Applied Sciences

27 pages, 1660 KB

Open AccessArticle

Trustworthy Wind Power Forecasting Based on Inverted Transformer with Variable-Wise Interaction and Evidential Learning

by Yiming Lou, Zhuoyu Hu, Guona Chen and Shujin Wu

Appl. Sci. 2026, 16(10), 4827; https://doi.org/10.3390/app16104827 (registering DOI) - 12 May 2026

The inherent nonlinearity and uncertainty of wind power generation pose significant challenges to the security, stability, and economic operation of power grids. Therefore, accurate and reliable wind power forecasting is crucial for seamless grid integration and effective risk assessment. Existing forecasting models often [...] Read more.

The inherent nonlinearity and uncertainty of wind power generation pose significant challenges to the security, stability, and economic operation of power grids. Therefore, accurate and reliable wind power forecasting is crucial for seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving point-prediction accuracy while overlooking effective multivariate dependency modeling and reliable uncertainty quantification, limiting both the informativeness and reliability of their forecasts. This study proposes a Fractional-order Momentum optimized Evidential iTransformer (FoM-EiT) for short-term wind power forecasting from multivariate time series. The proposed model integrates cyclic feature encoding for periodic variables, an inverted Transformer for variable-wise interaction learning, and an evidential output head that jointly produces point forecasts and uncertainty estimates from a shared representation. The proposed fractional-order momentum (FoM) optimization accumulates gradient history over an extended window, thereby smoothing oscillations caused by gradient competition and stabilizing the joint training process. Experiments on four real-world wind farms from different geographical regions show that FoM-EiT achieves competitive point forecasting performance, with

R^{2}

values of 0.6342, 0.8211, 0.7844, and 0.9161, and the Wilcoxon signed-rank test indicates that its advantages over the baselines are statistically significant in the vast majority of comparisons. For uncertainty quantification, FoM-EiT achieves Prediction Interval Coverage Probability (PICP) values of 0.9492, 0.9682, 0.9709, and 0.9498, while the Winkler score results further show that its prediction intervals outperform the conformal prediction and quantile regression baselines in terms of overall interval quality. These results indicate that FoM-EiT provides both accurate forecasts and trustworthy uncertainty information, making it a practical tool for dispatch, reserve allocation, and risk-aware short-term power system operation. Full article

(This article belongs to the Special Issue Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition)

23 pages, 2172 KB

Open AccessArticle

Dynamic Risk Assessment of Roof Fall and Rib Spalling Accidents in Non-Coal Mines: Based on FFTA-DBN and the 24Model

by Yumeng Xiao, Yanling Wu, Ruili Hu and Minbo Zhang

Appl. Sci. 2026, 16(10), 4826; https://doi.org/10.3390/app16104826 (registering DOI) - 12 May 2026

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