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arXiv · 2509.15481

Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies

Yanan Niu·Demetri Psaltis·Christophe Moser·Luisa Lambertini
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Abstract

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.

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Yanan Niu, Demetri Psaltis, Christophe Moser, Luisa Lambertini. 2025-09-18. Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies. https://doi.org/10.1145/3746252.3760905

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