基于LSTM的风矢量预测方法

The correlation between wind speed and wind direction is analyzed from the perspective of feature engineering, and the results show that wind speed and wind direction contain different feature information, which can be simultaneously used as the input variables for training models. The results also provide a basis for selecting the time length of input variables. The wind speed is decomposed into two orthogonal one-dimensional variables in east-west and north-south directions, which prevents the method complexity increased by multi-dimensional variables. The long short-term memory neural network (LSTM) is adopted to train the prediction model for wind speed in both directions, and the prediction results are restored to wind speed and wind direction prediction data. The example results show that the proposed method can better capture the information in both wind speed and wind direction, and the prediction accuracy rate is more than 90% when the prediction errors of wind speed and wind direction are respectively less than 1.0 m/s and 5°.