.oaire.downloads 1
gdc.oaire.impulse 6.0
gdc.oaire.influence 2.8612952E-9
gdc.oaire.isgreen true
gdc.oaire.keywords 690
gdc.oaire.keywords High-Rise Building
gdc.oaire.keywords Artificial neural networks
gdc.oaire.keywords Daylight illuminance
gdc.oaire.keywords Differential Evolution
gdc.oaire.keywords Daylight
gdc.oaire.keywords Optimisation
gdc.oaire.keywords Daylighting
gdc.oaire.keywords Lighting
gdc.oaire.popularity 7.460213E-9
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gdc.oaire.sciencefields 0211 other engineering and technologies
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
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gdc.opencitations.count 4
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