Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach

2026-03-16

AI Summary

This work proposes a model-agnostic framework that systematically incorporates uncertainty from missing values in photovoltaic (PV) power data into data-driven short-term forecasting. By leveraging stochastic multiple imputation combined with Rubin’s rules, the approach explicitly models and propagates missing-data uncertainty into the predictive distribution—a critical aspect often overlooked in existing methods. The framework effectively mitigates the overconfidence commonly observed in prediction intervals that fail to account for such uncertainty, thereby yielding substantially better-calibrated interval forecasts. Notably, this improvement in calibration is achieved without compromising the accuracy of point predictions, demonstrating a robust balance between reliability and precision in PV forecasting under incomplete data conditions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

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.

Problem

• predictive uncertainty
• missing data
• PV forecasting
• prediction intervals
• imputation uncertainty

Innovation

• multiple imputation
• predictive uncertainty
• PV forecasting
• missing data
• interval calibration