A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on
the U.S. Electricity Sector
- URL: http://arxiv.org/abs/2005.06631v7
- Date: Fri, 28 Aug 2020 02:53:08 GMT
- Title: A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on
the U.S. Electricity Sector
- Authors: Guangchun Ruan, Dongqi Wu, Xiangtian Zheng, Haiwang Zhong, Chongqing
Kang, Munther A. Dahleh, S. Sivaranjani, Le Xie
- Abstract summary: coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter.
We release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing U.S. wholesale electricity markets with COVID-19 case, weather, cellular location, and satellite imaging data.
We uncover a significant reduction in electricity consumption across that is strongly correlated with the rise in the number of COVID-19 cases, degree of social distancing, and level of commercial activity.
- Score: 1.2972684859455053
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The novel coronavirus disease (COVID-19) has rapidly spread around the globe
in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late
March. As the U.S. 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 U.S. wholesale electricity markets
with COVID-19 case, weather, cellular location, and satellite imaging data.
Leveraging cross-domain insights from public health and mobility data, we
uncover a significant reduction in electricity consumption across that is
strongly correlated with the rise in the number of COVID-19 cases, degree of
social distancing, and level of commercial activity.
Related papers
- Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19)
Pandemic: A Survey on the State-of-the-Arts [10.741018907229927]
The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019.
The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives.
Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak.
arXiv Detail & Related papers (2021-07-17T13:12:30Z) - Rapid COVID-19 Risk Screening by Eye-region Manifestations [64.6260390977642]
There are more and more ocular manifestations that have been reported in the COVID-19 patients as growing clinical evidence.
We propose a new fast screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras.
Our model for COVID-19 rapid prescreening have the merits of the lower cost, fully self-performed, non-invasive, importantly real-time, and thus enables the continuous health surveillance.
arXiv Detail & Related papers (2021-06-12T01:56:10Z) - COVID-19 Digital Contact Tracing Applications and Techniques: A Review
Post Initial Deployments [2.05040847923906]
coronavirus disease 2019 (COVID-19) is a severe global pandemic that has claimed millions of lives and continues to overwhelm public health systems.
To increase the effectiveness of contact tracing, countries across the globe are leveraging advancements in mobile technology and Internet of Things.
arXiv Detail & Related papers (2021-02-25T10:18:40Z) - Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A
Cross-Country Comparative Study [0.0]
This study analyzes the impact of the COVID-19 pandemic on the subjective well-being as measured through Twitter data indicators for Japan and Italy.
Overall, the subjective well-being dropped by 11.7% for Italy and 8.3% for Japan in the first nine months of 2020 compared to the last two months of 2019.
arXiv Detail & Related papers (2021-01-19T15:51:53Z) - Country-wide mobility changes observed using mobile phone data during
COVID-19 pandemic [5.402663611963239]
In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic.
Here we assess the effect of the lock-down quantitatively for all regions in Austria using near-real-time anonymized mobile phone data.
arXiv Detail & Related papers (2020-08-23T16:00:57Z) - Understanding the temporal evolution of COVID-19 research through
machine learning and natural language processing [66.63200823918429]
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world.
We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research.
Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
arXiv Detail & Related papers (2020-07-22T18:02:39Z) - A Survey on Applications of Artificial Intelligence in Fighting Against
COVID-19 [75.84689958489724]
The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.
As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic.
This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19.
arXiv Detail & Related papers (2020-07-04T22:48:15Z) - Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US.
We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
arXiv Detail & Related papers (2020-06-23T03:36:19Z) - Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries.
Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
arXiv Detail & Related papers (2020-06-05T02:04:25Z) - A County-level Dataset for Informing the United States' Response to
COVID-19 [5.682299443164938]
We present a dataset that aggregates relevant data from governmental, journalistic, and academic sources on the U.S. county level.
Our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit, scores, and healthcare system-related metrics.
arXiv Detail & Related papers (2020-04-01T05:07:27Z) - Mapping the Landscape of Artificial Intelligence Applications against
COVID-19 [59.30734371401316]
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization.
We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
arXiv Detail & Related papers (2020-03-25T12:30:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.