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

• URL: http://arxiv.org/abs/2509.15481v1
• Date: Thu, 18 Sep 2025 22:57:07 GMT
• Title: Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies
• Authors: Yanan Niu, Demetri Psaltis, Christophe Moser, Luisa Lambertini,
• Abstract summary: SolarCAST is a causally informed model predicting future global horizontal radiance (GHI) at a target site using only historical GHI from site X and nearby stations S.<n>It outperforms leading time-series and multimodal baselines across diverse geographical conditions.<n>It achieves a 25.9% error reduction over the top commercial forecaster, Solcast.
• Score: 5.291394959125948
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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