Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Conference Object
    A General Predictive Model to Evaluate Daylight Levels of Residential Buildings in the Mediterranean (Next Med) Region
    (Education and Research in Computer Aided Architectural Design in Europe, 2025) Ekici, B.
    Conceptual design is one of the most critical phases, as design decisions affect the buildings’ performance throughout their life cycle. Researchers consider various computational methods to achieve effective design proposals. Nevertheless, optimization algorithms are necessary to cope with the complexity and increase the efficiency of design alternatives in various aspects. In sustainable building design, these decisions require computationally expensive processes due to the simulation tasks. Besides, making sustainable design decisions is even more challenging in a Mediterranean climate due to changing conditions throughout the year. Therefore, recent studies frequently consider combining predictive models with optimization algorithms to decrease the burden of expensive simulation time. Relevant works present promising outcomes, yet they are limited to predicting the building performance of specific cases; thus, the proposed predictive models are limited to different design problems. This paper investigates the development of a general machine learning (ML) model to overcome this issue. With this motivation, a parametric test box consisting of twenty parameters related to weather data of twelve Mediterranean (Next Med) countries, space dimensions, vertical/horizontal louvers, and material type is developed using Grasshopper 3d. Moreover, a parametric urban model, which considers eight parameters related to the density of the surrounding buildings, is also created to generate numerous environments. The LadyBug tools simulate the daylight autonomy to generate 12,000 samples. Five different ML models involving artificial neural networks (ANN) are built in Python. Statistical results showed that train and test scores achieved promising outcomes in all ML models. However, when predicting user-defined scenarios not involved in the generated dataset, only ANNs perform generalizable, accurate predictions. The paper discusses the ability of ANN models to accurately predict different design scenarios and locations, and the trustworthiness of the training and test scores based only on collected data. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.
  • Article
    Citation - WoS: 7
    Citation - Scopus: 7
    Applicability of a Prismatic Panel To Optimize Window Size and Depth of a South-Facing Room for a Better Daylight Performance
    (Znack Publishing House, 2020) Köse, Büşra; Kazanasmaz, Zehra Tuğçe
    This study examines the performance of attached prismatic panels, which have shading capability, in a side-lit deep plan room to find out the least possible WWR value in relation to room depth satisfying the required daylight availability. The methodology is based on simulating a base model in Relux and testing it with alternative models composed of incrementally defined WWR and room depth values. In accordance with minimum IES requirements, the most satisfying sDA value was found to be 48.54 % in a room of 12 m depth with 67 % WWR. An sDA of 51.59 % and 59.26 % was achieved in a room of 9m depth with 43 % WWR and 6m depth with 30 % WWR, respectively. The least ASE values were obtained with the least WWR alternative of 30 % in all room depths. This study presents a new approach with the consideration of innovative daylight redirecting systems to propose revisions for the requirements mentioned in standards about daylight in buildings but based on conventional fenestration systems. © 2020, LLC Editorial of Journal ""Light Technik"". All rights reserved.