@article{Zhou2026, 
author = {Yan Zhou and Yonghui Sun and Sen Wang and Rabea Jamil Mahfoud and Dongchen Hou and Jianxi Wang},
title = {Very Short-term Probabilistic Prediction for Regional Wind Power Generation Based on OPNPIs},
year = {2026},
journal = {CSEE Journal of Power and Energy Systems},
volume = {12},
number = {2},
pages = {803-812},
keywords = {Detrending-based partial cross-correlation analysis, Huber-based approach, nonparametric prediction intervals, overall performance, regional wind power generation.},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2022.02790},
doi = {10.17775/CSEEJPES.2022.02790},
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.}
}