Accurate forecasting of wind power is crucial for addressing energy demands, promoting sustainable energy practices, and mitigating environmental challenges. In order to improve the prediction accuracy of wind power, a VMD-CNN-BiLSTM hybrid model with physical constraints is proposed in this paper. Initially, the isolation forest algorithm identifies samples that deviate from actual power outputs, and the LightGBM algorithm is used to reconstruct the abnormal samples. Then, leveraging the variational mode decomposition (VMD) approach, the reconstructed data are decomposed into 13 sub-signals. Each sub-signal is trained using a CNN-BiLSTM model, yielding individual prediction results. Finally, the XGBoost algorithm is introduced to add the physical penalty term to the loss function. The predicted value of each sub-signal is taken as the input to get the predicted result of wind power. The hybrid model is applied to the 12 h forecast of a wind farm in Zhangjiakou City, Hebei province. Compared with other hybrid forecasting models, this model has the highest score on five performance indicators and can provide reference for wind farm generation planning, safe grid connection, real-time power dispatching, and practical application of sustainable energy.

The large-scale integration of wind power generation, as an important sustainable energy, into the power grid relies on the support of the power system, and accurate wind power prediction is the key to ensuring the continuous and stable operation of the power system. Therefore, this paper proposes a hybrid wind power prediction model that integrates Successive Variational Mode Decomposition (SVMD) with KANCNN-BiLSTM. To address data volatility, the original wind power sequence is decomposed into seven modal components using SVMD. Subsequently, for enhanced capability in capturing nonlinear relationships, a KAN linear layer is integrated into a convolutional neural network, constructing the KANCNN-BiLSTM model for component prediction. Simultaneously, model hyperparameters are optimized via the Optuna framework to further improve predictive performance. Additionally, SHAP theory is applied to interpret the contribution of each component to the prediction results, thereby enhancing the transparency of the decomposition–integration process. Experimental results indicate that the proposed interpretable SVMD-KANCNN-BiLSTM wind power prediction model achieves a prediction accuracy of 0.998959, outperforms all comparison models across multiple evaluation metrics, and indicates superior predictive capability; additionally, the global interpretability analysis reveals that all IMF components positively contribute to the model’s predictions. The establishment of this model provides an interpretable new approach for realizing wind power prediction.