Conformalized temporal convolutional quantile regression networks for wind power interval forecasting
文献类型：期刊论文
| 作者 | Hu, Jianming1; Luo, Qingxi1; Tang, Jingwei2; Heng, Jiani3; Deng, Yuwen1 |
| 刊名 | ENERGY
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 248页码:16 |
| 关键词 | Wind power interval prediction Temporal convolutional network Conformalized quantile regression |
| ISSN号 | 0360-5442 |
| DOI | 10.1016/j.energy.2022.123497 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000792627100007 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/61267]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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