Hybrid intelligent method of relevant vector machine and regression tree for probabilistic load forecasting

Hiroyuki Mori, A. Takahashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

This paper proposes a new hybrid intelligent method for probabilistic short-term load forecasting (STLF) in power systems. It consists of Relevance Vector Machine (RVM) of the statistical learning method called Kernel Machine and regression tree (RT) of data mining. As the preconditioned technique of data, RT is used to classify learning data into some clusters with the data similarity. After classifying data into some clusters, RVM is constructed to predict one-step ahead loads at each cluster. RVM is one of efficient Kernel Machines that extend Support Vector Machine (SVM) to deal with continuous variables. It has advantage to narrow the lower and upper bounds of predicted values with high accuracy. The proposed method is successfully applied to real data of Japanese utilities.

Original languageEnglish
Title of host publication2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, ISGT Europe 2011
DOIs
Publication statusPublished - 1 Dec 2011
Event2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, ISGT Europe 2011 - Manchester, United Kingdom
Duration: 5 Dec 20117 Dec 2011

Publication series

NameIEEE PES Innovative Smart Grid Technologies Conference Europe

Conference

Conference2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, ISGT Europe 2011
CountryUnited Kingdom
CityManchester
Period5/12/117/12/11

Keywords

  • Bayesian Inference
  • Data Mining
  • Error Analysis
  • Kernel Machine
  • Load Forecasting
  • Regression Tree
  • Statistical Learning
  • Uncertainty

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  • Cite this

    Mori, H., & Takahashi, A. (2011). Hybrid intelligent method of relevant vector machine and regression tree for probabilistic load forecasting. In 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, ISGT Europe 2011 [6162721] (IEEE PES Innovative Smart Grid Technologies Conference Europe). https://doi.org/10.1109/ISGTEurope.2011.6162721