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With the increase of solar photovoltaic (PV) permeability in power systems, high-precision PV power forecasting is critical to secure the economic operation of the smart grid. Solar irradiance is the most important factor affecting PV power. Accurately estimating the amount of solar irradiance leads to better production estimates from solar panels. Meanwhile, satellite images provide information on current and future cloud coverage, and are potentially useful in inferring solar irradiance. To accurately capture the effect of cloud on irradiance, this article develops a real-time mapping model between satellite image and solar irradiance. Based on the mapping model, a new ultrashort-term global horizontal irradiance (GHI) forecasting method combining satellite visible images and meteorological information is proposed. First, a modified method is utilized for reducing the influence of the solar zenith angle on pixel intensity and extracting the features in the image processing stage. Then, the cloud cover factors are extracted from satellite visible images by using the modified convolutional neural network. After that, the GHI forecasting model is developed, which is based on the combined use of meteorological information and cloud cover factors. Meanwhile, a cloud motion forecasting method using predicted wind speeds is developed for discussion. The better performance of the proposed method is demonstrated by comparing it with several benchmark models. [ABSTRACT FROM AUTHOR]

• A satellite image-based photovoltaic power forecasting model is proposed. • A nonlinear cloud movement forecasting method based on deep learning is proposed. • An algorithm for intra-hour cloud condition estimation is established. • An algorithm for recognizing the cloud region that blocks sunlight is designed. The rapid variation of clouds is the main factor that causes the fluctuation of photovoltaic power. 1 1 This work was supported by the National Key Research and Development Program of China under Grant 2019YFE0118400. The satellite images contain plenty of information about clouds, applicable for photovoltaic power forecast. However, in practice, two main factors obstruct the application of the satellite images: 1) the relatively low update frequency of the satellite images mismatches the photovoltaic power forecasting frequency, and 2) the cloud region that blocks the sunlight changes significantly with time. In this paper, a novel satellite image-based approach for photovoltaic power forecast is proposed to overcome these obstacles and achieve accurate forecasting results. Firstly, concerning the hourly updated satellite images, a nonlinear cloud movement forecasting model, considering the thickness and shape changes of the cloud, is presented to forecast the hourly variation of the images. Secondly, an active cloud region selection rule is derived based on the changing solar position to dynamically select the cloud region that blocks the concerned photovoltaic power station in a satellite image. Thirdly, a sequential cloud region selection algorithm is provided to estimate the intra-hour variation of the cloud to match the photovoltaic power forecasting frequency. Finally, the photovoltaic power is predicted using the XGBoost algorithm concerning the effects of the cloud and other influencing factors. Testing results show that the proposed method can achieve more accurate photovoltaic power forecasts using the low update frequency satellite images. Meanwhile, the superior performance compared with other benchmarks also verifies the effectiveness of considering cloud information obtained by the proposed method for photovoltaic power forecast. [ABSTRACT FROM AUTHOR]