## 5 Results and Discussion  
This study constructs a research system comprising the following components based on multi-source telemetry data (including load current, time-phase, etc.):  
Multi-algorithm modeling: LSTM, RNN, ARIMA, and FNN models are established, with feature sequences containing load current as inputs, and hyperparameters are optimized through cross-validation.  
Comparison and validation: Using RMSE, MAE, and MSE as evaluation metrics, the performance of algorithms on the test set is compared, and the Adaptability of LSTM is verified through actual cases (such as load mutation scenarios). The study further applies this feature set to predict the solar array output current of an on-orbit MEO (Medium Earth Orbit) satellite. Results show that the prediction accuracy of the LSTM algorithm significantly outperforms other models.  
For satellites with normal solar array drives, an on-orbit MEO satellite, \"Star A\", is selected. A total of 746,860 data points are used, with the first 80% serving as the training set and the remaining 20% as the test set. Predictions are performed using LSTM, RNN, ARIMA, and FNN, respectively. The comparison of prediction results is shown in Figures 9 and 10 below:  
Figure 9: Comparison of model predictions for Satellite A.  
The comparison of evaluation metrics for the four algorithms is shown in Table 9.  
The loss function curves of the three models are shown in Figure 11 below.  
It can be seen that the prediction results of the LSTM algorithm are better than those of the other three algorithms.  
Satellite \"Star B\" is selected. A total of 921,897 data points are used, with the first 80% designated as the training set and the remaining 20% as the test set. LSTM, RNN, ARIVAL, and FNN are, respectively, used for prediction. The comparison of prediction results is shown in Figures 12 and 13 below, and the comparison of evaluation indicators of the four algorithms is shown in Table 10 below.  
Due to the characteristics of the telemetry transmission mechanism of on-orbit satellites, the distribution of actual satellite telemetry data points is not uniform. When the satellite is within the domestic coverage area, its telemetry data is transmitted at a frequency of 1 s per packet; when the satellite is outside the domestic coverage area, the frequency of telemetry frame data transmitted via inter-satellite links is reduced to 1 min per packet. Additionally, influenced by factors such as mission planning, satellite telemetry data may exhibit periodic invisibility. These transmission characteristics directly result in the non-uniform distribution of the time series of the solar array current output in Figure 10:  
Domestic phase: Data sampling is dense, and the current curve shows a smooth semi-sinusoidal shape with a well-defined arc.  
Overseas phase: Due to the reduced telemetry update frequency, the sinusoidal spikes in the current curve decay more rapidly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LSTM** & **RNN** & **FFN** & **ARIVA** \\ \hline MSE & 0.001056 & 0.001674 & 0.003699 & 0.0082 \\ RMSE & 0.032498 & 0.040913 & 0.060818 & 0.0904 \\ MAE & 0.024334 & 0.031002 & 0.047959 & 0.0734 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of model prediction metrics on Satellite B.  
Figure 12: Comparison of model predictions on Satellite B.  
Figure 13: Comparison of model predictions on Satellite B (first 200 data points).  
The prediction model proposed in this study effectively adapts to complex scenarios with uneven telemetry updates by introducing time phase as an input variable. In contrast, the telemetry data of Satellite A lacks significant orbital periodic characteristics, so its prediction model does not require time phase input and only needs load current as the sole characteristic factor--when the load changes, the solar array output current fluctuates correspondingly.  
It can be seen that the prediction results of the LSTM algorithm are better than those of the other three algorithms.