Abstract. Accurate prediction of TEC can significantly improve the accuracy of navigation and positioning, therefore TEC observation and prediction has become a hot spot in ionospheric research. TEC has the characteristics of nonlinearity and non-stationarity, that cannot accurately describe this change by analytic expressions. Through the analysis of TEC content changes at the same time for several consecutive days in different seasons, it can be concluded that the TEC change at the same time in a short period is relatively stable, the overall monotonous change trend has a certain correlation. Since the grey model performs better in the prediction of a small amount of data and has a high accuracy in the prediction of time series with monotonous changes, it is used in the prediction of the same time and point-to-point short-term prediction of TEC. The accuracy of the grey model is verified by the Posterior variance ratio, the Little error probability test and the relation grade. The residual correction is made for the prediction results with low prediction accuracy, by further establishing the GM (1,1) model of residual values, and the original prediction results being compensated and refined by the residual GM (1,1) model. The experimental results show that the improved model is more accurate than the grey prediction model and can reflect the changing characteristics of ionospheric TEC.
Facility Power Usage Modeling and Short Term Prediction with Artificial Neural Networks
