Convolutional versus Dense Neural Networks: Comparing the Two Neural Networks Performance in Predicting Building Operational Energy Use Based on the Building Shape

TL;DR

This study compares Dense Neural Networks and Convolutional Neural Networks for predicting building energy use based on shape, finding DNNs outperform CNNs in accuracy and efficiency, while CNNs offer better visual communication.

Contribution

It provides a comparative analysis of DNN and CNN architectures specifically for building energy prediction based on shape, highlighting their respective advantages.

Findings

DNN outperforms CNN in accuracy and computation time.

CNN benefits include better visualization for design communication.

DNN is simpler and more efficient for energy prediction tasks.

Abstract

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.