Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19

doi:10.1016/j.apenergy.2021.118458

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19
	Hu, Chenxi 1; Zhang, Jun 1; Yuan, Hongxia 2; Gao, Tianlu 1; Jiang, Huaiguang 3; Yan, Jing 1; Gao, David Wenzhong 4; Wang, Fei-Yue5
发表期刊	APPLIED ENERGY
ISSN	0306-2619
	2022-03-01
卷号	309 页码:10
通讯作者	Zhang, Jun(jun.zhang.ee@whu.edu.cn)
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.
关键词	Transfer learning Black swan event Small-sample learning COVID-19 Load forecasting
DOI	10.1016/j.apenergy.2021.118458
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0101504] ; Science and technology project of SGCC(State Grid Corporation of China)
项目资助者	National Key R&D Program of China ; Science and technology project of SGCC(State Grid Corporation of China)
WOS研究方向	Energy & Fuels ; Engineering
WOS类目	Energy & Fuels ; Engineering, Chemical
WOS记录号	WOS:000819657800004
出版者	ELSEVIER SCI LTD
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49202
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Zhang, Jun
作者单位	1.Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China 2.Digital Grid Res Inst, China Southern Power Grid, Guangzhou 510063, Peoples R China 3.Natl Renewable Energy Lab, Golden, CO 80401 USA 4.Univ Denver, Dept Elect & Comp Engn, Denver, CO 80208 USA 5.Chinese Acad Sci, Inst Automat, Beijing 100864, 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.