Spatiotemporal multi-step prediction of hydraulic support pressure based on LSTM-Informer model

Abstract

Currently, most multi-step hydraulic support pressure predictions are cumulative predictions of single step hydraulic support pressure. The more times a single step accumulates, the greater the cumulative error, which affects the prediction precision. In order to solve the above problems, a spatiotemporal multi-step prediction method of hydraulic support pressure based on long short term memory (LSTM)-Informer model is proposed. After using Kalman filtering to eliminate vibration noise in hydraulic support pressure data, two spatiotemporal datasets (Dataset 1 and Dataset 2) are established by selecting 5 adjacent hydraulic support pressure data at the end and middle of the working face. The spatiotemporal data is standardized and preprocessed. The method inputs spatiotemporal data into the LSTM model to extract spatiotemporal features, and inputs the extracted spatiotemporal features into the encoder of the Informer model. After position encoding, the method outputs multi head probability sparse self attention to focus on the changing features of the pressure sequence. After maximum pooling and one-dimensional convolution, the method eliminates the redundant combination of output feature map. By utilizing multi head probability sparse self attention to further focus on pressure sequence features, the decoder of the Informer model is changed to a fully connected layer to obtain the prediction results of hydraulic support pressure. The experimental results show that compared with prediction methods based on gated recurrent unit (GRU), LSTM, and Informer models, prediction methods based on LSTM-Informer model has the smallest root mean square error (RMSE) and mean absolute error (MAE) in predicting hydraulic support pressure at 6, 12, and 24 step sizes. The RMSE of the 6-step hydraulic support pressure predicted based on dataset 1 decreases by 41.63%, 49.74%, and 11.85%, and the MAE decreases by 41.75%, 50.00%, and 12.00%, respectively. The RMSE of the 6-step hydraulic support pressure predicted based on dataset 2 decreases by 48.15%, 59.86%, and 19.88%, and MAE decreases by 49.87%, 54.90%, and 13.16%, respectively.

Keywords

• hydraulic support pressure
• multi-step hydraulic support pressure prediction
• lstm-informer model
• time correlation
• kalman filtering