Investigation on Ground-Based Cloud Image Classification and Its Application in Photovoltaic Power Forecasting

Ground-based cloud image categorization is crucial for ground-cloud observation and significantly impacts activities like photovoltaic (PV) power forecasting. Current techniques fail to effectively integrate novel cloud image texture features with image classification algorithms. Enhancing cloud categorization precision in publicly available datasets and research tailored for PV power forecasting remains in its early stages. This study introduces a novel Cloud-ConvNeXt classification approach utilizing a comprehensive texture feature map. We generate a detailed texture feature map for significant areas of ground-based cloud images using a gray-level co-occurrence matrix (GLCM) and local binary patterns (LBPs). This image serves as input for an enhanced ConvNeXt classification network, producing precise cloud categories based on meteorological standards. Additionally, cloud occlusion feature and cloud factor feature are extracted from ground-based cloud images to improve cloud categories properties. Results show that the Cloud-ConvNeXt technique surpasses other models, achieving an accuracy of 85.40%. We have constructed a support vector machine (SVM)-Informer model to predict the power output of highly efficient PV systems in the next 5 min. The results demonstrate that our predictions outperform other methods that depend on cloud images acquired from the ground, with a coefficient of determination (R2) of 0.9668. Furthermore, ablation experiments on attributes linked to different cloud categories confirm their efficacy, thus building a strong foundation for precise solar production forecasting.