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Short-term load forecasting system using data mining

  • Jin Liu*
  • , Jilai Yu
  • *Corresponding author for this work
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, by means of data mining techniques, a platform of data warehouse is designed after preprocessing the huge amounts original data of power system, and a system for short term load forecasting (STLF) is developed, in which there is the synthetic technology of both fuzzy clustering and robust regression model in the platform. The useful data excavated from large amounts of data can offer the effective and accurate load forecasting information for reliable and economic operation of power systems. The validity of the designed system for STLF is shown by the simulation results of an actual power system in China.

Original languageEnglish
Title of host publicationProceedings of 2011 17th International Conference on Automation and Computing, ICAC 2011
Pages183-188
Number of pages6
StatePublished - 2011
Event2011 17th International Conference on Automation and Computing, ICAC 2011 - Huddersfield, United Kingdom
Duration: 10 Sep 201110 Sep 2011

Publication series

NameProceedings of 2011 17th International Conference on Automation and Computing, ICAC 2011

Conference

Conference2011 17th International Conference on Automation and Computing, ICAC 2011
Country/TerritoryUnited Kingdom
CityHuddersfield
Period10/09/1110/09/11

Keywords

  • data mining
  • data warehouse
  • fuzzy clustering
  • load forecasting
  • robust regression

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