• DocumentCode
    428567
  • Title

    ANN based load forecasting: a parallel structure

  • Author

    Hu, Chang ; Cao, Li

  • Author_Institution
    Dept. of Autom., Tsinghua Univ., Beijing, China
  • Volume
    4
  • fYear
    2004
  • fDate
    10-13 Oct. 2004
  • Firstpage
    3594
  • Abstract
    Air-conditioning system plays an important role in modern architectures, whereas load forecasting provides a more efficient and accurate way to control air-conditioning systems. In this article, a parallel structure called 2-ANN for air-conditioner load forecasting is proposed. 2-ANN combines two independent predictors by a corrector. For forecasting, first the predictors make separate predictions, and then their predictions are compared and revised by the corrector to form a single output. 2-ANN structure is easy to analysis as well as easy to implement. In implementation, the predictors and corrector are back-propagation (BP) neural networks. The parallel structure, set up with three BP networks, is trained and tested by real-world data of air-conditioner load. In those tests, 2-ANN outperforms each one of the two predictors alone.
  • Keywords
    air conditioning; backpropagation; load forecasting; neural nets; power engineering computing; air-conditioning system; artificial neural networks; back-propagation neural networks; independent predictors; load forecasting; parallel structure; Artificial neural networks; Automation; Control systems; Feedback loop; Load forecasting; Modems; Neural networks; Predictive models; Recurrent neural networks; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2004 IEEE International Conference on
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-8566-7
  • Type

    conf

  • DOI
    10.1109/ICSMC.2004.1400900
  • Filename
    1400900