Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Flasch:evoapps13,
  author =       "Oliver Flasch and Martina Friese and 
                 Katya Vladislavleva and Thomas Bartz-Beielstein and 
                 Olaf Mersmann and Boris Naujoks and Joerg Stork and 
                 Martin Zaefferer",
  title =        "Comparing Ensemble-Based Forecasting Methods for
                 Smart-Metering Data",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "172--181",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_18",
  size =         "10 pages",
  abstract =     "This work provides a preliminary study on applying
                 state-of-the-art time-series forecasting methods to
                 electrical energy consumption data recorded by smart
                 metering equipment. We compare a custom-build
                 commercial baseline method to modern ensemble-based
                 methods from statistical time-series analysis and to a
                 modern commercial GP system. Our preliminary results
                 indicate that that modern ensemble-based methods, as
                 well as GP, are an attractive alternative to
                 custom-built approaches for electrical energy
                 consumption forecasting",
  notes =        "http://www.kevinsim.co.uk/evostar2013/cfpEvoApplications.html
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",
}

Genetic Programming entries for Oliver Flasch Martina Friese Ekaterina (Katya) Vladislavleva Thomas Bartz-Beielstein Olaf Mersmann Boris Naujoks Joerg Stork Martin Zaefferer

Citations