Conformalized temporal convolutional quantile regression networks for wind power interval forecasting

doi:10.1016/j.energy.2022.123497

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	Conformalized temporal convolutional quantile regression networks for wind power interval forecasting
	Hu, Jianming 1; Luo, Qingxi 1; Tang, Jingwei 2; Heng, Jiani 3; Deng, Yuwen 1
	2022-06-01
发表期刊	ENERGY
ISSN	0360-5442
卷号	248 页码:16
摘要	Wind power interval prediction is an effective technique for quantifying forecasting uncertainty caused by the intermittent and fluctuant characteristics of wind energy. Valid coverage and short interval length are the two most critical targets in interval prediction to attain reliable and accurate information, providing effective support for decision-makers to better control the risks in the power planning. This paper proposes a novel interval prediction approach named conformalized temporal convolutional quantile regression networks (CTCQRN) which combines the conformalized quantile regression (CQR) algorithm with a temporal convolutional network (TCN), without making any distributional assumptions. The proposed model inherits the advantages of quantile regression and conformal prediction that is fully adaptive to heteroscedasticity implicated in data, and meets the theoretical guarantee of valid coverage. As opposed to conventional RNN-based approaches, the adopted TCN architecture frees from suffering iterative propagation and gradient vanishing/explosion, and can handle very long sequences in a parallel manner. Case studies on two different geographical wind power datasets show that the proposed model has a distinct edge over benchmark models in goals of valid coverage and narrow interval bandwidth, which can help to ensure the economic and secure operation of the electric power system.(c) 2022 Elsevier Ltd. All rights reserved.
关键词	Wind power interval prediction Temporal convolutional network Conformalized quantile regression
DOI	10.1016/j.energy.2022.123497
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[72071053] ; National Natural Science Foundation of China[71701053] ; Natural Science Foundation of Guangdong Province[2020A151501527]
WOS研究方向	Thermodynamics ; Energy & Fuels
WOS类目	Thermodynamics ; Energy & Fuels
WOS记录号	WOS:000792627100007
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/61268
专题	中国科学院数学与系统科学研究院
通讯作者	Luo, Qingxi
作者单位	1.Guangzhou Univ, Coll Econ & Stat, Guangzhou, Peoples R China 2.Univ Macau, Fac Sci & Technol, Dept Math, Macau, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Hu, Jianming,Luo, Qingxi,Tang, Jingwei,et al. Conformalized temporal convolutional quantile regression networks for wind power interval forecasting[J]. ENERGY,2022,248:16.
APA	Hu, Jianming,Luo, Qingxi,Tang, Jingwei,Heng, Jiani,&Deng, Yuwen.(2022).Conformalized temporal convolutional quantile regression networks for wind power interval forecasting.ENERGY,248,16.
MLA	Hu, Jianming,et al."Conformalized temporal convolutional quantile regression networks for wind power interval forecasting".ENERGY 248(2022):16.