Many urban energy use modeling tools and methods have been developed to understand
energy use in cities, but often have limitations in aggregating across multiple scales and end-
uses, which adversely affects accuracy and utility. Increased data availability and
developments in machine learning (ML) provide new possibilities for improving the accuracy
and complexity of urban energy use models. This paper presents an integrated framework
for urban energy use modeling (UEUM) that localizes energy performance data, considers …

Accurate measurement and analysis of the urban energy use is a fundamental step in
development of low-carbon cities. Despite the immense development of urban energy use
modeling tools and methods, they have limitations in representing a realistic urban scale
energy model. In addition to the methodological uncertainties embedded in these methods
which affect the reliability of results and their applicability for decision-making purposes, they
have limitations in aggregating across multiple scales and end-uses. Increased data …