U.S.-Ireland R&D Partnership: Intelligent Data Harvesting for Multi-Scale Building Stock Classification and Energy Performance Prediction

美国-爱尔兰 R

• 批准号: 2217410
• 负责人: Wangda Zuo
• 金额: $ 38.86万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217410&HistoricalAwards=false
• 关键词: U.S.; Ireland; R&D; Partnership; Intelligent

This joint trilateral project is in response to NSF Dear Colleague Letter: United States- Ireland-Northern Ireland R&D Partnership (NSF 20-064) in the areas of energy and sustainability. Residential buildings account for 14%-27% of greenhouse gas (GHG) emissions in the three jurisdictions and cause significant negative impact on the environment. Supported by NSF, the Science Foundation Ireland in the Republic of Ireland (RoI), and the Department for the Economy in Northern Ireland (NI), this joint research aims to reduce residential building energy consumption and related GHG emissions and environmental impacts across the three jurisdictions. The research will create decision support tools to inform policy makers, planners, and other stakeholders about the most beneficial residential retrofitting solutions at multiple scales (local to national). The methodology employed will lie at the confluence of various expertise, including green engineering of the NI team, building energy modeling and machine learning of the U.S. team, and information theory of the RoI team. The aim is to transform diverse public datasets in the three jurisdictions into actionable information. Empowered by this information, the anticipation is that better decisions can guide modern societies towards transformative green solutions for the built environment that leverage sustainable engineering systems and enable the creation of energy-efficient, healthy, and comfortable buildings for a nation's citizens. The approach is cognizant of society's need to provide ecological protection while maintaining favorable economic conditions.This joint research seeks to provide the foundational science needed to design, optimize, and deploy green engineering approaches that reduce residential building energy consumption and related GHG emissions. The interdisciplinary research targets to yield three results: 1) A methodology for data ingestion and an ontology and associated server that provides both a means of accessing and subsequently homogenizing data for both the data enrichment and the modeling processes. The intent is to enable previously unused data sources to be utilized as a whole to significantly improve the accuracy of modeling processes; 2) An advanced automated building energy model generation method powered by physics-informed machine learning, which can improve the efficiency of model generation, significantly reduce computing demand for large scale building energy prediction and protect building users' privacy. Algorithms will also be created to enable robust prediction with incomplete datasets; 3) A new complementary solution for predicting the GHG emissions reduction potential for stakeholders will be created to analyze near/zero GHG buildings in terms of energy performance. It is anticipated that these results will be beneficial both in terms of making buildings greener by reducing GHG emissions and energy consumption as well as decreasing operational costs. The plan is to seek the U.S. Department of Energy's Pacific Northwest National Laboratory to adopt the research results in their national building energy policy analysis for 139 million homes. The Northern Ireland Housing Executive will utilize this work to help predict decarbonization pathways for their housing stock of nearly 86,000 homes (10% of the housing stock in NI). The research will also assist the Sustainable Energy Authority of Ireland for its retrofit plan of 500,000 homes in the Republic of Ireland.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.

该三方联合项目是对NSF亲爱的同事信：美国-爱尔兰-北爱尔兰爱尔兰研发伙伴关系（NSF 20-064）在能源和可持续性领域的回应。住宅楼宇占三个司法管辖区温室气体排放量的14%至27%，对环境造成重大负面影响。在NSF、爱尔兰共和国爱尔兰科学基金会（RoI）和北方尔兰经济部（NI）的支持下，这项联合研究旨在减少三个司法管辖区的住宅建筑能耗和相关温室气体排放以及环境影响。该研究将创建决策支持工具，以告知政策制定者，规划者和其他利益相关者关于多个规模（从地方到国家）的最有益的住宅改造解决方案。所采用的方法将融合各种专业知识，包括NI团队的绿色工程，美国团队的建筑能源建模和机器学习，以及RoI团队的信息理论。其目的是将三个司法管辖区的各种公共数据集转化为可操作的信息。在这些信息的推动下，人们期望更好的决策能够引导现代社会为建筑环境提供变革性的绿色解决方案，这些解决方案利用可持续工程系统，并为一个国家的公民创造节能、健康和舒适的建筑。该方法认识到社会需要在保持良好经济条件的同时提供生态保护。该联合研究旨在提供设计、优化和部署绿色工程方法所需的基础科学，以减少住宅建筑能耗和相关GHG排放。跨学科研究的目标是产生三个结果：1）数据摄取的方法和本体和相关的服务器，为数据丰富和建模过程提供了访问和随后存储数据的方法。其目的是使以前未使用的数据源能够作为一个整体被利用，以显着提高建模过程的准确性; 2）一种由物理信息机器学习驱动的先进的自动化建筑能源模型生成方法，可以提高模型生成的效率，显着降低大规模建筑能源预测的计算需求，并保护建筑用户的隐私。还将创建算法，以实现对不完整数据集的稳健预测; 3）将创建一个新的补充解决方案，用于预测利益相关者的温室气体减排潜力，以分析近/零温室气体建筑的能源性能。预计这些结果将有利于通过减少温室气体排放和能源消耗以及降低运营成本使建筑物更加绿色。该计划将寻求美国能源部的太平洋西北国家实验室在其1.39亿家庭的国家建筑能源政策分析中采用研究结果。北方爱尔兰住房执行局将利用这项工作来帮助预测其近86，000套住房（占北爱尔兰住房存量的10%）的脱碳途径。该研究还将协助爱尔兰可持续能源管理局在爱尔兰共和国的500，000户家庭的改造计划。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Realistic Estimation of CO2 Emission Reductions Due to Building Retrofit

建筑改造带来的二氧化碳减排量的实际估算

• 发表时间: 2022
• 期刊: 2022 Building Performance Analysis Conference and SimBuild
• 作者: Lou, Yingli;Ye, Yungyang;Zuo, Wangda;Yang, Yizhi
• 通讯作者: Yang, Yizhi

The economic impacts of carbon emission trading scheme on building retrofits: A case study with U.S. medium office buildings

• DOI: 10.1016/j.buildenv.2022.109311
• 发表时间: 2022-06
• 期刊: Building and Environment
• 影响因子: 7.4
• 作者: Yingli Lou;Yizhi Yang;Y. Ye;Chuan He;W. Zuo

Carbon emission responsive building control: A case study with an all-electric residential community in a cold climate

• DOI: 10.1016/j.apenergy.2022.118910
• 发表时间: 2022-05
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: Jing Wang;Prateek Munankarmi;J. Maguire;Chengnan Shi;W. Zuo;D. Roberts;Xin Jin

Simulation-based assessment of ASHRAE Guideline 36, considering energy performance, indoor air quality, and control stability

• DOI: 10.1016/j.buildenv.2023.110371
• 发表时间: 2023-05
• 期刊: Building and Environment
• 影响因子: 7.4
• 作者: Cary A. Faulkner;Robert Lutes;Sen Huang;W. Zuo;D. Vrabie

Long-term carbon emission reduction potential of building retrofits with dynamically changing electricity emission factors

• DOI: 10.1016/j.buildenv.2021.108683
• 发表时间: 2022-01-02
• 期刊: BUILDING AND ENVIRONMENT
• 影响因子: 7.4
• 作者: Lou, Yingli;Ye, Yunyang;Zuo, Wangda
• 通讯作者: Zuo, Wangda