Regional‐scale holistic wind power prediction (WPP) is pivotal to securing the safety, stability, and economic efficiency of power systems. To improve the accuracy of regional short‐term WPP, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fine‐to‐coarse (FTC) feature mapping, and error compensation‐temporal convolutional network‐bidirectional Long short‐term memory network (EC‐TCN‐BiLSTM) is proposed in this paper. First, the regional input features, encompassing data from numerous wind farms, are decomposed using the CEEMDAN algorithm to extract intrinsic mode functions (IMFs) and residuals at different time scales. Second, the decomposed IMFs and residuals are reconstructed using the adaptive FTC feature mapping technique, forming a high‐dimensional feature set in the time‐frequency domain, which boasts fewer features than the original set, thus diminishing the computational intricacy of the prediction model. Third, by combining the strengths of TCN and BiLSTM neural networks, the temporal and spatial correlations of input features can be captured effectively. Fourth, the integration of the EC module corrects the systematic errors of the prediction results, thereby further improving the prediction accuracy. Finally, case studies elucidate the efficacy of the proposed WPP method, illustrating a 0.41%–2.4% diminution in 24‐h‐ahead root mean square error (RMSE) and a 0.68%–2.63% reduction in 96‐h‐ahead RMSE relative to conventional methodologies.

Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpretability of predictions, hindering effective support for grid dispatch planning. To address these issues, this study proposes a novel day-ahead wind power prediction method, referred to as SHapley Additive exPlanations (SHAP)–Mixture of Experts (MoE), which integrates SHAP into an MoE framework. Here, SHAP is employed for interpretability purposes. This study innovatively transforms SHAP analysis into prior knowledge to guide the decision-making of the MoE gating network and proposes a two-layer dynamic interpretation mechanism based on the collaborative analysis of gating weights and SHAP values. This approach clarifies key meteorological factors and the model’s advantageous scenarios, while quantifying the uncertainty among multiple expert decisions. Firstly, each expert model was pre-trained, and its parameters were frozen to construct a candidate expert pool. Secondly, the SHAP vectors for each pre-trained expert were computed over all sample features to characterize their decision-making logic under varying scenarios. Thirdly, an augmented feature set was constructed by fusing the original meteorological features with SHAP attribution matrices from all experts; this set was used to train the gating network within the MoE framework. Finally, for new input samples, each frozen expert model generates a prediction along with its corresponding SHAP vector, and the gating network aggregates these predictions to produce the final forecast. The proposed method was validated using operational data from an offshore wind farm located in southeastern China. Compared with the best individual expert model and traditional ensemble forecasting models, the proposed method reduces the Root Mean Square Error (RMSE) by 0.23% to 4.92%. Furthermore, the method elucidates the influence of key features on each expert’s decisions, offering insights into how the gating network adaptively selects experts based on the input features and expert-specific characteristics across different scenarios.