Short-Term Prediction of 80–88 km Wind Speed in Near Space Based on VMD–PSO–LSTM

Abstract

The accurate prediction of atmospheric wind speed in near space is of importance for both middle and upper atmospheric scientific research and engineering applications. In order to improve the accuracy of short-term wind speed predictions in near space, this paper proposes a multi-step hybrid prediction method based on the combination of variational modal decomposition (VMD), particle swarm optimization (PSO) and long short-term memory neural networks (LSTM). This paper uses the measurement of wind speed in the height range of 80–88 km at the Kunming site (25.6° N, 103.8° E) for wind speed prediction experiments. The results show that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of multi–step wind predictions are less than 6 m/s and 15%, respectively. Furthermore, the proposed VMD–PSO–LSTM method is compared with the traditional seasonal difference autoregressive sliding average model (SARIMA) to investigate its performance. Our analysis shows that the percentage improvement of prediction performance compared to the traditional time series prediction model can reach at most 85.21% and 83.75% in RMSE and MAPE, respectively, which means that the VMD–PSO–LSTM model has better accuracy in the multi-step prediction of the wind speed.