TY - JOUR
T1 - A Methodology for daylight optimisation of high-rise buildings in the dense urban district using overhang length and glazing type variables with surrogate modelling
AU - Ekici, Berk
AU - Kazanasmaz, Tugce
AU - Turrin, Michela
AU - Tasgetiren, M. Fatih
AU - Sariyildiz, Sevil
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - High-Rise Building
KW - Daylight
KW - Optimisation
KW - Differential Evolution
KW - Artificial neural networks
UR - http://www.scopus.com/inward/record.url?scp=85076257792&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1343/1/012133
DO - 10.1088/1742-6596/1343/1/012133
M3 - Conference article
SN - 1742-6588
VL - 1343
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
M1 - 012133
T2 - CISBAT 2019: International Conference on Climate Resilient Cities
Y2 - 4 September 2019 through 6 September 2019
ER -