Workshop: Forging Connections between Machine Learning, Data Science, and Power Systems Research. To be held at the National Science Foundation, March 5-6, 2020 in Alexandria, VA.

研讨会：建立机器学习、数据科学和电力系统研究之间的联系。

• 批准号: 2018828
• 负责人: Sairaj Dhople
• 金额: $ 10万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018828&HistoricalAwards=false
• 关键词: Workshop; Forging; Connections; between; Machine

Overview: This proposal seeks support from the National Science Foundation (NSF) to facilitate the organization of a workshop titled: "Forging Connections between Machine Learning, Data Science, and Power Systems Research." The workshop will examine theoretical and algorithmic advances from the areas of machine learning and data science that have the potential to address contemporary challenges in modeling, analysis, operations, and control of power systems. It will seek to do so by engaging the participation of leading researchers from power systems, machine learning, and data science as well as related enabling and synergistic application areas such as robotics and transportation networks. Intellectual Merit: Contemporary advances in the broad areas of data-driven computing and control hold tremendous potential in aiding the transition of the power system to a truly sustainable, resilient, and distributed infrastructure that can accommodate new generation, distribution, and end-use technology at scale. With this in mind, the proposed workshop will focus on teasing out foundational theories and methods from the areas of machine learning and data science that are relevant to research problems in power systems. Through a broad-based schedule that includes keynote talks, panel discussions, and breakout sessions, leading experts from the aforementioned areas will get a unique and unparalleled opportunity to exchange technical ideas that will result in new theories, methods, and algorithms tailored to realizing the next generation power system. Broader Impacts: Broader impacts of the proposed workshop will follow from a variety of avenues. First, it will serve as a unique opportunity for researchers from the areas of machine learning and data science to engage with leading experts in power systems to find common ground for engagement in research and development for the bulk power system: a critical infrastructure that underpins and governs societal progress. The workshop will also provide a unique stage to showcase and share outcomes from cutting-edge NSF funded research. Researchers will have an unparalleled opportunity to build collaborative scholarly and institutional partnerships that transcend boundaries imposed by their respective technical areas. Finally, participation of researchers from minorities and underrepresented groups, as well as early-career researchers will be encouraged.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.

概述：该提案寻求美国国家科学基金会（NSF）的支持，以促进组织一个名为“锻造机器学习，数据科学和电力系统研究之间的联系”的研讨会。研讨会将研究机器学习和数据科学领域的理论和算法进展，这些进展有可能解决电力系统建模、分析、运营和控制方面的当代挑战。它将寻求通过吸引来自电力系统，机器学习和数据科学以及相关的使能和协同应用领域（如机器人和交通网络）的领先研究人员的参与来实现这一目标。 智力优势：数据驱动计算和控制的广泛领域的当代进步在帮助电力系统过渡到真正可持续的，弹性的和分布式的基础设施方面具有巨大的潜力，可以大规模地适应新的发电，配电和最终使用技术。考虑到这一点，拟议的研讨会将侧重于梳理与电力系统研究问题相关的机器学习和数据科学领域的基础理论和方法。通过包括主题演讲、小组讨论和分组讨论在内的广泛日程，来自上述领域的领先专家将获得一个独特而无与伦比的机会，交流技术思想，从而产生为实现下一代电力系统而量身定制的新理论、方法和算法。 更广泛的影响：拟议的研讨会将通过各种途径产生更广泛的影响。首先，它将为机器学习和数据科学领域的研究人员提供一个独特的机会，与电力系统领域的领先专家合作，为参与大容量电力系统的研究和开发找到共同点：这是一个支撑和管理社会进步的关键基础设施。研讨会还将提供一个独特的舞台，展示和分享尖端NSF资助的研究成果。研究人员将有无与伦比的机会建立超越各自技术领域界限的学术和机构合作伙伴关系。最后，来自少数民族和代表性不足的群体的研究人员以及早期职业研究人员的参与将受到鼓励。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。