Ground-based Cloud Map Prediction Algorithm Based on Spatio-temporal Long Short-term Memory Neural Network
A ground-based cloud map prediction algorithm based on a Spatio-Temporal Long Short-Term Memory(ST-LSTM)neural network is proposed to address the problems of poor prediction accuracy and the loss of spatial structure details in traditional cloud motion trajectory prediction methods.First,a convolutional coding network is used to extract the high-dimensional image features of the input video stream.Then,multiple branches of potential information are obtained from the image in the feature extraction model.One part uses a ST-LSTM neural network to extract spatiotemporal features between different frames.The other part decomposes the image sequence and passes the decomposed information through a memory fusion network based on a gating mechanism to obtain the structural details in the image.Finally,the obtained branching features are combined.The final predicted video stream is output by a decoding network.Experimental results on the ground-based cloud map,Moving MNIST,and Human 3.6M datasets show that the prediction model outperforms current state-of-the-art models in terms of image prediction accuracy,structural detail information retention,and subjective perception by the human eye.Compared with the benchmark model TaylorNet,its Mean Squared Error(MSE)and Mean Absolute Error(MAE)metrics are reduced by 15.7%and 11.8%,respectively,on the Moving MNIST dataset.The Structural Similarity(SSIM)and Peak Signal-to-Noise Ratio(PSNR)metrics are improved by 1%and 3.2%,respectively,on the ground-based cloud map dataset.Additionally,the generated video stream data is clearer,which helps to describe the future motion of the clouds more accurately.This leads to more reliable predictions of the output power of the photovoltaic power station.
deep learningvideo predictionground-based cloud mapMaclaurin expansionSpatio-Temporal Long Short-Term Memory(ST-LSTM)neural network
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