Nowcasting of Surface Solar Irradiance Based on Cloud Optical Thickness from GOES-16

Surface solar irradiance (SSI) is a critical factor influencing the power generation capacity of photovoltaic (PV) power plants. Dynamic changes in cloud cover pose significant challenges to the accurate nowcasting of SSI, which in turn directly affects the reliability and stability of renewable energy systems. However, existing research often simplifies or overlooks changes in the optical and morphological characteristics of clouds, leading to considerable errors in SSI nowcasting. To address this limitation and improve the accuracy of ultra-short-term SSI forecasting, this study first forecasts changes in cloud optical thickness (COT) within the next 3 h based on a spatiotemporal long short-term memory model, since COT is the primary factor determining cloud shading effects, and then integrates the zenith and regional averages of COT, along with factors influencing direct solar radiation and scattered radiation, to achieve precise SSI nowcasting. To validate the proposed method, we apply it to the Albuquerque, New Mexico, United States (ABQ) site, where it yielded promising performance, with correlations between predicted and actual surface solar irradiance for the next 1 h, 2 h, and 3 h reaching 0.94, 0.92, and 0.92, respectively. The proposed method effectively captures the temporal trends and spatial patterns of cloud changes, avoiding simplifications of cloud movement trends or interference from non-cloud factors, thus providing a basis for power adjustments in solar power plants.