DUE-A: Data-driven Urban Energy Analytics for understanding relationships between building energy use and urban systems

Abstract

Abstract Cities account for over 75% of all primary energy usage in the world with buildings making up the bulk of this usage. It is well acknowledged that the building energy usage is greatly impacted by urban context and thus understanding the relationships between building energy use and surrounding urban systems is critical for more energy efficient and holistic planning. This paper proposes a Data-driven Urban Energy Analytics (DUE-A) workflow to investigate and quantify the relationships between building energy usage and the spatial proximity of other urban systems. A case study of 530 buildings in a mid-size city in the Unites States is conducted to validate the performance of the workflow and demonstrate the statistical significance of relationships between building energy use and spatial proximity of other systems. Results show that spatial proximity of other buildings, roads and trees can have both positive and negative impacts on the mean, variability and distribution of building energy usage, and indicate that more holistic planning and design of cities could unlock urban energy efficiency and low-carbon municipal pathways.