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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.00250 |
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| _version_ | 1866913713024401408 |
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| author | Niu, Yanan Sarkis, Roy Psaltis, Demetri Paolone, Mario Moser, Christophe Lambertini, Luisa |
| author_facet | Niu, Yanan Sarkis, Roy Psaltis, Demetri Paolone, Mario Moser, Christophe Lambertini, Luisa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00250 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series Niu, Yanan Sarkis, Roy Psaltis, Demetri Paolone, Mario Moser, Christophe Lambertini, Luisa Computer Vision and Pattern Recognition 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. |
| title | Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.00250 |