AI 中文总结
本研究提出FarSky生成式预测框架，结合任务感知隐空间耦合与隐扩散模型，在太阳辐照度预测中实现最优确定性及概率性能，斜坡事件检测F1超60%，预测技能最高提升11个百分点。
AI 中文摘要
准确的太阳辐照度预测对于将光伏电力可靠并入现代电网至关重要。全天成像仪（ASI）提供高分辨率云观测，非常适合小时级内预测。近期深度学习方法大幅提升了预测精度，但通常受限于确定性预测，且预判斜坡事件的能力不足。本研究提出FarSky，一种利用隐空间耦合学习天空图像任务感知表征的生成式预测框架。多任务自动编码器首先学习用于图像重建和辐照度估计的共享隐表征；随后，隐扩散模型基于近期观测生成未来隐状态，可直接解码得到辐照度预测，通过随机采样天然获得概率预测。该框架基于西班牙阿尔梅里亚太阳能平台多年ASI数据集开发，在两个独立测试数据集上，与 persistence、最先进的端到端及生成式预测方法对比评估。FarSky在整体确定性和概率预测性能上表现最佳，预测技能提升最高达11个百分点；此外，其斜坡事件检测性能较现有方法大幅提升，F1分数超60%。这些结果表明，生成式模型与任务感知隐空间耦合结合在太阳能预测中具有应用潜力。
英文摘要
Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids. All-sky imagers (ASI) provide high-resolution observations of clouds, making them well suited for intra-hour forecasting. Recent deep learning approaches have substantially improved forecast accuracy but are often limited by deterministic predictions and a reduced capability to anticipate ramp events. This work proposes FarSky, a generative forecasting framework that leverages latent-space coupling to learn task-aware representations of sky images. A multi-task autoencoder first learns a shared latent representation for image reconstruction and irradiance estimation. A latent diffusion model then generates future latent states conditioned on recent observations, from which irradiance forecasts are directly decoded. Probabilistic forecasts are inherently obtained through stochastic sampling. The framework is developed using a multi-year ASI dataset acquired at the Plataforma Solar de Almería, Spain, and evaluated on two independent test datasets against persistence, state-of-the-art end-to-end, and generative forecasting approaches. FarSky achieves the best overall deterministic and probabilistic forecasting performance, improving forecast skill by up to 11 percentage points. Furthermore, it substantially improves ramp event detection over existing methods, achieving F1-scores above 60%. These results demonstrate the potential of combining generative models with task-aware latent-space coupling for solar forecasting.
