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 (R 2 ), 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 . • This paper proposes a spatio-temporal wind-speed clustering denoising method that outperforms traditional clustering methods in noise reduction. • A CNN optimizes weights for VMD-decomposed frequency-domain wind-speed signals to reduce decomposition-induced signal loss. • Informer-BiGRU-Temporal Attention model achieves superior wind speed prediction accuracy over traditional methods. • The research introduces multi-step wind speed prediction, demonstrating the model's high accuracy in multi-step forecasting. • The study provides models and findings that offer valuable references for future analyses and predictions.