% 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{Rttgers:911992,
      author       = {Rüttgers, Mario and Lee, Sangseung and Shin, Heesoo},
      title        = {{N}eural {N}etworks for {I}mproving wind {P}ower
                      {E}fficiency: {A} {R}eview},
      journal      = {Fluids},
      volume       = {7},
      issn         = {2311-5521},
      address      = {Belgrade},
      publisher    = {MDPI},
      reportid     = {FZJ-2022-05222},
      pages        = {12},
      year         = {2022},
      abstract     = {The demand for wind energy harvesting has grown
                      significantly to mitigate the global challenges of climate
                      change, energy security, and zero carbon emissions. Various
                      methods tomaximize wind power efficiency have been proposed.
                      Notably, neural networks have shown large potential in
                      improving wind power efficiency. In this paper, we provide a
                      review of attempts tomaximize wind power efficiency using
                      neural networks. A total of three neural-network-based
                      strategies are covered: (i) neural-network-based turbine
                      control, (ii) neural-network-based wind farmcontrol, and
                      (iii) neural-network-based wind turbine blade design. In the
                      first topic, we introduce neural networks that control the
                      yaw of wind turbines based on wind prediction. Second, we
                      discussneural networks for improving the energy efficiency
                      of wind farms. Last, we review neural networks to design
                      turbine blades with superior aerodynamic performances.},
      cin          = {JSC},
      ddc          = {530},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000902649500001},
      doi          = {10.3390/fluids7120367},
      url          = {https://juser.fz-juelich.de/record/911992},
}