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

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

Ekici, B. (TU Delft Design Informatics)
Kazanasmaz, Tugce (Izmir Institute of Technology)
Turrin, M. (TU Delft Design Informatics)
Tasgetiren, M. Fatih (Qatar University)
Sariyildiz, I.S. (TU Delft Design Informatics)

2019

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.

High-Rise Building
Daylight
Optimisation
Differential Evolution
Artificial neural networks

http://resolver.tudelft.nl/uuid:a552e612-e334-467e-9885-c2688f5138df

https://doi.org/10.1088/1742-6596/1343/1/012133

1742-6588

Journal of Physics: Conference Series, 1343

CISBAT 2019: International Conference on Climate Resilient Cities, 2019-09-04 → 2019-09-06, Lausanne, Switzerland

Institutional Repository

journal article

© 2019 B. Ekici, Tugce Kazanasmaz, M. Turrin, M. Fatih Tasgetiren, I.S. Sariyildiz