@article{Xu2025, 
author = {Xinyi Xu and Shaojuan Ma and Cheng Huang},
title = {Uncertainty prediction of wind speed based on improved multi-strategy hybrid models},
year = {2025},
journal = {Electronic Research Archive},
volume = {33},
number = {1},
pages = {294-326},
keywords = {wind speed interval prediction, chaotic time series, nutcracker optimization algorithm, wavelet threshold, BiTCN-BiGRU},
url = {https://www.sciopen.com/article/10.3934/era.2025016},
doi = {10.3934/era.2025016},
abstract = {Accurate interval prediction of wind speed plays a vital role in ensuring the efficiency and stability of wind power generation. Due to insufficient traditional wind speed interval prediction methods for mining nonlinear features, in this paper, a novel interval prediction method was proposed by combining improved wavelet threshold and deep learning (BiTCN-BiGRU) with the nutcracker optimization algorithm (NOA). First, NOA was used to optimize the wavelet transform (WT) and BiTCN-BiGRU. Second, we applied NOA-WT to smooth the wind speed data. Then, to capture nonlinear features of time series, phase space reconstruction (PSR) was utilized to identify chaotic characteristics of the processed data. Finally, the NOA-BiTCN-BiGRU model was built to perform wind speed interval prediction. Under the same hyperparameters and network structure settings, a comparison with other deep learning methods showed that the prediction interval coverage probability (PICP) and prediction interval mean width (PIMW) of NOA-WT-BiTCN-BiGRU model achieves the best balance, with good prediction accuracy and generalization performance. This research can provide reference and guidance for nonlinear time-series interval prediction in the real world.}
}