A Methodology for daylight optimisation of high-rise buildings in the dense urban district using overhang length and glazing type variables with surrogate modelling

Berk Ekici, Tugce Kazanasmaz, Michela Turrin, M. Fatih Tasgetiren, Sevil Sariyildiz

Research output: Contribution to journalConference articleScientificpeer-review

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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.
Original languageEnglish
Article number012133
Number of pages6
JournalJournal of Physics: Conference Series
Volume1343
DOIs
Publication statusPublished - 2019
EventCISBAT 2019: International Conference on Climate Resilient Cities: Energy Efficiency & Renewables in the Digital Era - Lausanne, Switzerland
Duration: 4 Sep 20196 Sep 2019

Keywords

  • High-Rise Building
  • Daylight
  • Optimisation
  • Differential Evolution
  • Artificial neural networks

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