Bridging AI Large Meteorological Models and Solar Irradiance Forecasting Through Machine Learning Approaches

Renewable energy sources have an increasingly pivotal role in global electricity generation, which poses challenges to the accurate and efficient meteorological forecasting (such as solar irradiance and hub-height wind speed). The development of AI large models has significantly shortened the time required for medium-range global weather forecast. However, their outputs typically lack high-temporal-resolution solar irradiance (e.g., provided only at 6-hour intervals or not at all), which cannot be directly applied to renewable energy forecasting.

In this work, we propose a machine learning framework to integrate the output variables from AI large models with high-resolution solar irradiance forecasting. Specifically, we train XGBoost models at 15 sites in eastern China using ERA5 reanalysis variables (2020–2023) as inputs and hourly surface solar irradiance derived from Himawari-8/9 satellite as targets. The trained models are evaluated on a 2024 test set driven by ERA5, achieving an annual mean hourly RMSE of 88.5 W m^-2.

To assess the performance of this approach in medium range forecasting, we use hourly forecasts from the GDAS-driven Pangu Weather Model during January and July 2024 as inputs. Over 20 medium-range forecast tests, our approach (Pangu-ML) yields a day-ahead (24-h lead) RMSE of 62.5 (January) /95.4 (July) W m^-2 and a 10-day lead RMSE of 92.3 (January) /110.1 (July) W m^-2. For comparison, we conduct parallel simulations using the GFS-driven WRF v4.6 model at 9-km resolution over eastern China. The WRF-based irradiance forecasts produce day-ahead and 10-day RMSEs of 78.4 (January) /107.6 (July) W m^-2 and 109.8 (January) /130.3 (July) W m^-2 across the 15 sites, demonstrating that Pangu-ML achieves comparable or even superior accuracy.

In summary, our approach takes advantage of the computational efficiency of AI large meteorological models. It enables rapid generation of solar irradiance forecasts with minimal computational cost, thereby offering a practical pathway for subsequent operational ensemble irradiance forecasting.