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arXiv · 2503.00250

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
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Abstract

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.

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Yanan Niu, Roy Sarkis, Demetri Psaltis, Mario Paolone, Christophe Moser, Luisa Lambertini. 2025-02-28. Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series. https://arxiv.org/abs/2503.00250

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