A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD

Abstract

Accurate typhoon wind speed prediction is significant because it enables wind farms to take advantage of high wind speeds and to simultaneously protect wind turbines from damage. However, the wind characteristics of the typhoon are highly random, fluctuating, and nonlinear, which makes precise prediction difficult. One-year wind data collected from a wind farm on the southeast coast of China are employed in the study. The characteristics of the typhoon are analyzed, and a sensitivity study is carried out by comparing two groups of training datasets. This study proposes a hybrid approach that considers both the physical model and the artificial neural network (ANN) model to accurately predict the short-term typhoon wind speed. The variational mode decomposition (VMD) algorithm is selected to decompose wind speed, and the particle swarm optimization (PSO) method is employed to optimize the bidirectional, long short-term memory (Bi-LSTM) prediction model. The results show that the proposed PSO-VMD-Bi-LSTM has strong robustness for making uncertainty predictions and can be utilized to predict the wind speed of typhoons. This study demonstrates the potential of an innovative ANN method to predict wind speed during the typhoon period.