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
Title of host publicationCISBAT 2019 - International Scientific Conference
Subtitle of host publicationClimate Resilient Cities - Energy Efficiency & Renewables in the Digital Era
Publication statusAccepted/In press - 2019
EventCISBAT 2019: International Conference on Climate Resilient Cities: Energy Efficiency & Renewables in the Digital Era - Lausanne, Switzerland
Duration: 4 Sep 20196 Sep 2019

Conference

ConferenceCISBAT 2019: International Conference on Climate Resilient Cities
CountrySwitzerland
CityLausanne
Period4/09/196/09/19

    Research areas

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

ID: 56195030