Genetic algorithm-based RBF neural network load forecasting model

Zhangang Yang, Yanbo Che, Ka Wai Eric Cheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

17 Citations (Scopus)

Abstract

To overcome the limitation of the traditional load forecasting method, a new load forecasting system basing on radial basis Gaussian kernel function (RBF) neural network is proposed in this paper. Genetic algorithm adopting the real coding, crossover probability and mutation probability was applied to optimize the parameters of the neural network, and a faster convergence rate was reached. Theoretical analysis and simulations prove that this load forecasting model is more practical and has more precision than the traditional one.
Original languageEnglish
Title of host publication2007 IEEE Power Engineering Society General Meeting, PES
DOIs
Publication statusPublished - 1 Dec 2007
Event2007 IEEE Power Engineering Society General Meeting, PES - Tampa, FL, United States
Duration: 24 Jun 200728 Jun 2007

Conference

Conference2007 IEEE Power Engineering Society General Meeting, PES
CountryUnited States
CityTampa, FL
Period24/06/0728/06/07

Keywords

  • Convergence rate
  • Genetic algorithm
  • Load forecasting
  • RBF neural network
  • Real coding

ASJC Scopus subject areas

  • Energy(all)

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