A Cross-Domain Data-driven Approach to Analyzing and Predicting the Impact of COVID-19 on the U.S. Electricity Sector

跨域数据驱动方法分析和预测 COVID-19 对美国电力行业的影响

• 批准号: 2035688
• 负责人: Le Xie
• 金额: $ 33.5万
• 依托单位: Texas A&M Engineering Experiment Station
• 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: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035688&HistoricalAwards=false
• 关键词: Cross; Domain; Data; driven; Approach

This project aims at developing a cross-domain, data-driven approach to tracking and measuring the impact of the ongoing COVID-19 pandemic on the U.S. electricity sector. The COVID-19 crisis has gone beyond anybody’s wildest imagination and is turning out to be a once-in-a-century societal challenge. As the lifeblood of civil society and a key enabling infrastructure system, the electricity sector is quickly adjusting to the new normal, and it is crucial to understand the severity and the resiliency of the grid in response to disruption caused by COVID-19. This project substantiates a data-driven, science-based approach to evaluating the impact of various policy options on the operation of the electric energy infrastructure. Once successfully pursued, the project will provide much needed planning decision support for the electricity sector. The research program will be tightly coupled with an educational effort to train future leaders in the electricity and public health sectors. The research team has engaged female and African American students in building the preliminary version of this data hub and to continue research on the project. The team is also working with the industry members to provide training materials to a broad set of industry affiliatesThe goal of this project is to develop a first-of-its-kind cross-domain data hub and data-driven analysis of the COVID’s impact on the U.S. electricity sector. The approach is to 1) build a comprehensive open-access data hub with quality monitoring and daily updates, 2) quantify the sensitivity of electricity consumption with respect to social distancing and public health policies by using Ensemble Backcast Models and Restricted Vector Autoregression (VAR), and 3) construct a predictive model for the electricity sector considering social distancing policies and mobility in different sectors. The contribution of this project is four-fold. First, this is a first-of-its-kind data hub that combines otherwise unrelated domains of data like electricity markets, public health, and mobility data into a coherent infrastructure. A machine learning-based cleaning and pre-processing technique is proposed. Second, a statistical approach is proposed to quantify the unique impact of a public health crisis on the electricity sector. This entails building and analyzing novel statistical models that encompass societal mobility and public health data into the regression analysis of electricity consumption in major hot spots in the U.S. Third, a novel concept of elasticity of power consumption with respect to societal mobility is proposed and substantiated as an effective indicator of the power consumption as a function of social distancing policy measures. Last but not least, this project will combine all the above three innovations to create a first-of-its-kind predictive model of electricity sector as a function of social distancing policies and public health data. Drawing upon expertise from biostatistics and electric power engineering, this project will contribute to the cross-fertilization between the public health and electric energy sectors.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.

该项目旨在开发一种跨领域的数据驱动方法，以跟踪和衡量持续的COVID-19疫情对美国电力行业的影响。COVID-19危机已经超出了任何人最疯狂的想象，并正在成为百年一遇的社会挑战。作为公民社会的命脉和关键的基础设施系统，电力行业正在迅速适应新常态，了解电网在应对COVID-19造成的中断时的严重性和弹性至关重要。该项目证实了一种数据驱动的、以科学为基础的方法，用于评估各种政策选择对电力基础设施运营的影响。一旦成功实施，该项目将为电力部门提供急需的规划决策支持。该研究计划将与教育工作紧密结合，以培养电力和公共卫生部门的未来领导者。研究小组已让女性和非裔美国学生参与建立这一数据中心的初步版本，并继续对该项目进行研究。该团队还与行业成员合作，为广泛的行业分支机构提供培训材料。该项目的目标是开发首个跨域数据中心，并对COVID对美国电力行业的影响进行数据驱动分析。该方法是：1）建立一个全面的开放访问数据中心，并进行质量监控和每日更新; 2）通过使用环境回溯模型和限制向量自回归（VAR）来量化电力消耗对社交距离和公共卫生政策的敏感性;以及3）考虑不同行业的社交距离政策和流动性，构建电力行业的预测模型。该项目的贡献有四个方面。首先，这是一个首创的数据中心，它将电力市场、公共卫生和移动数据等不相关的数据领域整合到一个连贯的基础设施中。提出了一种基于机器学习的清洁和预处理技术。其次，提出了一种统计方法来量化公共卫生危机对电力部门的独特影响。这需要建立和分析新的统计模型，包括社会流动性和公共卫生数据到电力消费的回归分析在美国的主要热点第三，一个新的概念，电力消费的弹性相对于社会流动性提出并证实作为一个有效的指标的电力消费作为一个功能的社会距离政策措施。最后但并非最不重要的是，该项目将联合收割机结合上述三项创新，创建一个电力部门的首个预测模型，作为社交距离政策和公共卫生数据的函数。该项目利用生物统计学和电力工程的专业知识，将有助于公共卫生和电力能源部门之间的交叉施肥。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。

项目成果

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

迈向碳中和电力和交通：电网基础设施准备好了吗？

• DOI: 10.1016/j.joule.2021.06.011
• 发表时间: 2021
• 期刊: Joule
• 影响因子: 39.8
• 作者: Xie, Le;Singh, Chanan;Mitter, Sanjoy K.;Dahleh, Munther A.;Oren, Shmuel S.
• 通讯作者: Oren, Shmuel S.

Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods

微观和宏观层面的极端事件、能源安全和平等：概念、挑战和方法

• DOI: 10.1016/j.erss.2021.102401
• 发表时间: 2022
• 期刊: Energy Research & Social Science
• 影响因子: 6.7
• 作者: Chen, Chien-fei;Dietz, Thomas;Fefferman, Nina H.;Greig, Jamie;Cetin, Kristen;Robinson, Caitlin;Arpan, Laura;Schweiker, Marcel;Dong, Bing;Wu, Wenbo
• 通讯作者: Wu, Wenbo