Black Swan Event Small-Sample Transfer Learning (BEST-L) and its Case Study on Electrical Power Prediction in COVID-19

Hu, Chenxi; Zhang, Jun; Yuan, Hongxia; Gao, Tianlu; Jiang, Huaiguang; Yan, Jing; Gao, David Wenzhong; Wang, Fei-Yue

doi:10.1016/j.apenergy.2021.118458

Black Swan Event Small-Sample Transfer Learning (BEST-L) and its Case Study on Electrical Power Prediction in COVID-19

Journal Article · 12 January 2022 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2021.118458· OSTI ID:1843373

Hu, Chenxi; Zhang, Jun; Yuan, Hongxia; Gao, Tianlu; Jiang, Huaiguang; Yan, Jing; Gao, David Wenzhong; Wang, Fei-Yue

The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems.

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 1843373

Report Number(s):: NREL/JA-5D00-82003; MainId:82776; UUID:45e6d037-f755-4286-a613-f9c10db6f16e; MainAdminID:63773

Journal Information:: Applied Energy, Journal Name: Applied Energy Vol. 309

Country of Publication:: United States

Language:: English

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COVID-19
POWER TRANSMISSION AND DISTRIBUTION
black swan event
load forecasting
small-sample learning
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Black Swan Event Small-Sample Transfer Learning (BEST-L) and its Case Study on Electrical Power Prediction in COVID-19

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