RII Track-4:NSF: An Integrated Urban Meteorological and Building Stock Modeling Framework to Enhance City-level Building Energy Use Predictions

Accurately predicting building energy use at the city level plays a crucial role in improving urban energy and climate resilience and achieving energy saving and emission reduction goals. However, the understanding of city-level building energy use throughout the entire U.S. and its response to various weather and climate conditions in urban areas remains limited.

This knowledge gap becomes particularly critical during periods of extreme heat and cold waves when the sudden surge in energy demand places an extra burden on city and regional electric grids. The objective of this fellowship project is to bridge this knowledge gap by developing an integrated urban building energy modeling framework that is generalizable and applicable to all U.S. cities.

The project’s outcomes are anticipated to offer valuable insights for building retrofits, urban planning, and urban energy efficiency and decarbonization policies. The collaboration effort between the PI’s team and the National Renewable Energy Laboratory (NREL) will not only lay a robust foundation for the PI’s research, but will also enhance the research capacity of the University of Oklahoma, foster cross-disciplinary collaborations, and support Oklahoma’s energy resilience efforts.

Furthermore, this project will actively engage with Tribal Nations, contribute to STEM education enhancement, and promote student involvement in energy-related fields.

This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows project supports the development of an integrated urban building energy modeling framework that aims to enhance the predictive understanding of city-level building energy use under various local and regional meteorological conditions. Current understanding of city-level building energy use across the entire U.S., particularly its response to urban climates, has been largely hindered by several factors.

These include the lack of reliable models, limited representation of urban climates in station-based weather observations, and spatial scale mismatches between different models. To overcome these obstacles, the PI’s team will collaborate closely with the NREL and develop a modeling framework that integrates multi-scale, long-term urban meteorological predictions and physics-based building stock models.

The PI’s team will also rigorously assess the accuracy of this modeling framework and understand the prediction errors associated with meteorological data inputs. The collaboration leverages the PI’s expertise in urban meteorological modeling and NREL collaborators’ complementary expertise in building stock modeling and validation, which will be facilitated through in-person visits and training for the PI's team at NREL.

This project will provide an unmatched understanding of the prediction accuracy and uncertainties influenced by meteorological data and urban climates. The modeling framework developed in this project will improve current building stock modeling approaches and enable more accurate, realistic, yet computationally efficient predictions of urban building energy use at scale.

In addition, the cross-scale and cross-resolution numerical experiments conducted in this project will contribute to the advancement of next-generation high-resolution building stock models.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.