基于数据驱动方法的在轨卫星智能温度预测

Aiming at the problem of poor prediction accuracy and robustness of traditional satellite temperature forecasting methods, which are difficult to meet the demand for high-dimensional coupled data forecasting, a multivariate time series data forecasting model for satellite temperature telemetry data—advanced time series processing module (ATSPM)-Net is proposed. Firstly, an ATSPM consisting of one-dimensional convolution and gated recurrent unit(GRU) is constructed to extract temporal dependencies from highly coupled telemetry data at multiple scales. Next, a multivariate temporal data forecasting model ATSPM-Net is designed. By stacking ATSPM, ATSPM-Net ensures the flexible receptive field of the model, thereby achieving high accuracy and robustness in telemetry data forecasting. Finally, numerical experiments conducted on five datasets showed that compared to other types of time series data forecasting models, ATSPM-Net can demonstrate better temperature forecasting performance with fewer parameters.