Deep neural networks as surrogate models for urban energy simulations

Abstract

Building energy simulation helps governments implement effective policies to increase energy efficiency. In this work, we use deep neural networks (DNN) to create a surrogate model of an urban energy simulator. We modelled 7,860 buildings, with 2,620 geometries, and simulated them across all the climatic regions of the US. With these 68 million hourly data points, we trained two DNNs to predict the solar gains and thermal losses. The DNNs reduce computational time by a factor of 2500 while maintaining good accuracy (R2=0.85). Possible applications are prediction of energy demand due to climate change and building refurbishment measures.