We present a novel framework for spatiotemporal photovoltaic (PV) power forecasting and use it to evaluate the reliability, sharpness, and overall performance of seven intraday PV power nowcasting models. The model suite includes satellite-based deep learning and optical-flow approaches and physics-based numerical weather prediction models, covering both deterministic and probabilistic formulations. Forecasts are first validated against satellite-derived surface solar irradiance (SSI). Irradiance fields are then converted into PV power using station-specific machine learning models, enabling comparison with production data from 6434 PV stations across Switzerland. To our knowledge, this is the first study to investigate spatiotemporal PV forecasting at a national scale. We additionally provide the first visualizations of how mesoscale cloud systems shape national PV production on hourly and sub-hourly timescales. Our results show that satellite-based approaches outperform the Integrated Forecast System (IFS-ENS), particularly at short lead times. Among them, SolarSTEPS and SHADECast deliver the most accurate SSI and PV power predictions, with SHADECast providing the most reliable ensemble spread. The deterministic model IrradianceNet achieves the lowest root mean square error, while probabilistic forecasts of SolarSTEPS and SHADECast provide better-calibrated uncertainty. Forecast skill generally decreases with elevation. At a national scale, satellite-based models forecast the daily total PV generation with relative errors below 10% for 82% of the days in 2019-2020, demonstrating robustness and their potential for operational use.

翻译：本文提出了一种新颖的时空光伏功率预测框架，并利用该框架评估了七种日内光伏功率临近预报模型的可靠性、锐度及综合性能。该模型集涵盖了基于卫星的深度学习与光流方法，以及基于物理的数值天气预报模型，同时包含确定性及概率性两种形式。预报结果首先与卫星反演的地表太阳辐照度数据进行验证。随后，利用站点特定的机器学习模型将辐照度场转换为光伏功率，从而能够与瑞士境内6434个光伏电站的实际发电数据进行对比。据我们所知，这是首个在全国尺度上研究时空光伏预测的工作。此外，我们首次实现了中尺度云系如何在小时及亚小时时间尺度上影响全国光伏发电的可视化呈现。研究结果表明，基于卫星的预报方法在短预见期内表现优于集成预报系统，其中SolarSTEPS与SHADECast模型提供了最准确的地表太阳辐照度及光伏功率预测，且SHADECast能给出最可靠的集合离散度。确定性模型IrradianceNet取得了最低的均方根误差，而SolarSTEPS与SHADECast的概率性预报则能提供更优校准的不确定性。预报技巧总体上随海拔升高而降低。在全国尺度上，基于卫星的模型对2019-2020年间82%的日期的光伏日总发电量预测相对误差低于10%，展现了其稳健性及业务化应用的潜力。