A hybrid Monte Carlo quantile EMD-LSTM method for satellite in-orbit temperature prediction and data uncertainty quantification

Satellite in-orbit temperature prediction is essential in satellite reliability evaluation and health management. However, the non-stationary property of the time-series temperature data complicates forecasting, adversely affecting prediction accuracy. Moreover, conventional point estimation, which overlooks the pervasive data uncertainty due to sensor noises, diminishes the model's predictive credibility. To address these problems, this paper proposes a hybrid Monte Carlo quantile based empirical mode decomposition employing the Long Short-Term Memory method for temperature prediction and quantifying data uncertainty. The empirical mode decomposition method is adopted for tackling the non-stationary feature of satellite temperature data, greatly improving the prediction accuracy. The Monte Carlo quantile regression method is integrated with the Long Short-Term Memory network to quantify the data uncertainty, deeply enhancing the temperature prediction credibility. Two practical engineering cases are applied to validate the proposed method and address practical engineering problems. Results demonstrate that the proposed method not only provides accurate temperature predictions in comparison to other methods but also offers a reliable confidence interval at a given confidence level, facilitating timely judgments of abnormal satellite conditions.