一种基于CA-PatchTST模型的太阳能阵列温度多变量趋势预测方法
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.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
准确的太阳能阵列温度预测对于卫星的可靠性至关重要. 一种新的交叉注意力补丁时间序列变压器 (CA-PatchTST) 方法提高了多步预测的准确性,优于现有模型.
科学领域:
- 航空航天工程 航空航天工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 卫星系统的可靠性取决于太阳能阵列的性能,需要准确的温度预测.
- 目前的预测方法在与高维,长视界温度数据 (显示非线性和非静止动态) 进行斗争.
- 多步预测对于捕捉长期温度趋势和实现预测性维护至关重要.
研究的目的:
- 为太阳能阵列温度开发一种先进的多变量趋势预测方法.
- 解决卫星热数据中复杂的时间依赖性和交叉变量相关性的挑战.
主要方法:
- 为太阳能阵列温度预测提出了一个交叉注意力补丁时间序列变压器 (CA-PatchTST) 模型.
- 使用移动平均线过器实现序列分解,以分离趋势和剩余组件.
- 使用PatchTST进行特征提取和交叉注意力进行变量间相关性分析.
主要成果:
- 与主流基线相比,CA-PatchTST模型显示出更高的性能.
- 实现了较低的根平均平方误差 (RMSE),平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE).
- 废除研究证实了序列分解和交叉注意力机制的有效性.
结论:
- 在太阳能阵列温度预测准确度方面,CA-PatchTST方法提供了显著的进步.
- 该方法有效地处理复杂的动态,并提高卫星的预测性维护能力.
- 这种方法提高了空间系统的可靠性和自主电源管理.
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