Convergence Accelerator Phase I (RAISE): Open Knowledge Network for the Global Energy Data Commons

融合加速器第一阶段（RAISE）：全球能源数据共享的开放知识网络

• 批准号: 1937137
• 负责人: Kyle Bradbury
• 金额: $ 97.41万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937137&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Open

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to create a more robust scientific foundation for energy systems management and planning that will help energy managers, policy makers, and the communities they serve meet national and global energy needs in ways that are reliable, affordable, accessible and sustainable. The phase I effort will begin with expert and stakeholder-driven identification of energy data needs, data development opportunities, methods, and priorities that culminates in a Phase 2 implementation plan for creating a Global Energy Data Commons (GEDC). The planned GEDC will enable researchers, practitioners, and policymakers to access open energy information with much greater data availability and interoperability, allowing significantly more effective decision-making. The core project team represents a convergence of diverse disciplines including engineering, economics, machine learning, and energy policy and will establish an expert stakeholder working group to collaborate on identifying priority focus areas for the GEDC (and the wider scientific community) that can be rapidly catalyzed for short-term impact. The GEDC platform is intended to (1) inform an energy data research agenda that is driven by user-demand and real-world problems; (2) improve data interoperability and establish a closely coordinated research network; and (3) make energy data more usable to diverse disciplines through curated, centralized databases, online tools, and visualizations. The GEDC will be open to the public and will enable access and exploration of data in multiple formats (tabular, geospatial, etc.). Finally, this project will also contribute to graduate and post-graduate research training through student and postdoctoral engagement. The energy system presents significant challenges in data availability and interoperability that limit the ability of stakeholders in academia, non-profits, industry, and government to plan effectively. This project will establish a working group of stakeholders who will catalog open data sources, identify effective modes of enabling data interoperability, and evaluate feasible methods of data collection including machine learning approaches for automating the extraction of large-scale energy systems data. Using this information the working group will identify priority areas for focused data collection efforts and plan for execution under Phase II. The GEDC effort has the potential to establish a model for innovative data collection, curation, and sharing that could be replicated in other types of data, and will make all resulting findings and tools publicly available. Data generated to populate the GEDC across both phases I and II will add value to the energy community in key ways including increased interoperability, expanded geographic and thematic coverage, higher spatial and temporal fidelity, and centralization of information that has traditionally been separate. These data and accompanying analysis and visualization tools will accelerate meaningful inquiry across numerous disciplines in the social sciences, data sciences, and engineering, and will also facilitate planning and decision making by practitioners and managers.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.

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛的影响和潜在的社会效益是为能源系统管理和规划创造更强大的科学基础，帮助能源管理者、政策制定者和他们所服务的社区以可靠、负担得起、可获得和可持续的方式满足国家和全球能源需求。第一阶段的工作将从专家和利益相关者驱动的能源数据需求、数据开发机会、方法和优先事项的确定开始，最终形成创建全球能源数据共享(GEDC)的第二阶段实施计划。计划中的GEDC将使研究人员、从业者和政策制定者能够以更高的数据可用性和互操作性获取开放的能源信息，从而使决策更加有效。核心项目团队代表着包括工程学、经济学、机器学习和能源政策在内的不同学科的汇聚，并将建立一个专家利益攸关方工作组，以协作确定GEDC(和更广泛的科学界)可以迅速催化产生短期影响的优先重点领域。GEDC平台的目的是(1)提供由用户需求和现实世界问题驱动的能源数据研究议程；(2)提高数据互操作性，建立一个密切协调的研究网络；以及(3)通过精心策划的集中式数据库、在线工具和可视化，使能源数据更适用于不同的学科。地理空间数据中心将向公众开放，使人们能够访问和探索多种格式的数据(表格、地理空间等)。最后，该项目还将通过学生和博士后的参与为研究生和研究生的研究培训做出贡献。能源系统在数据可用性和互操作性方面提出了重大挑战，限制了学术界、非营利组织、行业和政府的利益相关者有效规划的能力。该项目将建立一个利益攸关方工作组，该工作组将编目开放的数据源，确定实现数据互操作性的有效模式，并评估可行的数据收集方法，包括用于自动提取大规模能源系统数据的机器学习方法。利用这些信息，工作组将确定重点数据收集工作的优先领域，并计划在第二阶段执行工作。全球数据中心的工作有可能建立一种创新的数据收集、整理和共享模式，可在其他类型的数据中复制，并将公开所有由此产生的结果和工具。为在第一阶段和第二阶段向GEDC填充而产生的数据将在关键方面为能源界增加价值，包括增强互操作性、扩大地理和专题覆盖范围、提高空间和时间保真度以及集中传统上分散的信息。这些数据和伴随的分析和可视化工具将加快社会科学、数据科学和工程学中许多学科的有意义的探索，也将促进从业者和管理者的规划和决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Estimating residential building energy consumption using overhead imagery

• DOI: 10.1016/j.apenergy.2020.116018
• 发表时间: 2020-12
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: Artem Streltsov;Jordan M. Malof;Bohao Huang;Kyle Bradbury

Mapping Electric Transmission Line Infrastructure from Aerial Imagery with Deep Learning

利用深度学习从航空图像绘制输电线路基础设施图

• DOI: 10.1109/igarss39084.2020.9323851
• 发表时间: 2020
• 期刊: IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium
• 作者: Hu, Wei;Alexander, Ben;Cathcart, Wendell;Hu, Atsushi;Nair, Varun;Zuo, Lin;Malof, Jordan;Collins, Leslie;Bradbury, Kyle
• 通讯作者: Bradbury, Kyle