利用卫星基太阳能预报模型进行全国范围内的日内时空光伏电力预测
Intraday spatiotemporal PV power prediction at national scale using satellite-based solar forecast models
AI总结：
本文提出了一种基于卫星的光伏电力预测框架，评估了多种模型的性能，发现基于卫星的方法在短时间尺度上表现更优，具有实际应用潜力。
摘要：
我们提出了一种新的框架用于时空光伏（PV）电力预测，并利用该框架评估了七种日内光伏电力现在预测模型的可靠性、锐度和整体性能。该模型套件包括基于卫星的深度学习和光流方法以及基于物理的数值天气预测模型，涵盖了确定性和概率性 formulations。预测首先通过卫星衍生的地表太阳辐照度（SSI）进行验证。辐照度场随后通过站点特定的机器学习模型转换为光伏电力，从而与瑞士6434个光伏站点的生产数据进行比较。据我们所知，这是首次在国家范围内研究时空光伏预测。我们还提供了首次可视化展示中尺度云系统如何在小时和亚小时尺度上影响国家光伏生产。我们的结果表明，基于卫星的方法在短预报时间尺度上优于集成预报系统（IFS-ENS）。其中，SolarSTEPS和SHADECast提供了最准确的SSI和光伏电力预测，SHADECast提供了最可靠的集合散布。确定性模型IrradianceNet实现了最低的均方根误差，而SolarSTEPS和SHADECast的概率预测提供了更好的不确定性校准。预测技能通常随着海拔升高而降低。在国家范围内，基于卫星的模型在2019-2020年间82%的日子里，对日总光伏发电量的相对误差低于10%，证明了其鲁棒性和在实际应用中的潜力。
英文摘要：
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.
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