The movement of the cloud causes strong fluctuations in solar irradiance, which in turn causes the randomness and volatility of photovoltaic power generation, and has a serious impact on safe and stable operation of the power system. Aiming at the above problems, the B-Informer combined interval prediction method based on cloud features of ground-based cloud images is proposed in this paper. Firstly, image processing technology is used to obtain cloud features of cloud images that affect solar irradiance, including the percentage of cloud with correction coefficient, and RGB value of optical flow cloud image. Secondly, the cloud features and historical meteorological data are combined to form the input sequence features, and the informer prediction model based on sparse attention mechanism is constructed. And then, the Bootstrap method is used to increase the sample diversity and to generate the prediction interval, which further improves the prediction accuracy of the model for long time series. Finally, the historical operation data and ground-based cloud images of a power station in Colorado are taken as an example, compared the prediction results with LUBE and other existing methods at a given confidence level. The mean prediction interval width of the proposed method is reduced by up to 27%, which verifies the effectiveness.