ConvLSTM Prediction of Cloud Movement Based on Meteorological Satellite Images

Abstract

We propose a cloud image prediction method using convolutional LSTM to improve the prediction accuracy for photovoltaic power generation systems. In particular, we focus on the effects of input data of the prediction model to improve the accuracy of cloud movement predictions. Future cloud images are generated (estimated) using cloud movement vectors obtained from time-series cloud images. Then, we examine the future images as input data for the model. The performance of the proposed method is evaluated from the error and correlation between the ground truth image and the predicted image.