摘要
提出一种由基于最优尺度量化的分类主成分分析数据处理模块和优化Transformer时序预测模块组成的卫星电源消耗预测方法.针对卫星工程数据的高冗余问题,建立了基于赫斯特指数分析(Hurst)、灰色关联分析以及分类主成分分析(CATPCA)的卫星高维数据处理模型,对百维度时序数据进行有效提取,重构输入数据.采用对抗学习网络架构,建立多学习Transformer的卫星电量预测模型,模型综合考虑影响卫星能源消耗的多种因素以及时序数据依赖,可以在较短的时间内完成高精度卫星电源消耗时序预测.实验部分采用卫星真实运行数据,综合考虑影响卫星能源消耗的多种因素,12 h预测拟合优度达到94%,比BP神经网络,长短期记忆网络(LSTM)精度更高.可以有效克服常规工程数据的冗余、缺失以及脏数据问题,解决了常规时序预测需要依赖长期数据的不足缺陷,有效完成卫星能源短时消耗高精度预测.这对卫星在轨任务规划、卫星在轨健康管理等后续任务提供可靠支持.
Taking a categorical principal components analysis data processing module based on optimal scale quantization and an optimized Transformer time series forecast module as main module,a satellite power consumption prediction method was proposed.Aiming at the high redundancy problem of satellite engineering data,a satellite high-dimensional data processing model based on Hurst index analysis,grey relational analysis and categorical principal component analysis(CATPCA)was established to effectively extract hundred-dimensional time series data and reconstruct the input data.In addition,the adversarial learning network architecture was used to establish a satellite power prediction model of multi-learning Transformer.The model was designed to comprehensively consider various affecting factors on satellite energy consumption and time series data dependencies,and to complete high-precision satellite power consumption time series prediction in a short period of time.In the experiment part,adopting the real operation data of satellite and comprehensively considering various factors that affect satellite energy consumption,the fitting accuracy of proposed method can reach up to 94%with 12h prediction,which is higher than that of BP neural network and long short-term memory network(LSTM).The results show that the method can effectively overcome the problems of redundancy,lack and dirty data of conventional engineering data,solve the deficiency that conventional time series prediction needs to rely on long-term data,effectively complete the high-precision prediction of satellite energy consumption in a short time.This provides reliable support for satellite on-orbit mission planning,satellite on-orbit health management and other follow-up tasks,and assists decision-making.
作者
张璋
常亮
田明华
邓雷
常建平
董亮
ZHANG Zhang;CHANG Liang;TIAN Minghua;DENG Lei;CHANG Jianping;DONG Liang(Innovation Academy for Microsatellites of CAS,Shanghai 201203,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shanghai Engineering Center for Microsatellites,Shanghai 201203,China)
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2023年第7期744-754,共11页
Transactions of Beijing Institute of Technology
基金
上海市青年科技英才扬帆计划(19YF1446200)。
关键词
时序预测
Transformer时序
分类主成分分析
深度学习
卫星电源预测
time series forecast
Transformer time series
categorical principal component analysis(CAT-PCA)
deep learning
satellite power forecast
