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Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference2012, Article number 62815022012 IEEE PES Transmission and Distribution Conference and Exposition, T and D 2012; Orlando, FL; United States; 7 May 2012 through 10 May 2012; Code 93493

A comparative analysis of SVM and ANN based hybrid model for short term load forecasting(Conference Paper)

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  • aTelvent DMS, Serbia
  • bFTN, Novi Sad, Serbia

Abstract

This paper represents comparison of two artificial intelligence based hybrid models for short term load forecasting (STLF). Models have the same input/output architecture and are built on SVM and ANN technologies, respectively. Algorithm consists of two modules connected in a sequence, and output from first module is connected as additional input to second module. First module acts as a predictor of maximal load of forecasting day and second acts as hourly load predictor. Models are part of large STLF solution and in respect to computational and memory limitations simple input space is designed. This architecture enables short training time which is targeted for frequent re-training needs in modern utilities due to frequent change in customer number and behavior. © 2012 IEEE.

Author keywords

Artificial neural networksdemand forecastingsupport vector machines

Indexed keywords

Engineering uncontrolled termsComparative analysisDemand forecastingHourly loadHybrid modelInput spaceInput/outputShort term load forecastingTraining time
Engineering controlled terms:ExhibitionsNeural networks
Engineering main heading:Support vector machines
  • ISSN: 21608555
  • ISBN: 978-146731934-8
  • Source Type: Conference Proceeding
  • Original language: English
  • DOI: 10.1109/TDC.2012.6281502
  • Document Type: Conference Paper

  Selakov, A.; Telvent DMS, Serbia
© Copyright 2013 Elsevier B.V., All rights reserved.

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