Toward Sustainable Photovoltaic Operations: Evaluating Validation Strategies for Inverter Fault Prediction Under Sparse-Event Conditions

This study evaluates how validation design affects the assessment of photovoltaic (PV) inverter fault prediction under sparse operational event conditions. Using an 89-day dataset from 18 co-located inverters at a single PV plant, minute-level SCADA measurements were transformed into 56-step input windows with 15 min future event labels. Three validation configurations were compared under the same XGBoost-based forecasting task: single-equipment temporal validation, pooled temporal validation, and leave-one-equipment-out (LOEO) validation. The results show that the three configurations provide different interpretations of predictive usefulness. Single-equipment validation achieved a mean PR-AUC of 0.699, pooled temporal validation achieved a PR-AUC of 0.637, and LOEO validation achieved a mean PR-AUC of 0.718 with a mean ROC-AUC of 0.930. Bootstrap confidence intervals confirmed that held-out equipment performance estimates were statistically more stable than in extremely sparse short-window settings; for example, held-out equipment 4 achieved a PR-AUC of 0.734 with a 95% confidence interval of 0.704–0.762. Variable-level permutation importance showed that predictive performance was mainly associated with DC-side voltage/current and selected AC-side electrical variables. These findings demonstrate that validation design is not a secondary implementation detail but a substantive methodological choice in PV predictive-maintenance evaluation. The study provides practical guidance for selecting validation strategies according to deployment scenarios, including asset-specific modeling, shared plant-level prediction, and predictive coverage for unseen or data-limited inverters.