Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion

Yan Zhou; Yonghui Sun; Sen Wang; Linquan Bai; Dongchen Hou; Rabea Jamil Mahfoud; Peng Wang

doi:10.17775/CSEEJPES.2020.05370

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Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion

Regular Papers | 更新时间：2026-10-15

- Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion
- Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion
- 中国电机工程学会电力与能源系统学报(英文) 2023年9卷第6期页码：2121-2129
- 作者机构：
  
  1. College of Energy and Electrical Engineering, Hohai University,Nanjing,China
  2. Department of Systems Engineering and Engineering Management, The University of North Carolina,NC,Charlotte,USA
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2020.05370
  中图分类号：
- 纸质出版：2023-11-01
- 稿件说明：
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Yan Zhou, Yonghui Sun, Sen Wang, 等. Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(6):2121-2129.

Yan Zhou, Yonghui Sun, Sen Wang, et al. Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion[J]. CSEE Journal of Power and Energy Systems, 2023, 9(6): 2121-2129.
Yan Zhou, Yonghui Sun, Sen Wang, 等. Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(6):2121-2129. DOI： 10.17775/CSEEJPES.2020.05370.

Yan Zhou, Yonghui Sun, Sen Wang, et al. Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion[J]. CSEE Journal of Power and Energy Systems, 2023, 9(6): 2121-2129. DOI： 10.17775/CSEEJPES.2020.05370.

摘要

Abstract

To enhance the performance of the prediction intervals (PIs)

a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage and selection operator (ALASSO) and integrated criterion (IC) is proposed. The ALASSO method is studied for shrinkage of output weights and selection of variables. Furthermore

for the better performance of PIs

composite weighted linear programming (CWLP) is proposed to modify the conventional linear programming cost function of quantile regression (QR)

by combining it with Bayesian information criterion (BIC) as an IC to optimize the coefficients of PIs. Then

the multiple fold cross model (MFCM) is utilized to improve the PIs performance. Multistep probabilistic prediction of 15-minute wind speed is performed based on the real wind farm data from the northeast of China. The effectiveness of the proposed approach is validated through the performances' comparisons with conventional methods.

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