B
Basak Kundakci Koyunbaba
Researcher at Yaşar University
Publications - 10
Citations - 475
Basak Kundakci Koyunbaba is an academic researcher from Yaşar University. The author has contributed to research in topics: Trombe wall & Evolutionary computation. The author has an hindex of 6, co-authored 7 publications receiving 367 citations. Previous affiliations of Basak Kundakci Koyunbaba include Energy Institute.
Papers
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Journal ArticleDOI
Review of simulation modeling for shading devices in buildings
TL;DR: In this article, the authors focused on the shading device types used in the building sector and the previous studies done for designating the performance aspects of different shading devices types and reviewed the importance of simulation modeling for shading devices in buildings.
Journal ArticleDOI
The comparison of Trombe wall systems with single glass, double glass and PV panels
TL;DR: In this paper, the energy performance comparison of single glass, double glass and a-Si semi-transparent PV module integrated on the Trombe wall facade of a model test room built in Izmir, Turkey has been carried out.
Journal ArticleDOI
An approach for energy modeling of a building integrated photovoltaic (BIPV) Trombe wall system
TL;DR: In this paper, an attempt has been made to validate the simulation model with experimental results of a model BIPV Trombe wall built in Izmir, Turkey, based on transient condition.
Proceedings ArticleDOI
Multi-objective diagrid façade optimization using differential evolution
TL;DR: This study considers façade design as a multiobjective optimization problem, integrating diverse design criteria, namely indoor daylight distribution, structural performance and cost, and uses Differential Evolution (DE) to search for best-tradeoff solutions.
Proceedings ArticleDOI
Multi-objective optimization for shading devices in buildings by using evolutionary algorithms
Ayca Kirimtat,Basak Kundakci Koyunbaba,Ioannis Chatzikonstantinou,Sevil Sariyildiz,Ponnuthurai Nagaratnam Suganthan +4 more
TL;DR: A multi-objective self-adaptive differential evolution algorithm (jDEMO), inspired from the DEMO algorithm from the literature with some modifications, is developed and compared to the well-known fast and non-dominated sorting genetic algorithm so called NSGA-II in order to solve this complex problem and identify alternative design solutions to decision makers.