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.

Immediately and accurately assessing the risk of coal spontaneous combustion and taking targeted action are crucial steps in coal spontaneous combustion prevention and control. A new model, AEM-AHP-LSTM, was proposed to solve the weight calculation problem of multiobjective evaluation in the process of coal spontaneous combustion. Firstly, the key indicators of coal spontaneous combustion were analyzed and used as risk factors to establish an evaluation system. Next, the objective and subjective weights were calculated using AEM and AHP, respectively. The objective and subjective weights were then combined, and TOPSIS was used to calculate the score of the evaluation sample. Finally, the obtained evaluation samples were trained with the BP, RBF, and LSTM model to resolve the problem of model overdependence on historical data and achieve the auto-adapt adjustment of weight with data change. Additionally, data from 15 typical Chinese coal mines were applied to the model. The results indicate that, compared with the BP and RBF neural networks, the LSTM model has higher prediction accuracy, stronger generalization ability, and stronger practicability. The modeling and application findings show that the AEM-AHP-LSTM model was better appropriate for the risk assessment of coal spontaneous combustion. This method can potentially be further applied as reliable approach for the assessment of mine disaster risk.

To improve the accuracy of gas outburst early warning, this paper proposes a gas outburst risk warning model based on XGBoost–GR–stacking. The statistic is based on gas outburst data from 26 mines and establishes a data generation model based on XGBoost. The obtained virtual datasets are analyzed through visualization analysis and ROC curve analysis with respect to the original data. If the augmented data has an ROC area under the curve of 1, it indicates good predictive performance of the augmented data. Grey correlation analysis is used to calculate the grey correlation degrees between each indicator and the “gas emission”. The indicator groups with correlation degrees greater than 0.670 are selected as the main control factor groups based on the sorting of correlation degrees. In this study, SVM, RF, XGBoost, and GBDT are selected as the original models for stacking. The original data and virtual data with correlation degrees greater than 0.670 are used as inputs for SVM, RF, XGBoost, GBDT, and stacking fusion models. The results show that the stacking fusion model has an MAE, MSE, and R2 of 0.031, 0.031, and 0.981. Comparing the actual and predicted values for each model, the stacking fusion model achieves the highest accuracy in gas outburst prediction and the best model fitting effect.

Predicting the temperature of the coal spontaneous combustion (CSC) is essential for preventing and managing coal mine fires. In this paper, a Rough Set-Stacking-SHapley Additive Explanations (RS-Stacking-SHAP) prediction model of CSC based on grid search optimized is proposed. Compared with the traditional machine learning model, the model has better prediction accuracy and generalization ability. Based on the data collected from experimental coal samples in Lijiahao Coal Mine, rough set algorithm was used for attribute approximation to identify O₂, CO, CH₄, C₂H₄, C₂H₆, C₃H₈, CO/CH₄, C₂H₄/C₂H₆ as the model indexes, thereby establishing the system of warning indexes for spontaneous combustion of coal. XGBoost, SVR, RF, LightGBM and BP models were selected as base models to establish an early warning model for CSC based on the stacking integration architecture. The grid search algorithm was utilized to optimize the model parameters, ensuring the selection of the most suitable parameter configurations. The dataset was then divided into the training and test sets in a 7:3 ratio, and the extracted indicators of each gas were used as inputs to the model and the temperature was used as outputs. The mean absolute error (MAE), root mean square error (RMSE), r-square (R²), mean absolute percentage error (MAPE), weighted mean absolute percentage error (WMAPE) and variance account for (VAF) were chosen to evaluate the results. The predictive performance of the model was compared with that of the individual base models, and the results displayed that the R² value of the RS-Stacking model was 0.991, representing improvements of 12.7%, 14.1%, 0.6%, 3.5% and 17.7% over the XGBoost, SVR, RF, LightGBM, and BP models, respectively. GS-RS-Stacking was considered to be the best model, where MAPE = 5.14%, WMAPE = 3.76%, VAF = 99.08%, MAE = 5.081, RMSE = 6.461, close to the ideal value. Finally, we used SHAP to provide global feature interaction interpretation and local interpretation for the model, analyzing the contributions of CH₄, C₂H₆, C₃H₈, and CO to the model’s predictive outcomes. The results show that the model proposed in this paper has better prediction effect and robustness for temperature of CSC.