基于深度学习的FY-4A图像地表太阳辐照度预测OA

To address the issues such as complex modeling,limited prediction range,low accuracy,significant seasonal variations,and difficulties in multi-task collaborative prediction in existing methods for surface solar irradiance prediction,a deep learning model based on the Encoder-Decoder framework combined with convolutional neural networks,self-attention mechanism and bidirectional gated recurrent units has been proposed,which is used to predict the surface solar irradiance.Firstly,the images are processed using the block partitioning technology,the convolutional layers are constructed in the encoder and the output feature vector dimension is increased to extract spatial features of image sequences,significantly expanding the prediction range and enhancing feature extraction capabilities.Subsequently,the self-attention mechanism is employed to encode the input sequence and the global contextual temporal information is captured by the bidirectional gated recurrent units to reduce the limitations of local receptive fields.Finally,to improve short-term prediction accuracy of surface solar irradiance,a weighted loss function based on mean squared error and structural similarity is adopted.The results suggest that the inputted parameters are simplified and the mean squared error is remarkably reduced to 21.75(W/m2)2 with a high structural similarity of 88.46%.Compared to traditional algorithms,the proposed method exhibits obvious improvements,which can effectively enhance the prediction performances of surface solar irradiance.