@inproceedings{ZangerSchulzGrodmeieretal.2024,
  author    = {Zanger, Michael and Schulz, Alexander and Grodmeier, Lukas and Agaj, Dion and Schindler, Rafael and Weiss, Lukas and M{\"o}hring, Michael},
  title     = {HollerithEnergyML : a prototype of a machine learning energy consumption recommender system},
  booktitle = {Informatik 2024: Lock-in or log out? Wie digitale Souver{\"a}nit{\"a}t gelingt, 24.-26. September 2024, Wiesbaden, proceedings (Lecture Notes in Informatics; vol. 352)},
  editor    = {Klein, Maike and Krupka, Daniel and Winter, Cornelia and Gergeleit, Martin and Martin, Ludger},
  isbn      = {978-3-88579-746-3},
  issn      = {1617-5468},
  doi       = {10.18420/inf2024_132},
  institution = {Informatik},
  pages     = {1519 -- 1523},
  year      = {2024},
  abstract  = {Energy consumption aspects of machine learning classifiers are important for research and practice as well. Due to sparse research in this area, a prototype of a recommender system was developed to provide energy consumption recommendations of different possible classifiers. The prototype is demonstrated as well as discussed and future research points are derived.},
  language  = {en}
}