to analyze the long-term characteristics of solar array output current [9]. [PERSON] et al. predicted array degradation under the premise of normalizing the illumination angle and power temperature coefficient [10]; [PERSON] et al. established a theoretical model including solar radiation, shadow, thermal characteristics, and output power for the problem of solar array shadowing by balloons in stratospheric balloons [11]. [PERSON] et al. proposed a modeling environment for low-earth orbit (LEO) spacecraft solar arrays (SSA), constructing a mathematical model including irradiance, temperature, and energy balance [12]. [PERSON] et al. considered power loss under shadow conditions [13]. In recent years, machine learning and deep learning technologies have shown significant potential in the field of energy forecasting. For example, scholars such as [PERSON] achieved high-precision prediction of solar panel power output based on artificial neural network (ANN) and random forest (RF) models [14]; [PERSON] et al. confirmed the effectiveness of current features in anomaly detection of satellite solar arrays through standardized diagnostic methods [15]. However, most existing studies focus on long-term steady-state forecasting, with insufficient adaptability to model short-term fluctuations. Although random forests have the ability to analyze feature importance, they have limitations in modeling temporal dependencies [16]; while autoregressive integrated moving average models (ARIMA) are suitable for linear time series forecasting, they struggle to capture dynamic nonlinear fluctuations [17]. Traditional prediction models mostly rely on environmental parameters (such as temperature and irradiance). For example, [PERSON] et al. used BP neural networks to predict solar array current, but the prediction error was large (0.1 A) due to the lack of consideration of the temporal characteristics of load current [18]; another study by [PERSON] et al. reported an average relative error of 3% in predicting the long-term degradation of solar arrays [19]. In contrast, as the basic model of neural networks, FFN (Feedforward Neural Network) realizes complex function approximation through multi-layer nonlinear transformations; RNN (Recurrent Neural Network) enables the model to capture the temporal dependency in sequences by introducing a \"recurrent\" structure; Long Short-Term Memory (LSTM) stands out in capturing long-term dependencies in time series by virtue of its gating mechanism [20]. These neural network models provide new paths for the short-period fluctuation prediction of solar array current. [PERSON] et al. analyzed the thermoelectric coupling under parallel mismatch of triple-junction GaAs solar cells for satellites and provided suggestions for the series-parallel design of arrays to prevent parallel mismatch [21]. However, they did not analyze the small-amplitude fluctuations in the output current of the battery array under thermoelectric coupling conditions. [PERSON] et al. [22] proposed a new hybrid deep learning method that integrates the complementary advantages of Recurrent Neural Networks (RNNs), Transformer models, and Long Short-Term Memory (LSTM) networks, aiming to improve the accuracy of solar energy prediction. By combining serial processing with a multi-head self-attention mechanism, this model can capture complex dependencies across different time scales, thereby enhancing the performance of time series prediction. It is worth noting that this study did not include load current as a feature factor; instead, it focused on modeling relevant influencing factors such as weather conditions. The model was validated through experiments under diverse and complex weather scenarios, including sunny, cloudy, and rainy days. From the above survey, it can be seen that current research lacks a systematic analysis of the short-term current fluctuation characteristics of load-fluctuating satellites (such as communication satellites, reconnaissance satellites, navigation satellites, etc.).