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

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。这个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一个更强大的科学基础，这将有助于能源管理者、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的能源需求。第一阶段的工作将开始，由专家和技术人员确定能源数据需求、数据开发机会、方法和优先事项，最终制定第二阶段实施计划，以创建全球能源数据共享区（GEDC）。计划中的GEDC将使研究人员、从业者和政策制定者能够获得具有更大数据可用性和互操作性的开放能源信息，从而实现更有效的决策。 核心项目团队代表了包括工程学、经济学、机器学习和能源政策在内的不同学科的融合，并将建立一个专家利益相关者工作组，以合作确定GEDC（和更广泛的科学界）的优先重点领域，这些领域可以迅速产生短期影响。GEDC平台旨在（1）为由用户需求和现实问题驱动的能源数据研究议程提供信息;（2）提高数据互操作性并建立密切协调的研究网络;（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学科。GEDC将向公众开放，并将允许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促进研究生和研究生的研究培训。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，工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相关者工作组，负责对开放数据源进行编目，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，包括自动提取大规模能源系统数据的机器学习方法。工作组将利用这一信息，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。GEDC的努力有可能建立一个创新的数据收集，管理和共享模型，可以复制到其他类型的数据中，并将公开所有结果和工具。在第一阶段和第二阶段填充GEDC所生成的数据将以关键方式为能源界增加价值，包括提高互操作性，扩大地理和专题覆盖范围，提高空间和时间保真度，以及集中传统上分离的信息。这些数据和相应的分析和可视化工具将加速社会科学、数据科学和工程领域众多学科的有意义的研究，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。

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.