仅用公开传感器数据，实现高精度本地太阳能预测。
Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies
- 基于因果关系建模三类干扰因素，融合嵌入、图网络与门控变压器。
- 在多地理条件下超越主流模型，较商业方案误差降低25.9%。
- 无需卫星或摄像头，适合资源受限场景部署。
精准的太阳能预测对可再生能源管理至关重要。本文提出SolarCAST，一种基于因果信息的模型，仅使用目标站点X及周边站点S的历史全球水平辐照度（GHI）数据，即可预测未来GHI，无需依赖需专用硬件和复杂预处理的云相机或卫星图像。为在仅使用公共传感器数据的前提下实现高精度，SolarCAST采用可扩展的神经组件建模三类导致X-S相关性的混淆因素：(i) 可观测的同步变量（如时间、站点身份），通过嵌入模块处理；(ii) 隐含的同步因素（如区域天气模式），由时空图神经网络捕捉；(iii) 时滞影响（如云团跨站点移动），通过门控变压器学习时间偏移。该模型在多种地理条件下均优于主流时间序列与多模态基线，相较顶级商业预报器Solcast实现25.9%的误差降低。SolarCAST提供了一种轻量、实用且可泛化的本地太阳能预测解决方案。
原文摘要 · Abstract (English)
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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