Abstract
This study introduces a novel method for predicting solar radiation by focusing on the sun's position and cloud conditions in the sky. The proposed approach utilizes sun position feature maps and spatial attention mechanisms to improve the accuracy of solar irradiance prediction. Key contributions include the development of sun position feature maps to precisely locate the sun in all-sky images and the integration of spatial attention mechanisms with convolutional neural networks to enhance predictive modeling. Comparative analysis with three other models demonstrates the effectiveness of the proposed approach, particularly in overcast conditions. These innovative techniques significantly enhance the accuracy of solar radiation prediction and offer valuable insights for the design and operation of renewable energy systems.
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