基于深度学习的机场风长时序预测OA

To address the issues of insufficient accuracy and poor timeliness in traditional wind field prediction methods,this study introduced the Informer model to enhance the forecast accuracy of the long-term meteorological data at Xiamen Gaoqi International Airport.The paper details the unique advantages of the Informer model in handling wind field time series data,including its probabilistic sparse self-attention mechanism and self-attention distillation technology.These features enable the model to efficiently capture long-term dependencies and complex characteristics within the data.Compared with traditional Artificial Neural Networks(ANNs)and Long Short-Term Memory(LSTM)models,the Informer model demonstrates higher prediction accuracy across different time scales.In the 60-minute predictions and seasonal variations,the Informer model demonstrated high robustness and efficiency.Additionally,a comparison of the effects of different wind field variations on the model's wind field predictions revealed that the Informer model consistently maintained stable predictive performance under varying wind field conditions,further validating its broad applicability and robustness.By enhancing prediction accuracy and timeliness,this research not only provides more accurate wind speed and direction forecasts for aviation meteorological services,aiding in flight safety,optimizing flight scheduling,and improving energy efficiency,but also has a positive impact on short-term weather forecasting and offers new research ideas and solutions.It has significant implications for advancing the application of deep learning in meteorological forecasting.