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 most important control parameters in the methanol distillation process, which are directly related to product quality and yield, are the temperature, pressure and water content of the finished product at the top of the column. In order to adapt to the development trend of modern industrial technology to be more accurate, faster and more stable, the fusion of multi-sensor data puts forward higher requirements. Traditional control methods, such as PID control and fuzzy control, have the disadvantages of low heterogeneous data processing capability, poor response speed and low control accuracy when dealing with complex industrial process detection and control. For the control of tower top temperature and pressure in the methanol distillation industry, this study innovatively combines generative artificial intelligence and a type II fuzzy neural network, using a GAN for data preprocessing and a type II fuzzy neural network for steady-state inverse prediction to construct the GAN-T2FNN temperature and pressure control model for an atmospheric pressure tower. Comparison experiments with other neural network models and traditional PID control models show that the GAN-T2FNN model has a better performance in terms of prediction accuracy and fitting effect, with a minimum MAE value of 0.1828, which is more robust, and an R² Score of 0.9854, which is closer to 1, for the best overall model performance. Finally, the SHAP model was used to analyze the influence mechanism of various parameters on the temperature and pressure at the top of the atmospheric column, which provides a more comprehensive reference and guidance for the precise control of the methanol distillation process.

As the demand for deep mineral resource extraction intensifies, optimizing pipeline transportation systems in backfill mining has become increasingly critical. Thus, reducing energy loss while ensuring the filling effect becomes crucial for improving process efficiency. Owing to variations among mines, accurately calculating pipeline resistance loss remains challenging, resulting in significant inaccuracies. The rapid development of Industry 4.0 provides intelligent and data-driven optimization ideas for this challenge. This study introduces a novel pipeline resistance loss prediction framework integrating generative artificial intelligence with a TransKAN model. This study employs generative artificial intelligence to produce physically constrained augmented data, integrates the KAN network’s B-spline basis functions for nonlinear feature extraction, and incorporates the Transformer architecture to capture spatio-temporal correlations in pipeline pressure sequences, enabling precise resistance loss calculation. The experimental data collected from pipeline pressure sensors provides empirical validation for the model. Compared with traditional mathematical formulas, BP neural networks, SVMs, and random forests, the proposed model demonstrates superior performance, achieving an R² value of 0.9644, an RMSE of 0.7126, and an MAE of 0.4703.

In intelligent transportation systems (ITSs), the collaborative optimization of multimodal traffic data is the core support for improving the efficiency of urban green travel. However, due to the dual contradictions of surging travel demand and resource mismatch faced by the green transportation system, traditional methods are difficult to effectively analyze the heterogeneous associations of multimodal traffic data. Compared with existing methods, the distribution of shared single spatiotemporal data is dynamically unbalanced, and the interactive modeling of multi-modal traffic states lacks dynamic topological adaptive modeling capabilities. To solve this problem, this paper proposes a G²-HGNN framework (GAN-GBC-Hypergraph Neural Network), which integrates the OD matrix of shared bicycles, online car-hailing, and buses and subways into a multi-modal traffic state hypergraph, and constructs a dynamic hypergraph with dual-domain coupling of time and space for traffic state prediction and optimization. Firstly, the spatiotemporal distribution of orders from multiple transportation parties is analyzed, and the GAN network is used to solve the problem of dynamic imbalance of shared bicycles. Secondly, the granular ball computing classifier (GBC) is used to construct feature hyperedges, and a multivariate traffic state graph of traffic mode distribution is constructed. The dynamic G²-HGNN framework is constructed to model the high-order relationships of the hypergraph and dynamically embed nodes. The traffic state prediction optimization is performed through the joint optimization mechanism of hypergraph convolution and topology perception. Finally, it is verified in two travel mode datasets of bimodal (working days) and scattered (rest days) in Luohu District, Shenzhen. Comprehensive experiments show that compared with other benchmark methods, the prediction accuracy, F1-Score, recall rate and precision of the scheme in green transportation planning reach 88.71%, 92.51%, 97.32% and 88.16% respectively, which reflects the superior performance of G²-HGNN in actual traffic planning tasks and provides data and modeling support for future urban transportation development planning.