@inproceedings{GoeddeMayerZinneretal.2019,
  author    = {Felix G{\"o}dde and Bernhard Mayer and Tobias Zinner and Jonas Witthuhn and Hartwig Deneke and Christopher Schirrmeister},
  title     = {Predicting variability of horizontal surface solar irradiance using machine learning},
  series = {EMS Annual Meeting Abstracts},
  volume    = {16},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-49959},
  year      = {2019},
  abstract  = {Renewable energies play an increasingly important role for energy production in Europe. Unlike coal or gas powerplants, solar energy production is highly variable in space and time. This is due to the strong variability of cloudsand their influence on the surface solar irradiance. Especially in regions with large contribution from photovoltaicpower production, the intermittent energy feed-in to the power grid can be a risk for grid stability. Therefore goodforecasts of temporal and spatial variability of surface irradiance are necessary to be able to properly regulate thepower supply.},
  language  = {en}
}