Volume 39 Issue 2
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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2010  >  39(2): 316-320

CHEN Qi-song, CHEN Xiao-wei, ZHANG Xin, WU Mao-nian. Optimal Support Vector Machine Model for Boiler Load Forecasting[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 316-320. doi: 10.3969/j.issn.1001-0548.2010.02.035
Citation: CHEN Qi-song, CHEN Xiao-wei, ZHANG Xin, WU Mao-nian. Optimal Support Vector Machine Model for Boiler Load Forecasting[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 316-320. doi: 10.3969/j.issn.1001-0548.2010.02.035
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Optimal Support Vector Machine Model for Boiler Load Forecasting

doi: 10.3969/j.issn.1001-0548.2010.02.035
  • 1.

    School of Computer Science and Technology,Guizhou University Guiyang 550025;

  • 2.

    Guizhou Key Laboratory for Photoelectric Technology and Application Guiyang 550025

  • Received Date: 2008-10-06
  • Rev Recd Date: 2009-05-12
  • Publish Date: 2010-04-15
  • Intelligently optimal support vector machine (SVM) were introduced in electric utility boiler to improve short-term load forecasting accuracy and generalization ability. Wavelet transform is adopted to filter noise in training and testing data set. Kernel principle component analysis is used in feature selection. Then quantum-behaved particle swarm algorithm is chosen to determinate optimal hyper-parameter in SVM. This optimal algorithm has been tested on power plant and the results show that the prediction can get higher precision and convergence speed.
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  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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Optimal Support Vector Machine Model for Boiler Load Forecasting

doi: 10.3969/j.issn.1001-0548.2010.02.035
  • 1. School of Computer Science and Technology,Guizhou University Guiyang 550025;
  • 2. Guizhou Key Laboratory for Photoelectric Technology and Application Guiyang 550025
  • Received Date: 2008-10-06
  • Rev Recd Date: 2009-05-12
  • Publish Date: 2010-04-15

Abstract: Intelligently optimal support vector machine (SVM) were introduced in electric utility boiler to improve short-term load forecasting accuracy and generalization ability. Wavelet transform is adopted to filter noise in training and testing data set. Kernel principle component analysis is used in feature selection. Then quantum-behaved particle swarm algorithm is chosen to determinate optimal hyper-parameter in SVM. This optimal algorithm has been tested on power plant and the results show that the prediction can get higher precision and convergence speed.

CHEN Qi-song, CHEN Xiao-wei, ZHANG Xin, WU Mao-nian. Optimal Support Vector Machine Model for Boiler Load Forecasting[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 316-320. doi: 10.3969/j.issn.1001-0548.2010.02.035
Citation: CHEN Qi-song, CHEN Xiao-wei, ZHANG Xin, WU Mao-nian. Optimal Support Vector Machine Model for Boiler Load Forecasting[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(2): 316-320. doi: 10.3969/j.issn.1001-0548.2010.02.035
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