Architecture / Mimarlık
Permanent URI for this collectionhttps://hdl.handle.net/11147/24
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Article Citation - WoS: 40Citation - Scopus: 51Three 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, StephenThis 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: 91Citation - Scopus: 122Artificial Neural Networks To Predict Daylight Illuminance in Office Buildings(Elsevier Ltd., 2009) Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; Binol, SelcenA 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.