###### Abstract  
The penetration of photovoltaic (PV) energy has gained a significant increase in recent years because of its sustainable and clean characteristics. However, the uncertainty of PV power affected by variable weather poses challenges to an accurate short-term prediction, which is crucial for reliable power system operation. Existing methods focus on coupling satellite images with ground measurements to extract features using deep neural networks. However, a flexible predictive framework capable of handling these two data structures is still not well developed. The spatial and temporal features are merely concatenated and passed to the following layer of a neural network, which is incapable of utilizing the correlation between them. Therefore, we propose a novel dual-encoder transformer (DualET) for short-term PV power prediction. The dual encoders contain wavelet transform and series decomposition blocks to extract informative features from image and sequence data, respectively. Moreover, we propose a cross-domain attention module to learn the correlation between the temporal features and cloud information and modify the attention modules with the spare form and Fourier transform to improve their performance. The experiments on real-world datasets, including PV station data and satellite images, show that our model achieves better results than other models for short-term PV power prediction. transformer; photovoltaic power forecasting; satellite images; deep learning +
Footnote †: journal: Medical  
motion of clouds [10; 11] recorded in consecutive cloud images. In light of this, ground-based or satellite remote-sensing images are valuable data sources to support accurate short-term PV power prediction. Several approaches employ ground-based/sky images for the prediction of irradiance [12; 13] and PV power [14]. With sky images, the cloud motion vector (CMV) methods that can calculate the speed and direction of clouds have been developed to improve the irradiance prediction accuracy [15; 16]. These methods have a considerably decreasing value beyond a 30 min future horizon and are thus commonly used in ultra-short-term PV power prediction [17]. Moreover, ground-based images are limited in observation range and require equipment installation. In contrast, satellite images offer top-down detection with greater spatial coverage, as well as advantages for prediction up to a few hours ahead [18]. They are not only suitable for CMV methods [11; 19; 20; 21] but also able to provide other valuable information. [PERSON] et al. [22] cluster the interval gray value of satellite images to construct a relationship with PV power. [PERSON] et al. [23] propose a CNN-based fluctuation pattern prediction model with satellite images as input and apply LSTMs to individually forecast different patterns of PV power. Besides these methods that adopt only satellite images, researchers are increasingly interested in coupling satellite data with ground measurements to improve forecasting performance [24]. [PERSON] et al. [25] extracted cloud cover factor from satellite images by the modified CNN and then combined it with meteorological information for irradiance prediction. [PERSON] et al. [26] presented a spatiotemporal model with Lasso to integrate multi-source data for 6-h-ahead prediction. Furthermore, they used the proposed model to assess the impact of each source on forecasting performance, which showed that satellite images improved the accuracy by 3%. [PERSON] et al. [27] proposed an encoder-decoder model, which includes U-Net and LSTM to extract the spatial features from satellite short-wave radiation data and temporal features from meteorological sequences, respectively. Then, the spatial and temporal features were concatenated for intra-hour PV power prediction. However, a flexible predictive framework capable of handling data sources with different structures (i.e., image and sequence) is still not well discussed.