  Building performance is commonly calculated during the last phases of design,
where most design specifications get fixed and are unlikely to be majorly
modified based on design programs. Predictive models could play a significant
role in informing architects and designers of the impact of their design
decisions on energy consumption in buildings during early design stages. A
building outline is a significant predictor of the final energy consumption and
is conceptually determined by architects in the early design phases. This paper
evaluates the impact of a building's outline on energy consumption using
synthetic data to achieve appropriate predictive models in estimating a
building's energy consumption. Four office outlines are selected in this study,
including square, T, U, and L shapes. Besides the shape parameter, other
building features commonly used in literature (i.e., Window to Wall Ratio
(WWR), external wall material properties, glazing U value, windows' shading
depth, and building orientation) are utilized in generating data distribution
with a probabilistic approach. The results show that buildings with square
shapes, 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 very
similar 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 data
variation. Finally, we applied polynomial regression models with different
degrees of complexity to predict the synthesized building models' energy
consumptions based on their outlines. The results show that degree 2 polynomial
models, fitting the data over 98% R squared (coefficient of determination),
could be used to predict new samples with high accuracy.
