Abstract With the introduction of national policies, photovoltaic (PV) power forecasting requirements for PV power plants are becoming increasingly stringent. It is particularly critical that PV power predictions are accurate while new energy is being consumed. It is also important to consider the satellite imagery of the location of the PV power plant and the meteorological information of the plant itself. The authors aim to explore the impact of these two elements on PV power prediction to better support PV power prediction. Therefore, this paper explores the cloud information elements of the satellite images from a multi‐view perspective and performs feature extraction and processing of the meteorological information to learn the impact of cloud cover on PV power prediction. Meanwhile, this paper introduces the mutual information mechanism for the influence of meteorological factors on PV power generation. It constructs the mutual information matrix and adopts the graph neural network for representation learning. A time‐series prediction model for short‐term PV power prediction is constructed and more accurate prediction results are obtained. The experimental results demonstrate that the proposed method is effective, has generalisation ability, and improved performance compared with the traditional model. The proposed method can also provide a novel approach and solution for short‐term PV power prediction.

Abstract Clean energy is a major trend. The importance of photovoltaic power generation is also growing. Photovoltaic power generation is mainly affected by the weather. It is full of uncertainties. Previous work has relied chiefly on historical photovoltaics data for time series forecasts. However, unforeseen weather conditions can sometimes skew. Consequently, a spatial‐temporal‐meteorological‐long short‐term memory prediction model (STM‐LSTM) is proposed to compensate for the shortage of photovoltaic prediction models for uncertainties. This model can simultaneously process satellite image data, historical meteorological data, and historical power generation data. In this way, historical patterns and meteorological change information are extracted to improve the accuracy of photovoltaic prediction. STM‐LSTM processes raw satellite data to obtain cloud image data. It can extract cloud motion information using the dense optical flow method. First, the cloud images are processed to extract cloud position information. By adaptive attentive learning of images in different bands, a better representation for subsequent tasks can be obtained. Second, it is important to process historical meteorological data to learn meteorological change patterns. Last but not least, the historical photovoltaic power generation sequences are combined to obtain the final photovoltaic prediction results. After a series of experimental validation, the performance of the proposed STM‐LSTM model has a good improvement compared with the baseline model.