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

准确预测城市建筑能耗对提高城市能源和气候适应能力、实现节能减排目标具有重要意义。然而，对整个美国城市建筑能源使用及其对城市地区各种天气和气候条件的反应的了解仍然有限。在极端高温和寒潮期间，当能源需求的突然激增给城市和区域电网带来额外负担时，这种知识差距变得尤为严重。该奖学金项目的目标是通过开发一个综合的城市建筑能源建模框架来弥合这一知识差距，该框架可推广并适用于所有美国城市。预计该项目的成果将为建筑改造、城市规划、城市能源效率和脱碳政策提供有价值的见解。PI团队和国家可再生能源实验室（NREL）之间的合作努力不仅将为PI的研究奠定坚实的基础，而且还将提高俄克拉何马大学的研究能力，促进跨学科合作，并支持俄克拉何马州的能源弹性努力。此外，该项目将积极与部落国家合作，促进STEM教育的发展，并促进学生参与能源相关领域。该研究基础设施改善轨道4 EPSCoR研究人员项目支持开发综合城市建筑能源建模框架，旨在提高对各种地方和区域气象条件下城市建筑能源使用的预测性理解。目前对整个美国城市级别建筑能源使用的理解，特别是对城市气候的反应，在很大程度上受到几个因素的阻碍。这些问题包括缺乏可靠的模型，基于站点的天气观测中城市气候的代表性有限，以及不同模型之间的空间尺度不匹配。为了克服这些障碍，PI的团队将与NREL密切合作，开发一个建模框架，该框架集成了多尺度、长期城市气象预测和基于物理的建筑库存模型。PI的团队还将严格评估该建模框架的准确性，并了解与气象数据输入相关的预测误差。该合作利用了PI在城市气象建模方面的专业知识和NREL合作者在建筑库存建模和验证方面的互补专业知识，将通过亲自访问和对PI在NREL的团队进行培训来促进这一合作。该项目将对气象数据和城市气候影响下的预测精度和不确定性提供无与伦比的理解。本项目开发的建模框架将改进当前的建筑存量建模方法，使城市建筑能源使用的大规模预测更加准确、真实、且计算效率更高。此外，本项目进行的跨尺度和跨分辨率数值实验将有助于下一代高分辨率建筑模型的发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.