Under complex weather conditions, weather fluctuations are significant, and traditional solar radiation prediction models used in photovoltaic power stations cannot effectively handle complex nonlinear relationships. This inaccuracy poses challenges for the protection and grid connection safety of power systems. To address this challenge, a solar radiation prediction model based on ANFIS (adaptive-network-based fuzzy inference systems) was established. This model incorporates satellite remote sensing data as input to complement traditional meteorological data. Firstly, the sample entropy calculation method was used to determine complex weather conditions. Secondly, ARIMA(autoregressive integrated moving average) model was adopted to predict the optical thickness of cloud cover and aerosols, two critical satellite remote sensing data points, for the next 24 hours. Combining solar radiation and temperature data from the upper atmosphere, an ANFIS-based solar radiation prediction model was established to obtain solar radiation predictions for the next 24 hours. In a case study, the accuracy of the ANFIS model was compared to that of BP (back propagation) neural network prediction model and LSTM (long short-term memory) neural network prediction model under different weather conditions. The results show that under simple weather conditions, the root mean square errors of the ANFIS, BP, and LSTM models are 0.112 2, 0.318 4, and 0.253 4 W/m², respectively, which are relatively small and not significantly different. However, under complex weather conditions, the root mean square error of the ANFIS model is 0.860 6 W/m², which is 4.039 6 W/m² and 2.025 2 W/m² lower than that of the BP and LSTM models, indicating that the ANFIS model performs better under complex weather conditions and can adapt to data with strong volatility. The study also shows that considering satellite remote sensing data in addition to meteorological data can reduce the predicted root mean square error by 0.132 W/m², further improving prediction accuracy.