Photovoltaic (PV) power forecasting in edge-enabled grids requires balancing forecasting accuracy, robustness under weather-driven distribution shifts, and strict latency constraints. Existing models work well under normal conditions but often struggle with rare ramp events and unexpected weather changes. Relying solely on cloud-based large models often leads to significant communication delays, which can hinder timely and efficient forecasting in practical grid environments. To address these issues, we propose a condition-adaptive cloud-edge collaborative framework *CAPE* for PV forecasting. *CAPE* consists of three main modules: a site-specific expert model for routine predictions, a lightweight edge-side model for enhanced local inference, and a cloud-based large retrieval model that provides relevant historical cases when needed. These modules are coordinated by a screening module that evaluates uncertainty, out-of-distribution risk, weather mutations, and model disagreement. Furthermore, we employ a Lyapunov-guided routing strategy to dynamically determine when to escalate inference to more powerful models under long-term system constraints. The final forecast is produced through adaptive fusion of the selected model outputs. Experiments on two real-world PV datasets demonstrate that *CAPE* achieves superior performance in terms of forecasting accuracy, robustness, routing quality, and system efficiency.

翻译：光伏（PV）功率预测在边缘赋能电网中需平衡预测精度、天气驱动分布偏移下的鲁棒性以及严格的延迟约束。现有模型在正常运行条件下表现良好，但常难以应对罕见的爬坡事件和突发的天气变化。仅依赖基于云的大模型往往导致显著的通信延迟，从而在实际电网环境中妨碍及时高效的预测。为解决这些问题，我们提出了一种面向光伏预测的条件自适应云边协作框架*CAPE*。*CAPE*包含三个主要模块：用于常规预测的站点专属专家模型、用于增强本地推理的轻量级边缘侧模型，以及按需提供相关历史案例的基于云的检索大模型。这些模块由一个筛选模块协调，该模块评估不确定性、分布外风险、天气突变及模型分歧。此外，我们采用基于李雅普诺夫（Lyapunov）引导的路由策略，在长期系统约束下动态决定何时将推理升级至更强模型。最终预测通过对所选模型输出的自适应融合生成。在两个真实光伏数据集上的实验表明，*CAPE*在预测精度、鲁棒性、路由质量及系统效率方面均实现了优越性能。