An integrated deep neural network framework for predicting the wake flow in the wind field

Ultra-short-term wake flow prediction is crucial for wind resource assessment and wind farm operation control. To improve the power generation efficiency and stable operation level of wind farms, a kind of prediction method is proposed that integrates the physical model and mathematical model into a deep neural network, enabling the prediction of the precise wake flow with sparse measured data. The proposed method can predict the entire flow field in real-time, providing accurate and reliable predictions for wind farm operation and management. The results of evaluation and validation of the integrated method show that the proposed method can accurately achieve ultra-short-term prediction, with a small error in all directions of velocity. Compared with the widely used LSTM neural network model and Multilayer Perceptron, there are certain advantages in both spatial and temporal scales, with a significant reduction in the average absolute error, indicating better generalization performance and prediction accuracy in the prediction of the wake flow field.