A Multistep Direct and Indirect Strategy for Predicting Wind Direction Based on the EMD-LSTM Model
Yang Ding, Xiao‐Wei Ye, Yong Guo
Abstract
For the wind speed prediction, many researchers have established prediction models based on machine learning methods, statistical methods, and theoretical methods, that is, direct methods. However, the direct method cannot be widely used in the wind direction prediction because the wind direction has strong randomness and uncertainty. In order to solve this problem, this paper proposed a wind direction prediction method, that is, indirect method. Specifically, the wind speed is decomposed into crosswind speed and alongwind speed considering the correlation between wind speed and wind direction. The crosswind speed and alongwind speed are predicted based on long short-term memory (LSTM) model with empirical mode decomposition (EMD), and then, the wind direction prediction value can be calculated, that is, the wind direction prediction is realized. One-month wind monitoring data collected by the structural health monitoring (SHM) system installed on investigated bridge are employed to demonstrate the effectiveness of direct and indirect prediction for forecasting the wind speed and wind direction.