Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs

Yan Zhou; Yonghui Sun; Sen Wang; Rabea Jamil Mahfoud; Dongchen Hou; Jianxi Wang

doi:10.17775/CSEEJPES.2022.02790

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Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs

Regular Papers | 更新时间：2026-04-16

- Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs
- Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs
- 中国电机工程学会电力与能源系统学报(英文) 2026年12卷第2期页码：803-812
- 作者机构：
  
  1. College of Energy and Electrical Engineering, Hohai University,Nanjing,China
  2. College of Water Conservancy and Hydropower Engineering, Hohai University,Nanjing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.02790
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Yan Zhou, Yonghui Sun, Sen Wang, 等. Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(2):803-812.

Yan Zhou, Yonghui Sun, Sen Wang, et al. Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 803-812.
Yan Zhou, Yonghui Sun, Sen Wang, 等. Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(2):803-812. DOI： 10.17775/CSEEJPES.2022.02790.

Yan Zhou, Yonghui Sun, Sen Wang, et al. Very Short-Term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 803-812. DOI： 10.17775/CSEEJPES.2022.02790.

摘要

Abstract

Due to the uncertainty and fluctuation of wind power generation

probabilistic prediction for regional wind power generation is critical to accurately quantify the uncertainty of meaningful information to the dispatching departments of power grid. This paper proposes an approach of very short-term probabilistic prediction for regional wind power generation based on optimal performance-based nonparametric prediction intervals (OPNPIs). First

the deterministic prediction for regional wind power generation considering the division of wind farms based on the detrending-based partial cross-correlation analysis (DPCCA) is studied. Based on the deterministic prediction and its prediction errors

the OPNPIs are proposed considering the reliability and overall performance for the uncertainty analysis. Furthermore

a regulating coefficient is studied to further enhance the performance of PIs. Effectiveness of the proposed method is verified through multistep PIs of 15-minute based on the real wind power generation data.

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