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@article{Gatón2026,

title = {Multi-frame cloud prediction in all-sky images from RGB images and segmented masks},

author = {Javier Gatón and Roberto Román and Cesar Guzman and Daniel González-Fernández and Bruno Longarela and Carlos Toledano and Ramiro González},

url = {https://www.sciencedirect.com/science/article/pii/S0038092X26002033},

doi = {https://doi.org/10.1016/j.solener.2026.114515},

issn = {0038-092X},

year  = {2026},

date = {2026-03-18},

urldate = {2026-01-01},

journal = {Solar Energy},

volume = {311},

pages = {114515},

abstract = {This paper presents a comparative study on the impact of input representation on deterministic artificial intelligence models for short-term multi-frame prediction in all-sky images. This work compares a model operating on 8-bit RGB all-sky images with a model that shares the same backbone, but operates directly on semantically segmented masks that encode cloud-related classes. Using an available sky segmentation model, predictions are evaluated in the segmentation label space using segmenter-derived masks as a proxy reference. Within this evaluation framework, the use of semantic masks as input for short-term prediction leads to improved temporal stability and higher agreement across standard segmentation metrics such as intersection over union, Dice coefficient, and categorical cross-entropy. While these results suggest potential relevance for weather and solar energy nowcasting applications, further validation against physical irradiance measurements is required.},

keywords = {All-sky images, Artificial intelligence, Cloud motion prediction, clouds, Next-frame prediction, Semantic segmentation},

pubstate = {published},

tppubtype = {article}

}