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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.

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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

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.