Explainable Optimization of Extreme Value Analysis for Photovoltaic Prediction: Introducing Dynamic Correlation Shifts and Weighted Benchmarking

We present an enhanced Extreme Value Analysis (EVA) framework designed to improve the forecasting of extremely low-production events in photovoltaic (PV) systems and to reveal the key inter-variable relationships governing performance under extreme conditions. The proposed Extreme Value Dynamic Benchmarking Method (EVDBM) extends classical EVA by integrating the Dynamic Identification of Significant Correlation (DISC)-thresholding algorithm and explainable AI (XAI) mechanisms, enabling dynamic identification and quantification of correlation shifts during extreme scenarios. Through a combination of grid and Bayesian optimization, EVDBM adaptively fine-tunes variable weights to improve fit, interpretability, and benchmarking consistency. By transforming return values predicted via EVA into dynamic benchmarking scores, EVDBM evolves static tail modeling into a data-driven, explainable benchmarking system capable of identifying critical vulnerabilities and resilience patterns in real time. Applied to real PV production datasets, EVDBM achieved an average improvement of 13.2% in correlation-based Rcorr2 and demonstrated statistically significant reductions in residual error (pt<0.01) in the João dataset, confirming its robustness and generalizability. Quantile-to-quantile analyses further showed improved alignment between modeled and empirical extremes, validating the method’s stability across distributional tails. Ablation studies revealed cumulative gains in interpretability and predictive stability in the EVA → EVDBM → EVDBM + XAI progression, while computational complexity remained near-linear with respect to input dimensionality. Overall, EVDBM delivers a transparent, statistically validated, and operationally interpretable framework for extreme event modeling. Its explainable benchmarking structure supports actionable insights for risk management, infrastructure resilience, and strategic energy planning, establishing EVDBM as a generalizable approach for understanding and managing extremes across diverse application domains.