Missing values are common in photovoltaic (PV) power data, yet the uncertainty they induce is not propagated into predictive distributions. We develop a framework that incorporates missing-data uncertainty into short-term PV forecasting by combining stochastic multiple imputation with Rubin's rule. The approach is model-agnostic and can be integrated with standard machine-learning predictors. Empirical results show that ignoring missing-data uncertainty leads to overly narrow prediction intervals. Accounting for this uncertainty improves interval calibration while maintaining comparable point prediction accuracy. These results demonstrate the importance of propagating imputation uncertainty in data-driven PV forecasting.

翻译：光伏发电数据中普遍存在缺失值，然而这些缺失值所引发的不确定性并未被传递至预测分布中。本文通过将随机多重插补与Rubin规则相结合，构建了一个将缺失数据不确定性纳入光伏短期预测的框架。该方法具有模型无关性，可与标准的机器学习预测器集成。实证结果表明，忽略缺失数据不确定性会导致预测区间过度狭窄。考虑这种不确定性可在保持可比点预测精度的同时改善区间校准效果。这些结果证明了在数据驱动的光伏预测中传递插补不确定性的重要性。