Time Series Prediction of Wind Speed Based on SARIMA and LSTM

Wind speed has an important impact on the navigation of ships at sea. If the wind speed can be accurately predicted, the safety of ship navigation would be greatly improved. This paper proposes a wind speed series prediction model based on SARIMA and LSTM. Firstly, the SARIMA model is used to predict and model the observed wind speed sequence data to obtain the predicted value and the residual between the predicted value and the observed value. Training the long and short-term memory neural network with the residual sample set to get a trained network for residual prediction. Finally, to sum the two parts predicted values up to obtain the predicted value of the wind speed series. In order to test the prediction effect of this model, a deep learning environment based on Keras was built, and 5 days of real-time wind speed data in a certain sea area of the South China Sea was used as the input of the model. The prediction results are compared with the prediction results of SARIMA model, LSTM network model, BP network model, LSTM and ARIMA combined model. The experimental results show that the model has high accuracy and less error in the prediction of wind speed series.