Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series

Yanan Niu, Roy Sarkis, Demetri Psaltis, Mario Paolone, Christophe Moser, Luisa Lambertini; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5051-5060

Accurate 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 short-term 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.