Abstract The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable.

    Highlights Novel deep neural network (N-BEATS) applied to 27-day forecasting of F10.7 solar flux. Estimate of forecast uncertainty provided through deep ensembles. Systematic comparison with statistical and neural network based operational forecasts. Improved forecast accuracy and uncertainty quantification learning only from F10.7.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A deep learning approach to solar radio flux forecasting



    Published in:

    Acta Astronautica ; 193 ; 595-606


    Publication date :

    2021-08-02


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Load Forecasting Using Deep Learning

    Akhil Srinivas, T.V. / Subash, Ashwath / Amutha, A.L. | IEEE | 2021


    Solar flux forecasting using mutual information with an optimal delay

    Ashrafi, S. / Conway, D. / Rokni, M. et al. | NTRS | 1993


    FORECASTING AIRCRAFT MILES FLOWN TIME SERIES USING A DEEP LEARNING-BASED HYBRID APPROACH

    Victor Sineglazov / Olena Chumachenko / Vladyslav Gorbatiuk | DOAJ | 2018

    Free access

    A new approach to solar cycle forecasting

    Strong, Keith T. | Online Contents | 2009