Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data

This work introduces a model for all-sky-image-based cloud and direct irradiance nowcasting (MACIN), which predicts direct normal irradiance (DNI) for solar energy applications based on hemispheric sky images from two all-sky imagers (ASIs). With a synthetic setup based on simulated cloud scenes, the model and its components are validated in depth. We train a convolutional neural network on real ASI images to identify clouds. Cloud masks are generated for the synthetic ASI images with this network. Cloud height and motion are derived using sparse matching. In contrast to other studies, all derived cloud information, from both ASIs and multiple time steps, is combined into an optimal model state using techniques from data assimilation. This state is advected to predict future cloud positions and compute DNI for lead times of up to inline-formula20 min. For the cloud masks derived from the ASI images, we found a pixel accuracy of 94.66 % compared to the references available in the synthetic setup. The relative error of derived cloud-base heights is 4 % and cloud motion error is in the range of inline-formula $M2inlinescrollmathml\pm \mathrm{normal\; 0.1}unit\mathrm{normal\; m}{\mathrm{normal\; s}}^{-\mathrm{normal\; 1}}$ 52pt14ptsvg-formulamathimg3fee4f8ee0736c5e67dad1621ed9b149 amt-16-3257-2023-ie00001.svg52pt14ptamt-16-3257-2023-ie00001.png . For the DNI nowcasts, we found an improvement over persistence for lead times larger than 1 min. Using the synthetic setup, we computed a DNI reference for a point and also an area of inline-formula500 m×500 m. Errors for area nowcasts as required, e.g., for photovoltaic plants, are smaller compared with errors for point nowcasts. Overall, the novel ASI nowcasting model and its components proved to work within the synthetic setup.