Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data

  • Oliver Flasch
  • Martina Friese
  • Katya Vladislavleva
  • Thomas Bartz-Beielstein
  • Olaf Mersmann
  • Boris Naujoks
  • Jörg Stork
  • Martin Zaefferer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7835)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Oliver Flasch
    • 1
  • Martina Friese
    • 1
  • Katya Vladislavleva
    • 1
  • Thomas Bartz-Beielstein
    • 1
  • Olaf Mersmann
    • 1
  • Boris Naujoks
    • 1
  • Jörg Stork
    • 1
  • Martin Zaefferer
    • 1
  1. 1.Fakultät für Informatik und IngenieurwissenschaftenGummersbachGermany

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