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  3. A Solar Array Temperature Multivariate Trend Forecasting Method Based On The Ca-patchtst Model.
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  3. A Solar Array Temperature Multivariate Trend Forecasting Method Based On The Ca-patchtst Model.

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A Solar Array Temperature Multivariate Trend Forecasting Method Based on the CA-PatchTST Model.

Yunhai Wang1, Xiaoran Shi1, Zhenxi Zhang1

  • 1Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi'an 710071, China.

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|December 11, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Accurate solar array temperature forecasting is crucial for satellite reliability. A new Cross-Attention Patch Time Series Transformer (CA-PatchTST) method improves multi-step forecasting accuracy, outperforming existing models.

Keywords:
PatchTSTcross-attention mechanismsatellite telemetry datasolar arraytemperature multivariate trend forecasting

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Area of Science:

  • Aerospace Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Satellite system reliability hinges on solar array performance, necessitating accurate temperature forecasting.
  • Current forecasting methods struggle with high-dimensional, long-horizon temperature data exhibiting nonlinear and non-stationary dynamics.
  • Multi-step forecasting is vital for capturing long-term temperature trends and enabling predictive maintenance.

Purpose of the Study:

  • To develop an advanced multivariate trend forecasting method for solar array temperature.
  • To address the challenges of complex temporal dependencies and cross-variable correlations in satellite thermal data.

Main Methods:

  • Proposed a Cross-Attention Patch Time Series Transformer (CA-PatchTST) model for solar array temperature forecasting.
  • Implemented sequence decomposition using a moving average filter to separate trend and residual components.
  • Utilized PatchTST for feature extraction and cross-attention for inter-variable correlation analysis.

    • Main Results:

    • The CA-PatchTST model demonstrated superior performance compared to mainstream baselines.
    • Achieved lower root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).
    • Ablation studies confirmed the effectiveness of sequence decomposition and cross-attention mechanisms.

    Conclusions:

    • The CA-PatchTST method offers a significant advancement in solar array temperature forecasting accuracy.
    • The approach effectively handles complex dynamics and improves predictive maintenance capabilities for satellites.
    • This method enhances the reliability and autonomous power management of space systems.