  A building self-shading shape impacts substantially on the amount of direct
sunlight received by the building and contributes significantly to building
operational energy use, in addition to other major contributing variables, such
as materials and window-to-wall ratios. Deep Learning has the potential to
assist designers and engineers by efficiently predicting building energy
performance. This paper assesses the applicability of two different neural
networks structures, Dense Neural Network (DNN) and Convolutional Neural
Network (CNN), for predicting building operational energy use with respect to
building shape. The comparison between the two neural networks shows that the
DNN model surpasses the CNN model in performance, simplicity, and computation
time. However, image-based CNN has the benefit of utilizing architectural
graphics that facilitates design communication.
