Under the background of “double carbon”, building carbon emission reduction is urgent, and improving energy efficiency through short-term building heat load forecasting is an efficient means of building carbon emission reduction. Aiming at the characteristics of the decomposed short-term building heat load data, such as complex trend changes, significant seasonal changes, and randomness, a single-step short-term building heat load prediction method driven by the multi-component fusion LSTM Ridge Regression Ensemble Model (ST-LSTM-RR) is designed and implemented. First, the trend and seasonal components of the heat load are decomposed by the STL seasonal decomposition algorithm, which are fused into the original data to construct three diversified datasets; second, three basic models, namely, the trend LSTM, the seasonal LSTM, and the original LSTM, are trained; and then, the ridge regression model is trained to fuse the predicted values of the three basic models to obtain the final predicted values. Finally, the method of this paper is applied to the heat load prediction of eight groups in a large mountain hotel park, and the root mean square error (RMSE) and mean absolute error (MAE) are used as the evaluation indexes. The experimental results show that the average RMSE and average MAE of the prediction results of the proposed method in this paper are minimized on the eight groups.

Rockburst may cause damage to engineering equipment, disrupt construction progress, and endanger human life. To this day, the occurrence of rockburst remains complex and difficult to predict. This study proposes the D-P-Transformer algorithm to address this issue by improving the embedding structure of the Transformer for specific applications to rockburst data. To reduce the computational requirement, sparse self-attention is adopted to replace self-attention. A distilling operation and multiple layer replicas are simultaneously used to enhance the robustness and speed up the algorithm’s process. Taking all relevant rockburst factors into consideration, multiple experiments are conducted on seven large-scale rockburst datasets with different training ratios to verify the reliability of the proposed D-P-Transformer rockburst prediction algorithm. As compared to the original algorithm, the proposed algorithm shows average reductions of 24.45%, 46.56%, 17.32%, and 48.11% in the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. The results indicate that the novel D-P-Transformer rockburst prediction algorithm is superior to the Transformer prediction algorithm, and could be used for coal mine rockburst prediction analysis.