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@INPROCEEDINGS{Hassanian:1007354,
      author       = {Hassanian, Reza and Helgadottir, Asdis and Aach, Marcel and
                      Lintermann, Andreas and Riedel, Morris},
      title        = {{A} proposed hybrid two-stage {DL}-{HPC} method for wind
                      speed forecasting: using the first average forecast output
                      for long-term forecasting},
      reportid     = {FZJ-2023-02023},
      pages        = {-},
      year         = {2023},
      abstract     = {Energy consumption is growing extensively, which is caused
                      by new demanding technological applications and continuously
                      changing lifestyles, also with respect to climate change.
                      Climate change is a significant issue and scientific reports
                      notice the temperature environment continuously increasing,
                      particularly in the summer. To alleviate the heat, people in
                      many countries tend to use air conditioning systems in
                      residential and business buildings. This puts additional
                      pressure on the electricity network and the energy producers
                      must be able to predict such events. It is agreed worldwide
                      that harvesting renewable energy is the best option for
                      fighting climate change. For example, recently, the number
                      of electric cars has increased and it becomes more and more
                      attractive to utilize green energy, e.g., produced by wind
                      turbines, for them. The advantages of wind energy have
                      intensively been studied, and a wide range of methods to
                      create very short-term, short-term, medium-term, and
                      long-term predictions using wind energy models or wind speed
                      profiles are in use [1,2]. However, some of the forecasting
                      methods are highly complex and costly in computing [3,4].
                      This study uses a gated recurrent unit (GRU) model, a deep
                      learning model, to efficiently perform medium-term
                      predictions of wind energy production. There is effort to
                      apply these medium-term predictions to create long-term
                      forecasting models. The literature has reported that GRUs
                      are faster than long short-term memory (LSTM) models, which
                      have been used in recent studies, can deal with relatively
                      fewer data, and are cheaper in computing. The study applies
                      empirical wind speed data from 5 years, which the Iceland
                      Metrological office has measured at 10 m height at the
                      Búfrell location. The log law is used to scale the speed up
                      to 55 m, which is the height of an Enercon E44 wind turbine
                      hub. The predictions are performed on the DAM module of the
                      DEEP cluster at the Jülich Supercomputing Centre. The
                      parallel machine allows to speed up the model scaling. The
                      results show that the proposed model can predict medium and
                      long-term wind speeds as a function of the ratio of training
                      data. This method conducts the forecasting cheaper in
                      computing than LSTM but with equal performance.},
      month         = {Apr},
      date          = {2023-04-25},
      organization  = {Proceedings of the IACM Computational
                       Fluids Conference (CFC2023), Cannes
                       (France), 25 Apr 2023 - 28 Apr 2023},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / RAISE - Research on
                      AI- and Simulation-Based Engineering at Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/1007354},
}