Prediticting global solar irradiance using satellite images












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I have the aim to build a model to predict global horizontal irradiance (ghi) using satellite images and other features namely the day of the year and time of the day.
For extracting the satellite images features, I aim at using one of the available network in keras (e.g. Xception). Then I can concatenate the features output from the CNN to the other features (I already made the modifications to keras to handle those requirements.
Now, essentially, ghi is also a time-series problem so I assumed using time-series techniques could help. But I don't know where to look for those architectures (CNN + Time series + other features) so :



given my problem and desires, do you have ressources to help me tackle this issue?



one thing that is not yet clear to me is if I have don't freeze Xception internal layers, are the fiters parameters automatically learned by the model? I read here and there that filters are optimized also but I never found in keras a clear statement about that.










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    $begingroup$


    I have the aim to build a model to predict global horizontal irradiance (ghi) using satellite images and other features namely the day of the year and time of the day.
    For extracting the satellite images features, I aim at using one of the available network in keras (e.g. Xception). Then I can concatenate the features output from the CNN to the other features (I already made the modifications to keras to handle those requirements.
    Now, essentially, ghi is also a time-series problem so I assumed using time-series techniques could help. But I don't know where to look for those architectures (CNN + Time series + other features) so :



    given my problem and desires, do you have ressources to help me tackle this issue?



    one thing that is not yet clear to me is if I have don't freeze Xception internal layers, are the fiters parameters automatically learned by the model? I read here and there that filters are optimized also but I never found in keras a clear statement about that.










    share|improve this question







    New contributor




    FenryrMKIII is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







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      1





      $begingroup$


      I have the aim to build a model to predict global horizontal irradiance (ghi) using satellite images and other features namely the day of the year and time of the day.
      For extracting the satellite images features, I aim at using one of the available network in keras (e.g. Xception). Then I can concatenate the features output from the CNN to the other features (I already made the modifications to keras to handle those requirements.
      Now, essentially, ghi is also a time-series problem so I assumed using time-series techniques could help. But I don't know where to look for those architectures (CNN + Time series + other features) so :



      given my problem and desires, do you have ressources to help me tackle this issue?



      one thing that is not yet clear to me is if I have don't freeze Xception internal layers, are the fiters parameters automatically learned by the model? I read here and there that filters are optimized also but I never found in keras a clear statement about that.










      share|improve this question







      New contributor




      FenryrMKIII is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I have the aim to build a model to predict global horizontal irradiance (ghi) using satellite images and other features namely the day of the year and time of the day.
      For extracting the satellite images features, I aim at using one of the available network in keras (e.g. Xception). Then I can concatenate the features output from the CNN to the other features (I already made the modifications to keras to handle those requirements.
      Now, essentially, ghi is also a time-series problem so I assumed using time-series techniques could help. But I don't know where to look for those architectures (CNN + Time series + other features) so :



      given my problem and desires, do you have ressources to help me tackle this issue?



      one thing that is not yet clear to me is if I have don't freeze Xception internal layers, are the fiters parameters automatically learned by the model? I read here and there that filters are optimized also but I never found in keras a clear statement about that.







      regression lstm cnn






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      FenryrMKIII is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







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      asked 5 hours ago









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