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Building performance is commonly calculated during the last phases of design,where most design specifications get fixed and are unlikely to be majorlymodified based on design programs. Predictive models could play a significantrole in informing architects and designers of the impact of their designdecisions on energy consumption in buildings during early design stages. Abuilding outline is a significant predictor of the final energy consumption andis conceptually determined by architects in the early design phases. This paperevaluates the impact of a building's outline on energy consumption usingsynthetic data to achieve appropriate predictive models in estimating abuilding's energy consumption. Four office outlines are selected in this study,including square, T, U, and L shapes. Besides the shape parameter, otherbuilding features commonly used in literature (i.e., Window to Wall Ratio(WWR), external wall material properties, glazing U value, windows' shadingdepth, and building orientation) are utilized in generating data distributionwith a probabilistic approach. The results show that buildings with squareshapes, in general, are more energy-efficient compared to buildings with T, U,and L shapes of the same volume. Also, T, U, and L shape samples show verysimilar behavior in terms of energy consumption. Principal Component Analysis(PCA) is applied to assess the variables' correlations on data distribution;the results show that wall material specifications explain about 40% of datavariation. Finally, we applied polynomial regression models with differentdegrees of complexity to predict the synthesized building models' energyconsumptions based on their outlines. The results show that degree 2 polynomialmodels, fitting the data over 98% R squared (coefficient of determination),could be used to predict new samples with high accuracy.

Studying Buildings Outlines to Assess and Predict Energy Performance in Buildings: A Probabilistic Approach