Black Swan Event Small-Sample Transfer Learning (BEST-L) and its Case Study on Electrical Power Prediction in COVID-19: Article No. 118458

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

Research output: Contribution to journalArticlepeer-review

11 Scopus Citations

Abstract

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.
Original languageAmerican English
Number of pages10
JournalApplied Energy
Volume309
DOIs
StatePublished - 2022

NREL Publication Number

  • NREL/JA-5D00-82003

Keywords

  • black swan event
  • COVID-19
  • load forecasting
  • small-sample learning
  • transfer learning

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