Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

Browse

Filters

2009 - 200912010 - 20192

Settings

Scopus Q: search.filters.scopusQuartile.Q1Subject: search.filters.subject.Daylighting

Search Results

Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 21
    Citation - Scopus: 27
    Exploring the Impact of External Shading System on Cognitive Task Performance, Alertness and Visual Comfort in a Daylit Workplace Environment
    (SAGE Publications Inc., 2019) Leccese, F.; Salvodori, G.; Öner, Merve; Kazanasmaz, Zehra Tuğçe
    The authors examined the effect of external shading system on cognitive performance, alertness and visual comfort of visual display terminal (VDT) users under two realistic office lighting settings in this study. Daylight was the source of illumination being considered as the most significant and preferred one. A total of 26 participants performed visual and cognitive demanding tasks as well as providing subjective alertness, performance and visual evaluations in a full-scale mock-up VDT workstation. Two trials (with and without shading system) were executed during one experimental session. Results revealed that the use of a shading system improves the performance of a user on colour-naming task requiring sustained attention, while no differential effects were observed on tasks involving other cognitive skills such as search velocity and vigilance. Within-subject performance differences were more pronounced during morning hours. Higher performance was reported in some cognitive tests when the subjective sensation of visual discomfort was lower.
  • Article
    Citation - WoS: 40
    Citation - Scopus: 51
    Three Approaches To Optimize Optical Properties and Size of a South-Facing Window for Spatial Daylight Autonomy
    (Elsevier Ltd., 2016) Kazanasmaz, Zehra Tuğçe; Grobe, Lars Oliver; Bauer, Carsten; Krehel, Marek; Wittkopf, Stephen
    This study presents optimization approaches by a recent Climate-Based-Daylight-Modeling tool, EvalDRC, to figure out the necessary area for a daylight redirecting micro-prism film (MPF) while minimizing the glazing area. The performance of a window in terms of spatial Daylight Autonomy (sDA) is optimized by its geometry and optical properties. Data implemented in simulation model are gathered through on-site measurements and Bidirectional-Scattering Distribution Function (BSDF) gonio-measurements. EvalDRC based on Radiance with a data driven model of the films' BSDF evaluates the window configurations in the whole year. The case to achieve an sDA of at least 75% is a South-facing window of a classroom in Switzerland. A window zone from 0.90 m to 1.80 m height provides view to the outside. The upper zone from 1.80 m to 3.60 m is divided into six areas of 0.30 m height in three optimization approaches including the operation of sunshades as well. First, the size of the clear glazing is incrementally reduced to find the smallest acceptable window-to-wall ratio (WWR). Second, micro-prism films are applied to an incrementally varying fraction the initial glazed area to determine the minimum film-to-window ratio (FWR). Finally, both approaches are combined for a minimum FWR and WWR. With clear glazing and WWR of 75%, the sDA of 70.2% fails to meet the requirements. An sDA of 86.4% and 80.8% can be achieved with WWR 75%, FWR 1/9 and WWR 50%, FWR 1/2 respectively. The results demonstrate the films' potential to improve the performance of windows with reduced WWR.
  • Article
    Citation - WoS: 91
    Citation - Scopus: 122
    Artificial Neural Networks To Predict Daylight Illuminance in Office Buildings
    (Elsevier Ltd., 2009) Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; Binol, Selcen
    A prediction model was developed to determine daylight illuminance for the office buildings by using artificial neural networks (ANNs). Illuminance data were collected for 3 months by applying a field measuring method. Utilizing weather data from the local weather station and building parameters from the architectural drawings, a three-layer ANN model of feed-forward type (with one output node) was constructed. Two variables for time (date, hour), 5 weather determinants (outdoor temperature, solar radiation, humidity, UV index and UV dose) and 6 building parameters (distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification) were considered as input variables. Illuminance was used as the output variable. In ANN modeling, the data were divided into two groups; the first 80 of these data sets were used for training and the remaining 20 for testing. Microsoft Excel Solver used simplex optimization method for the optimal weights. The model's performance was then measured by using the illuminance percentage error. As the prediction power of the model was almost 98%, predicted data had close matches with the measured data. The prediction results were successful within the sample measurements. The model was then subjected to sensitivity analysis to determine the relationship between the input and output variables. NeuroSolutions Software by NeuroDimensions Inc., was adopted for this application. Researchers and designers will benefit from this model in daylighting performance assessment of buildings by making predictions and comparisons and in the daylighting design process by determining illuminance.