Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Shiqi Ou, Xin He (), Weiqi Ji, Wei Chen, Lang Sui, Yu Gan, Zifeng Lu, Zhenhong Lin, Sili Deng, Steven Przesmitzki and Jessey Bouchard
Additional contact information
Shiqi Ou: Energy and Transportation Science Division, Oak Ridge National Laboratory
Xin He: Aramco Services Company: Aramco Research Center—Detroit
Weiqi Ji: Massachusetts Institute of Technology
Wei Chen: Michigan Department of Transportation
Lang Sui: Aramco Services Company: Aramco Research Center—Detroit
Yu Gan: Energy Systems Division, Argonne National Laboratory
Zifeng Lu: Energy Systems Division, Argonne National Laboratory
Zhenhong Lin: Energy and Transportation Science Division, Oak Ridge National Laboratory
Sili Deng: Massachusetts Institute of Technology
Steven Przesmitzki: Aramco Services Company: Aramco Research Center—Detroit
Jessey Bouchard: Aramco Services Company: Aramco Research Center—Detroit

Nature Energy, 2020, vol. 5, issue 9, 666-673

Abstract: Abstract Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people’s resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020.

Date: 2020
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DOI: 10.1038/s41560-020-0662-1

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