Short-term Wind Speed Probabilistic Prediction Model Using DeepAR

In order to predict the wind speed along the railway in advance and ensure the safety of bridge construction and high-speed train operation, a short-term wind speed probabilistic prediction method based on the DeepAR was proposed. The method was verified by the measured wind speed data of the Pingtan Straits Highway-Railway Bridge and the Xihoumen Bridge. Four models including the hybrid model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network (WPD-CNNLSTM-CNN) were used as the comparison model for point prediction. The Simplefeed-forward model, auto-regressive integrated moving average (ARIMA) model and Random Walk model were used to carry out interval prediction with confidence of 50% and 95% as the comparison model for interval prediction. The experimental results show that in both point prediction and interval prediction, the DeepAR prediction model can extract characteristic signals from random and intermittent short-term wind speed sequences and make predictions with high accuracy. Compared with other models, the DeepAR model, with better accuracy and generalization ability, can meet the needs of short-term wind speed prediction in practical construction projects. © 2023 Science Press. All rights reserved.