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 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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This study performed with the purpose of constructing and validating a model named OptimLUM (Optimizing Luminaire Layouts) to estimate the most accurate location, number and type of artificial light sources according to average illuminance and maximum uniformity in an office. OptimLUM is appling through Excel Spreadsheet to develop the model and uses Evolver, which is basing on genetic algorithm to implement optimization routine. To validate the reliability of the proposed model, luminaire layout scenairos generated for two types of luminaires after taking illuminance measurements in an actual office. OptimLUM illuminance values were comparing statistically with measurement and DIALux results to test the applicability of the model. The model performance is highly accurate in determining luminaire positions: coefficient of determination R2 and coefficient of variation CV were equal to (86–99)% and to (0.04–0.12) respectively, and for all scenarios. Its outputs are closer to the actual measurements when compared with DIALux outputs.