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The global warming effect has been accelerating rapidly and poses a threat to human survival and health. The top priority to solve this problem is to provide reliable renewable energy. To achieve this goal, it is important to provide fast and accurate solar radiation predictions based on limited observation data. In this study, a fast and accurate solar radiation nowcasting method is proposed by combining FY-4A satellite data and the McClear clear sky model under the condition of only radiation observation. The results show that the random forest (RF) performed better than the support vector regression (SVR) model and the reference model (Clim-Pers), with the smallest normalized root mean square error (nRMSE) values (between 13.90% and 33.80%), smallest normalized mean absolute error (nMAE) values (between 7.50% and 24.77%), smallest normalized mean bias error (nMBE) values (between −1.17% and 0.7%) and highest R2 values (between 0.76 and 0.95) under different time horizons. In addition, it can be summarized that remote sensing data can significantly improve the radiation forecasting performance and can effectively guarantee the stability of radiation predictions when the time horizon exceeds 60 min. Furthermore, to obtain the optimal operation efficiency, the prediction results were interpreted by introducing the latest SHapley Additive exPlanation (SHAP) method. From the interpretation results, we selected the three key channels of an FY-4A and then made the model lightweight. Compared with the original input model, the new one predicted the results more rapidly. For instance, the lightweight parameter input model needed only 0.3084 s (compared to 0.5591 s for full parameter input) per single data point on average for the 10 min global solar radiation forecast in Yuzhong. Meanwhile, the prediction effect also remained stable and reliable. Overall, the new method showed its advantages in radiation prediction under the condition that only solar radiation observations were available. This is very important for radiation prediction in cities with scarce meteorological observation, and it can provide a reference for the location planning of photovoltaic power stations. [ABSTRACT FROM AUTHOR]

An algorithm to forecast very short-term (30–180 min) surface solar irradiance using visible and near infrared channels (AGRI) onboard the FengYun-4A (FY-4A) geostationary satellite was constructed and evaluated in this study. The forecasting products include global horizontal irradiance (GHI) and direct normal irradiance (DNI). The forecast results were validated using data from Chengde Meteorological Observatory for four typical months (October 2018, and January, April, and July 2019), representing the four seasons. Particle Image Velocimetry (PIV) was employed to calculate the cloud motion vector (CMV) field from the satellite images. The forecast results were compared with the smart persistence (SP) model. A seasonal study showed that July and April forecasting is more difficult than during October and January. For GHI forecasting, the algorithm outperformed the SP model for all forecasting horizons and all seasons, with the best result being produced in October; the skill score was greater than 20%. For DNI, the algorithm outperformed the SP model in July and October, with skill scores of about 12% and 11%, respectively. Annual performances were evaluated; the results show that the normalized root mean square error (nRMSE) value of GHI for 30–180 min horizon ranged from 26.78 to 36.84%, the skill score reached a maximum of 20.44% at the 30-min horizon, and the skill scores were all above 0 for all time horizons. For DNI, the maximum skill score was 6.62% at the 180-min horizon. Overall, compared with the SP model, the proposed algorithm is more accurate and reliable for GHI forecasting and slightly better for DNI forecasting. [ABSTRACT FROM AUTHOR]