Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19
文献类型：期刊论文
| 作者 | Hu, Chenxi5; Zhang, Jun5; Yuan, Hongxia4; Gao, Tianlu5; Jiang, Huaiguang3; Yan, Jing5; Gao, David Wenzhong2; Wang, Fei-Yue1
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| 刊名 | APPLIED ENERGY
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 309页码:10 |
| 关键词 | Transfer learning Black swan event Small-sample learning COVID-19 Load forecasting |
| ISSN号 | 0306-2619 |
| DOI | 10.1016/j.apenergy.2021.118458 |
| 通讯作者 | Zhang, Jun(jun.zhang.ee@whu.edu.cn) |
| 英文摘要 | 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. |
| 资助项目 | National Key R&D Program of China[2018AAA0101504] ; Science and technology project of SGCC(State Grid Corporation of China) |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000819657800004 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Key R&D Program of China ; Science and technology project of SGCC(State Grid Corporation of China) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49201]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Zhang, Jun |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China 2.Univ Denver, Dept Elect & Comp Engn, Denver, CO 80208 USA 3.Natl Renewable Energy Lab, Golden, CO 80401 USA 4.Digital Grid Res Inst, China Southern Power Grid, Guangzhou 510063, Peoples R China 5.Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Chenxi,Zhang, Jun,Yuan, Hongxia,et al. Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19[J]. APPLIED ENERGY,2022,309:10. |
| APA | Hu, Chenxi.,Zhang, Jun.,Yuan, Hongxia.,Gao, Tianlu.,Jiang, Huaiguang.,...&Wang, Fei-Yue.(2022).Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19.APPLIED ENERGY,309,10. |
| MLA | Hu, Chenxi,et al."Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19".APPLIED ENERGY 309(2022):10. |
入库方式： OAI收割
来源：自动化研究所
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