Abstract：In the field of wind farm power prediction, the non-stationarity of power sequences and the coupling effect of meteorological factors severely restrict the prediction accuracy. However, traditional prediction models (such as single LSTM or Informer) are difficult to effectively decouple the fluctuation mode features, and existing feature selection methods often ignore the nonlinear interaction between variables. Although some studies have introduced signal decomposition techniques in recent years, most of them have problems such as mode aliasing and unclear physical meaning of component reconstruction. At the same time, the redundant information in the feature selection stage significantly increases the computational burden. To address these challenges, an xLSTM-Informer prediction framework that integrates feature selection and signal decomposition for collaborative optimization is proposed. By combining the time-varying characteristics of meteorological factors and multi-scale power sequence features, it achieves high-precision wind power prediction. Firstly, based on the historical power data of the wind farm and multi-dimensional meteorological monitoring data, the training sample set is constructed on a quarterly basis. Secondly, a two-stage feature processing procedure is executed: in the first stage, the Spearman-MIC hybrid feature selection strategy is adopted to simultaneously capture the linear and nonlinear correlation features of meteorological factors and eliminate redundant variables; in the second stage, the “SGMD-rePE-VMD” signal decomposition chain is established, where the symplectic geometry mode decomposition (SGMD) is used to decouple the original power sequence, the components are adaptively reorganized based on the robust permutation entropy (rePE) criterion, and the high-frequency components are further optimized by variational mode decomposition (VMD). Finally, the xLSTM-Informer hybrid model is designed: the xLSTM module captures long-term dependency features through an exponential gating mechanism, the Informer module processes global sequence correlations with ProbSparse attention, and the gated fusion network integrates cross-scale information. The model generates the final power value through the prediction of decomposed sub-sequences and result reconstruction. The validation results of actual wind farm data show that when the prediction step is 15 minutes, the average root mean square error of the proposed model in four seasons is 0.725 6 MW, and the coefficient of determination reaches 0.991 6. The key feature ablation experiments show that when Spearman-MIC feature selection is not used, the average root mean square error rises to 1.807 1 MW; when VMD secondary decomposition is not implemented, the error climbs to 1.643 2 MW. In the model comparison test, xLSTM-Informer has a significant advantage over the single LSTM (average RMSE of 2.233 3 MW) and Informer (average RMSE of 1.870 9 MW). Comprehensive experimental analysis leads to the following conclusions: (1) The proposed Spearman-MIC hybrid screening mechanism can effectively identify key meteorological factors (such as the MIC of wind speed at 100 meters height in all four seasons being greater than 0.41), reducing the prediction error by 59.86%. (2) The “SGMD-rePE-VMD” decomposition chain, by suppressing the modal aliasing phenomenon, improves the accuracy by 60.75% compared to single-stage decomposition models. (3) This framework only requires historical power and meteorological monitoring data, making it more suitable for actual wind farm deployment than physical models that rely on numerical weather forecasts. Meanwhile, its modeling method of fusing multi-source data into a three-dimensional feature tensor significantly enhances the expression ability of spatiotemporal features.
张振龙, 聂达文, 张新生, 任金哥. 融合特征筛选与信号分解协同优化的xLSTM-Informer风电功率预测[J]. 电工技术学报, 2026, 41(17): 5903-5916.
Zhang Zhenlong, Nie Dawen, Zhang Xinsheng, Ren Jinge. xLSTM-Informer Wind Power Prediction with Synergistic Optimization of Feature Selection and Signal Decomposition. Transactions of China Electrotechnical Society, 2026, 41(17): 5903-5916.
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2026, Vol. 41 
