Optimization Research on Wind Power Prediction System for High-altitude Wind Power
In order to improve the accuracy of wind power prediction for high-altitude wind power,the wind power prediction system model is optimized.The article takes wind power data preprocessing as the starting point,based on VMD decomposition method,extracts frequency domain information and time domain information as modal air volume,divides it into low-frequency and high-frequency components using AFC algorithm,and adopts adaptive network models to learn and train different frequency feature components.The results indicate that the AT encoding decoding network can be chosen for low-frequency signals,and the DBN network can be chosen for high-frequency components to integrate their data and effectively improve the prediction accuracy of the model.
high mountainswind power generationwind power predictionsystem optimization
万方数据
维普
