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能源使用情况对于提高城市能源和气候适应能力以及实现节能减排目标至关重要，然而，了解整个美国城市层面的建筑能源使用情况及其对各种天气的响应。城市地区的气候条件仍然有限，在极端炎热和寒冷的时期，能源需求的突然激增给城市和地区电网带来了额外的负担，这种知识差距变得尤为重要。这种知识差距是由综合城市建筑造成的该项目的成果预计将为建筑改造、城市规划以及城市能源效率和脱碳政策提供宝贵的见解。 （NREL）不仅将为 PI 的研究奠定坚实的基础，还将增强俄克拉荷马大学的研究能力，促进跨学科合作，并支持俄克拉荷马州的能源弹性工作。该研究基础设施改进 Track-4 EPSCoR 研究人员项目支持开发综合城市建筑能源建模框架，旨在增强能源相关领域的能力。对各种地方和区域气象条件下的城市级建筑能源使用的预测性了解，目前对整个美国城市级建筑能源使用的了解，特别是对城市气候的反应，在很大程度上受到多种因素的阻碍。可靠的模型，有限的代表性为了克服这些障碍，PI 团队将与 NREL 密切合作，开发一个集成多尺度、长期城市气象预测和预测的建模框架。 PI 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。此次合作将利用 PI 在城市气象建模和 NREL 方面的专业知识。建立库存建模和验证方面的互补专业知识将通过对 NREL 的 PI 团队的亲自访问和培训来促进，该项目将提供对受气象数据和城市气候影响的预测准确性和不确定性的无与伦比的理解。该项目开发的框架将改进当前的建筑群建模方法，并能够对城市建筑能源使用进行更准确、更现实且计算效率更高的预测。此外，该项目中进行的跨尺度和跨分辨率数值实验也将做出贡献。下一代高分辨率建筑群模型的进步。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。