Solar Multimodal Transformer: Intraday Solar Irradiance Predictor Using Public Cameras and Time Series
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.
Cite
Text
Niu et al. "Solar Multimodal Transformer: Intraday Solar Irradiance Predictor Using Public Cameras and Time Series." Winter Conference on Applications of Computer Vision, 2025.Markdown
[Niu et al. "Solar Multimodal Transformer: Intraday Solar Irradiance Predictor Using Public Cameras and Time Series." Winter Conference on Applications of Computer Vision, 2025.](https://mlanthology.org/wacv/2025/niu2025wacv-solar/)BibTeX
@inproceedings{niu2025wacv-solar,
title = {{Solar Multimodal Transformer: Intraday Solar Irradiance Predictor Using Public Cameras and Time Series}},
author = {Niu, Yanan and Sarkis, Roy and Psaltis, Demetri and Paolone, Mario and Moser, Christophe and Lambertini, Luisa},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2025},
pages = {5051-5060},
url = {https://mlanthology.org/wacv/2025/niu2025wacv-solar/}
}