一种基于STL-Prophet-Informer模型的太阳电池阵多变量趋势预测方法

A Multivariable Trend Prediction Method for Solar Array Based on STL-Prophet-Informer Model

为了提高太阳电池阵多变量预测的精度,解决阳电池阵遥测参数存在周期波动与增长性互相耦合的问题,提出一种基于STL-Prophet-Informer模型的太阳电池阵多变量预测算法.该算法首先应用局部加权周期趋势分解算法(seasonal and trend decomposition procedure based on loess,STL)对太阳电池阵的多个参数分解为趋势分量、周期分量和残差分量,然后采用对趋势性数据预测效果较好的Prophet预测趋势分量,Informer模型预测周期分量和残差分量,最后将各分量预测结果相加后得到总的太阳电池阵参数预测值.以某卫星太阳电池阵实际遥测数据做算例分析,提出算法的各项误差评价指标和单一的Informer模型、LSTM模型等相比有明显减小,将该组合预测模型用于太阳电池阵多变量参数预测中,可以提高参数预测精度,提升卫星自主运行性能.

In order to improve the accuracy of multivariable prediction of solar array and solve cyclical volatility and growth of telemetry parameters of solar array couple with each other,a multivariable prediction algorithm of solar array based on STL-Prophet-Informer model was proposed.The algorithm firstly uses the seasonal and trend decomposition procedure based on loess to decompose multiple parameters of the solar array into trend components,periodic components and residual components.Then Prophet is used to predict the trend component,and Informer model is used to predict the periodic component and residual component.Finally,the predicted values of the total solar array parameters are obtained by adding the predicted results of each component.Taking the actual telemetry data of a satellite solar array as an example,this paper proposes that the various error evaluation indexes of the algorithm are significantly reduced compared with the single Informer model and LSTM model,etc.Applying the combined prediction model to the multivariable parameter prediction of the satellite battery array can improve the accuracy of parameter prediction and improve the autonomous operation performance of the satellite.