Architecture / Mimarlık
Permanent URI for this collectionhttps://hdl.handle.net/11147/24
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Article Citation - WoS: 119Citation - Scopus: 130Identification of the Building Parameters That Influence Heating and Cooling Energy Loads for Apartment Buildings in Hot-Humid Climates(Elsevier Ltd., 2011) Yıldız, Yusuf; Durmuş Arsan, ZeynepIdentifying the building parameters that significantly impact energy performance is an important step for enabling the reduction of the heating and cooling energy loads of apartment buildings in the design stage. Implementing passive design techniques for these buildings is not a simple task in most dense cities; their energy performance usually depends on uncertainties in the local climate and many building parameters, such as window size, zone height, and features of materials. For this paper, a sensitivity analysis was performed to determine the most significant parameters for buildings in hot-humid climates by considering the design of an existing apartment building in Izmir, Turkey. The Monte Carlo method is selected for sensitivity and uncertainty analyses with the Latin hypercube sampling (LHC) technique. The results show that the sensitivity of parameters in apartment buildings varies based on the purpose of the energy loads and locations in the building, such as the ground, intermediate, and top floors. In addition, the total window area, the heat transfer coefficient (U) and the solar heat gain coefficient (SHGC) of the glazing based on the orientation have the most considerable influence on the energy performance of apartment buildings in hot-humid climates.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.