Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918144183894016 |
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| author | Niu, Yanan Psaltis, Demetri Moser, Christophe Lambertini, Luisa |
| author_facet | Niu, Yanan Psaltis, Demetri Moser, Christophe Lambertini, Luisa |
| contents | Accurate solar forecasting underpins effective renewable energy management. We present SolarCAST, a causally informed model predicting future global horizontal irradiance (GHI) at a target site using only historical GHI from site X and nearby stations S - unlike prior work that relies on sky-camera or satellite imagery requiring specialized hardware and heavy preprocessing. To deliver high accuracy with only public sensor data, SolarCAST models three classes of confounding factors behind X-S correlations using scalable neural components: (i) observable synchronous variables (e.g., time of day, station identity), handled via an embedding module; (ii) latent synchronous factors (e.g., regional weather patterns), captured by a spatio-temporal graph neural network; and (iii) time-lagged influences (e.g., cloud movement across stations), modeled with a gated transformer that learns temporal shifts. It outperforms leading time-series and multimodal baselines across diverse geographical conditions, and achieves a 25.9% error reduction over the top commercial forecaster, Solcast. SolarCAST offers a lightweight, practical, and generalizable solution for localized solar forecasting. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies Niu, Yanan Psaltis, Demetri Moser, Christophe Lambertini, Luisa Machine Learning Social and Information Networks I.2.6; I.5.4 Accurate solar forecasting underpins effective renewable energy management. We present SolarCAST, a causally informed model predicting future global horizontal irradiance (GHI) at a target site using only historical GHI from site X and nearby stations S - unlike prior work that relies on sky-camera or satellite imagery requiring specialized hardware and heavy preprocessing. To deliver high accuracy with only public sensor data, SolarCAST models three classes of confounding factors behind X-S correlations using scalable neural components: (i) observable synchronous variables (e.g., time of day, station identity), handled via an embedding module; (ii) latent synchronous factors (e.g., regional weather patterns), captured by a spatio-temporal graph neural network; and (iii) time-lagged influences (e.g., cloud movement across stations), modeled with a gated transformer that learns temporal shifts. It outperforms leading time-series and multimodal baselines across diverse geographical conditions, and achieves a 25.9% error reduction over the top commercial forecaster, Solcast. SolarCAST offers a lightweight, practical, and generalizable solution for localized solar forecasting. |
| title | Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies |
| topic | Machine Learning Social and Information Networks I.2.6; I.5.4 |
| url | https://arxiv.org/abs/2509.15481 |