a b s t r a c tA 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.

a b s t r a c tThe several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.

a b s t r a c tThis 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) goniomeasurements. 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, microprism 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.

a b s t r a c tBy considering the energy efficiency legislations among the European Union, Turkey is responsible to provide regulations to comply for the latest European Energy Performance of Buildings Directive 2010/31/EC. New legislation in Turkey requires information about the evaluation of energy performance of existing buildings. This study aimed to determine energy performance of residential buildings in Izmir, regarding significant relationships between their performance and architectural configuration through statistical analysis. The focus was on the heating energy consumption due to Energy Efficiency Law (2007) and Building Energy Performance Regulation (2008), and Standard Assessment Method for Energy Performance of Dwellings (KEP-SDM). This energy performance assessment method was based on Turkish standard TS 825, and European standard EN ISO 13790. It is known that architectural configuration of buildings and design norms have impact on energy performance of buildings. However, emphasis was given on significant values of architectural considerations through certain area-based ratios. The levels of these ratios were matched with the levels of energy consumption. By this, the consideration was to take early-precautions against high energy consumptions in the early design stage and to enhance legislation by adding recommendations of concrete architectural values. These would assist to predict the level of energy performance in the early design phase. Findings would provide feedback information on the residential building stock inİzmir, Turkey.

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