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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.

Keywords

Root Mean Square Error Genetic Programming ARIMA Model Electrical Energy Consumption Symbolic Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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