Ruan Guangchun, Wu Dongqi, Zheng Xiangtian, Zhong Haiwang, Kang Chongqing, Dahleh Munther A, Sivaranjani S, Xie Le
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Department of Electrical Engineering, the State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment, Tsinghua University, Beijing 100084, China.
Joule. 2020 Nov 18;4(11):2322-2337. doi: 10.1016/j.joule.2020.08.017. Epub 2020 Sep 21.
The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing US wholesale electricity markets with COVID-19 case, weather, mobile device location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we rigorously uncover a significant reduction in electricity consumption that is strongly correlated with the number of COVID-19 cases, degree of social distancing, and level of commercial activity.
2020年，新型冠状病毒肺炎（COVID-19）在全球迅速传播，自3月下旬以来，美国成为COVID-19病例的中心。随着美国开始逐步恢复经济活动，政策制定者和电力系统运营商必须采取科学方法来理解和预测对电力部门的影响。在此，我们发布了首个跨领域开放获取数据中心，整合了来自美国所有现有批发电市场的数据以及COVID-19病例、天气、移动设备位置和卫星成像数据。利用来自公共卫生和流动性数据的跨领域见解，我们严格揭示了电力消耗的显著下降，这与COVID-19病例数、社会距离程度和商业活动水平密切相关。
