四川轻化工大学学报（自然科学版）2024，Vol.37Issue(4)：58-67,10.DOI:10.11863/j.suse.2024.04.07
基于深度学习的FY-4A图像地表太阳辐照度预测
Prediction of Surface Solar Irradiance in FY-4A Images Based on Deep Learning
摘要
Abstract
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.关键词
地表太阳辐照度预测/卷积神经网络/自注意力机制/双向门控循环单元/深度学习/图像序列Key words
surface solar irradiance prediction/convolutional neural networks/self-attention mechanism/bidirectional gated recurrent units/deep learning/image sequences分类
信息技术与安全科学引用本文复制引用
姚蕊,刘小芳,张杰,郭旭萍..基于深度学习的FY-4A图像地表太阳辐照度预测[J].四川轻化工大学学报（自然科学版）,2024,37(4):58-67,10.基金项目
教育部高等教育司产学合作协同育人项目(202101038016) （202101038016）
高层次创新人才培养专项资助项目(B12402005) （B12402005）
四川轻化工大学人才引进项目(2021RC16) （2021RC16）
