% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Hassanian:1037647,
      author       = {Hassanian, Reza and Shahinfar, Abdollah and Helgadóttir,
                      Ásdís and Riedel, Morris},
      title        = {{O}ptimizing {W}ind {E}nergy {P}roduction: {L}everaging
                      {D}eep {L}earning {M}odels {I}nformed with {O}n-{S}ite
                      {D}ata and {A}ssessing {S}calability through {HPC}},
      journal      = {Acta polytechnica hungarica},
      volume       = {21},
      number       = {9},
      issn         = {1785-8860},
      address      = {Budapest},
      reportid     = {FZJ-2025-00812},
      pages        = {45 - 56},
      year         = {2024},
      abstract     = {This study suggests employing a deep learning model trained
                      on on-site windspeed measurements to enhance predictions for
                      future wind speeds. The model uses a gatedrecurrent unit
                      (GRU) derived from the long short-term memory (LSTM)
                      variant, and istrained using actual measured wind velocity
                      data collected at both 10-minute and hourlyintervals. The
                      approach relies on using same-season data for predicting
                      wind velocity,necessitating regular updates to the model
                      with recent measurements to ensure accuratepredictions in a
                      timely manner.The results from the prediction model,
                      particularly at a 10-minute interval, demonstrate
                      asignificant alignment with the actual data during
                      validation. Comparative analysis of theemployed model over a
                      two-year span, with a 24-year distinction, indicates its
                      efficiencyacross different time periods and seasonal
                      conditions, contingent upon frequent updateswith recent
                      on-site wind velocity data.Given the reliance of sequential
                      deep learning models on extensive data for enhancedaccuracy,
                      this study emphasizes the importance of employing
                      high-performance computing(HPC). As a recommendation, the
                      study proposes equipping the wind farm or wind farmcluster
                      with an HPC machine powered by the wind farm itself, thereby
                      transforming it intoa sustainable green energy resource for
                      the HPC application. The recommended approachin this work is
                      enforcing the smart power grid to respond to the power
                      demand that isconnected to predictable wind farm
                      production.},
      cin          = {JSC},
      ddc          = {600},
      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) /
                      EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733 /
                      G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.12700/APH.21.9.2024.9.4},
      url          = {https://juser.fz-juelich.de/record/1037647},
}