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Accurate solar radiation estimation is very important for solar energy systems and is a precondition of solar energy utilization. Due to the rapid development of new energy sources, the demand for surface solar radiation estimation and observation has grown. Due to the scarcity of surface radiation observations, high-precision remote sensing data are trying to fill this gap. In this paper, a global solar irradiance estimation method (in different months, seasons, and weather conditions), using data from the advanced geosynchronous radiation imager (AGRI) sensor onboard the FengYun-4A satellite with cloud index methodology (CSD-SI), was tested. It was found that the FengYun-4A satellite data could be used to calculate the clear sky index through the Heliosat-2 method. Combined with McClear, the global horizontal irradiance (GHI) and the direct normal irradiance (DNI) in northeast China could be accurately obtained. The estimated GHI accuracy under clear sky was slightly affected by the seasons and the normalized root mean square error (nRMSE) values (in four sites) were higher in summer and autumn (including all weather conditions). Compared to the estimated GHI, the estimated DNI was less accurate. It was found that the estimated DNI in October had the best performance. In the meantime, the nRMSE, the normalized mean absolute error (nMAE), and the normalized mean bias error (nMBE) of Zhangbei were 35.152%, 27.145%, and −8.283%, while for Chengde, they were 43.150%, 28.822%, and −13.017%, respectively. In addition, the estimated DNI at ground level was significantly higher than the actual observed value in autumn and winter. Considering that the error mainly came from the overestimation of McClear, a new DNI radiation algorithm during autumn and winter is proposed for northern China. After applying the new algorithm, the nRMSE decreased from 49.324% to 48.226% for Chengde and from 48.342% to 41.631% for Zhangbei. Similarly, the nMBE decreased from −32.351% to −18.823% for Zhangbei and from −26.211% to −9.107% for Chengde. [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]