Accurate interval forecasting of wind power is crucial for ensuring the safe, stable, and cost-effective operation of power grids. In this paper, we propose a hybrid deep learning model for day-ahead wind power interval forecasting. The model begins by utilizing a Gaussian mixture model (GMM) to cluster daily data with similar distribution patterns. To optimize input features, a feature selection (FS) method is applied to remove irrelevant data. The empirical wavelet transform (EWT) is then employed to decompose both numerical weather prediction (NWP) and wind power data into frequency components, effectively isolating the high-frequency components that capture the inherent randomness and volatility of the data. A convolutional neural network (CNN) is used to extract spatial correlations and meteorological features, while the bidirectional gated recurrent unit (BiGRU) model captures temporal dependencies within the data sequence. To further enhance forecasting accuracy, a multi-head self-attention mechanism (MHSAM) is incorporated to assign greater weight to the most influential elements. This leads to the development of a day-ahead wind power interval forecasting model based on GMM-FS-EWT-CNN-BiGRU-MHSAM. The proposed model is validated through comparison with a benchmark forecasting model and demonstrates superior performance. Furthermore, a comparison with the interval forecasts generated using the NPKDE method shows that the new model achieves higher accuracy.

In this paper, we propose SADGR, an adaptive cross-modal emotion recognition framework that follows an “align first, modulate second, and fuse later” paradigm. First, we introduce a self-supervised text–audio contrastive alignment (STA-CA) stage, which leverages large-scale unlabeled audio–text data to map heterogeneous modalities into a shared semantic space prior to fusion. Second, we design an audio-guided visual gating (AG-VR) mechanism, where emotionally salient acoustic cues dynamically modulate visual representations, suppressing redundant information while enhancing emotion-relevant patterns. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that SADGR consistently outperforms state-of-the-art methods across both regression and classification tasks, achieving superior performance in ACC-7, ACC-2, and F1-score while maintaining competitive correlation results. These results indicate that explicit self-supervised alignment combined with dynamic cross-modal gating provides an effective and practical solution for robust multimodal emotion recognition.