Niu, YananSarkis, RoyPsaltis, DemetriPaolone, MarioMoser, ChristopheLambertini, Luisa2025-04-162025-04-162025-04-142025-02-2610.1109/wacv61041.2025.00494https://infoscience.epfl.ch/handle/20.500.14299/249347Accurate intraday solar irradiance forecasting is crucial for optimizing dispatch planning and electricity trading. For this purpose, we introduce a novel and effective approach that includes three distinguishing components from the literature: 1) the uncommon use of single-frame public camera imagery; 2) solar irradiance time series scaled with a proposed normalization step, which boosts performance; and 3) a lightweight multimodal model, called Solar Multimodal Transformer (SMT), that delivers accurate shortterm solar irradiance forecasting by combining images and scaled time series. Benchmarking against Solcast, a leading solar forecasting service provider, our model improved prediction accuracy by 25.95%. Our approach allows for easy adaptation to various camera specifications, offering broad applicability for real-world solar forecasting challenges.entext::conference output::conference proceedings::conference paper