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

RII Track-4：NSF：综合城市气象和建筑群建模框架，以增强城市级建筑能源使用预测

• 批准号: 2327435
• 负责人: Chenghao Wang
• 金额: $ 29.57万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327435&HistoricalAwards=false
• 关键词: RII; Track; NSF; Integrated; Urban

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.

准确预测城市层面的建筑用能，对于提高城市能源和气候韧性，实现节能减排目标至关重要。然而，对整个美国城市级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。这种知识差距在极端热浪和寒潮期间变得特别严重，因为能源需求的突然激增给城市和区域电网带来了额外的负担。该奖学金项目的目标是通过开发一个可推广和适用于所有美国城市的综合城市建筑能源建模框架来弥合这一知识差距。该项目的成果预计将为建筑改造、城市规划、城市能效和脱碳政策提供有价值的见解。PI的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荷马州大学的研究能力，促进跨学科合作，并支持俄克拉荷马州的能源弹性工作。此外，该项目将积极与部落国家合作，促进STEM教育的加强，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研究员项目支持开发一个综合的城市建筑能源建模框架，旨在增强对各种当地和区域气象条件下城市级建筑能源使用的预测性理解。目前对整个美国城市级建筑能源使用的理解，特别是其对城市气候的反应，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、基于台站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，将多尺度、长期的城市气象预测和基于物理的建筑存量模型集成在一起。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合作者在建筑存量建模和验证方面的互补专业知识，这将通过亲自访问和培训NREL的PI团队来促进。该项目将对气象数据和城市气候影响的预测准确性和不确定性提供无与伦比的了解。该项目开发的建模框架将改进当前的建筑存量建模方法，并实现更准确，更现实，但计算效率更高的大规模城市建筑能耗预测。此外，在该项目中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存量模型的发展。该奖项反映了NSF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。