%Aigaion2 BibTeX export van HES SO Valais Publications
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@ARTICLE{,
    author = {Weibel, Amine and jordan, Nicolas and Wannier, David},
  keywords = {machine learning, modeling, optimization, Simulation, VOLTAGE PREDICTION},
     month = sep,
     title = {A data augmentation methodology for machine learning modelling of distribution power grid: Application on optimal storage sizing and control},
   journal = {CIRED 2021},
      year = {2021},
     pages = {5},
       url = {https://www.cired2021.org/},
  abstract = {The growth of distributed energy generations and electric vehicle charging stations in the low voltage grid brings out new
challenges for distribution system operators. Actual methods of distribution network modelling are computationally expensive
and omit aging of network physical components. This paper proposes a method of data augmentation for data-driven modelling.
The methodology is divided into four parts: data generation, data-driven modelling, power flow boundaries estimation, and
finally the application on optimal energy storage sizing and control.}
}