AI-Augmented Clear-Sky Algorithms for High-Granularity Solar Irradiance Modelling

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Chinese explanation / 中文解读

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Original abstract

Accurate short-term solar irradiance forecasting is important for reliable photovoltaic operation in regions affected by rapid cloud movement and atmospheric variability. However, clear-sky models cannot adequately respond to cloud-driven irradiance fluctuations, while purely data-driven models may suffer from weak physical consistency and reduced generalisability. The present study developed a physics-informed hybrid framework in which clear-sky irradiance was used as the physical baseline, while machine learning was employed to learn the residual atmospheric deviations caused by clouds, aerosols, moisture and near-surface meteorology. The proposed model was evaluated against persistence, clear-sky, AI-only and standalone Gated Recurrent Unit benchmark models using 10-min-ahead global horizontal irradiance prediction. During the Roodepoort summer test period, the hybrid model achieved an RMSE of 79 W/m2, MAE of 59 W/m2, MBE of −8 W/m2 and R2 of 0.91, corresponding to RMSE reductions of 32%, 44%, 27% and 18% when compared with the persistence, clear-sky, AI-only and Gated Recurrent Unit models, respectively. Furthermore, the model maintained seasonal RMSE values between 79 and 96 W/m2 without retraining, retained useful transferability at external Southern African Universities Radiometric Network stations with a mean external RMSE of 95.5 W/m2 and R2 of 0.86, and produced calibrated 95% prediction intervals with 93% empirical coverage. These findings showed that clear-sky constrained residual learning can improve deterministic accuracy, uncertainty reliability and seasonal robustness, thus supporting more reliable photovoltaic forecasting, grid integration and operational energy management under variable sky conditions.

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