Research on wind field feature extraction and prediction model of Yakou terrain transmission line driven by deep learning

In response to the complex and variable terrain features and difficulty in accurately predicting wind field characteristics, this paper proposes a hybrid model that combines convolutional neural networks (CNN) and long short-term memory networks (LSTM). The model can effectively extract key features from spatial data such as topographic elevation maps and satellite remote sensing images, and combine them with time series data to make precise predictions of the wind field. Through specific experimental verification, the model has excellent performance in predicting wind speed and direction, with high prediction accuracy and robustness. The research findings are of great significance for enhancing the safety operation level of transmission lines and optimizing the development of wind energy resources.