An Informer-BiGRU-temporal attention multi-step wind speed prediction model based on spatial-temporal dimension denoising and combined VMD decomposition

Accurate wind speed prediction is crucial for optimizing renewable energy utilization and enhancing operational safety in wind farms. However, existing methods face challenges due to data noise, mode mixing in decomposition, and limited model adaptability for multi-step forecasting. This paper proposes a novel hybrid framework (HPMTC-CVMD-IBTA) integrating three innovations: (1) A spatial-temporal denoising method (HPMTC) combining high-order polynomial fitting with M-estimator correction and temporal clustering to preserve signal integrity while removing noise; (2) A decomposition-optimization approach (CVMD) that adaptively weights variational mode decomposition (VMD) components via convolutional neural networks, reducing reconstruction errors compared to traditional methods; and (3) An Informer-BiGRU-Temporal Attention (IBTA) model that leverages multi-variable dependencies and long-sequence patterns through bidirectional gated units and attention mechanisms. Experiments on real-world wind farm datasets (Guangdong and Gansu, China) demonstrate the framework's superiority: It achieves over 99 % prediction accuracy (R2), reduces MAE by 15–40 % against benchmarks (e.g., LSTM, BiGRU), and improves multi-step forecasting robustness across seasons. The proposed system addresses critical limitations in noise sensitivity, decomposition instability, and temporal feature decay, offering a reliable solution for energy management and disaster prevention.