考虑缺失数据不确定性，提升光伏短期预测可信度
Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach
- 用多重随机插补+鲁宾规则融合缺失数据不确定性
- 忽略该不确定性会使预测区间过窄，校准性差
- 适用于任何机器学习模型，适合电力系统研究者
光伏功率数据中缺失值普遍存在，但其引发的不确定性未被纳入预测分布。本文提出一种框架，通过结合随机多重插补与鲁宾规则，将缺失数据不确定性融入短期光伏预测。该方法模型无关，可与标准机器学习预测器集成。实验表明，忽略缺失数据不确定性会导致预测区间过于狭窄。考虑该不确定性后，区间校准性显著改善，同时保持相近的点预测精度。结果凸显了在数据驱动的光伏预测中传播插补不确定性的关键作用。
原文摘要 · Abstract (English)
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.
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