A Deep Learning Framework for Estimating Global and Diffuse Solar Irradiance Using All-Sky Images

Abstract

Nowadays, all-sky imagers (ASI) provide valuable information regarding the sky’s state, and they have been extensively used in cloud detection, segmentation, and solar forecasting studies. In this study, global and diffuse horizontal irradiances (GHI and DHI) are modeled using a Convolutional Neural Network (CNN) and Red–Green–Blue (RGB) information retrieved through ASI images. The predicted GHI and DHI underestimated observations with systematic biases of −1.8 W m−2 and −0.5 W m−2, while the dispersion errors were 82.7 W m−2 and 39.8 W m−2, respectively. The correlation coefficient was high, approaching 0.95 and 0.85 for GHI and DHI.

Keywords

• clouds
• all-sky images
• convolutional neural network
• RGB
• GHI
• DHI