High-resolution photovoltaic power forecasting using machine learning models under seasonal and stress conditions

Abstract

Accurate short-term photovoltaic (PV) power forecasting at sub-hourly resolutions is essential for maintaining grid reliability and supporting renewable energy integration. This study benchmarks six machine learning models against a persistence baseline using high-resolution data from the Yulara Solar System (327.6 kW) resampled at 5-, 10-, and 15-minute intervals. A comprehensive preprocessing framework was developed, incorporating cyclical temporal encoding, lag and rolling statistical features, interquartile-range-based outlier filtering, and hyperparameter optimization using RandomizedSearchCV with TimeSeriesSplit. The evaluated models include XGBoost, Decision Tree, Linear Regression, Ridge, Lasso, and Elastic Net. Performance was assessed using multiple error metrics (MAE, RMSE, WAPE, SMAPE, and skill score) across training, validation, testing, seasonal subsets, and stress scenarios involving night-time and highly variable irradiance conditions. Results demonstrate that tree-based models substantially outperform linear regressors in both accuracy and robustness. The Decision Tree achieved the best overall performance, with MAE values of 0.33-0.54 kW and RMSE values of 0.82-1.65 kW, corresponding to 97-98% skill-score improvements over persistence forecasting. XGBoost also showed strong and consistent performance across temporal resolutions and seasonal variations. In contrast, linear models exhibited limited capability in capturing nonlinear PV behavior, particularly under highly variable conditions. These findings highlight the suitability of tree-based approaches for accurate, interpretable, and resilient short-term PV forecasting in smart grid applications.