A Framework for Urban Building Energy Use Modeling

Reliable quantification of energy consumption by buildings plays a key role in development of sustainable cities. However, there are methodological uncertainties embedded in the most common urban scale energy use modeling methods and tools which affect the reliability of these tools and their applicability for decision-making purposes. This article presents a novel bottom- up data-driven framework for urban energy use modeling (UEUM) to help predict energy use more precisely through utilizing disaggregated data at building level, incorporating the actual urban spatial patterns, and testing different algorithms to propose an enhanced prediction model. This framework integrates the influential factors in the model including building characteristics; i.e., height, as an urban intensity metric, urban attributes; i.e., sprawl indices, that are captured in a multidimensional way representing compactness and connectivity of neighborhoods, and occupant characteristics. A case study on 800,000 buildings in seventy-seven neighborhoods in Chicago was used to test the framework. This framework has the potential to help better understand the existing urban energy use profiles and provides a more holistic image of urban energy use at multi-scales of building, block, neighborhood, and urban levels