Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach
This addresses a domain-specific issue for PV forecasting practitioners by providing a method to handle missing data uncertainty, though it is incremental as it builds on existing imputation techniques.
The paper tackled the problem of missing data in photovoltaic (PV) power forecasting by developing a framework that incorporates missing-data uncertainty into predictive distributions, showing that accounting for this uncertainty improves interval calibration while maintaining point prediction accuracy.
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.