• DocumentCode
    2629219
  • Title

    A novel approach to electrical load forecasting based on a neural network

  • Author

    Srinivasan, Dipti ; Liew, A.C. ; Chen, John S P

  • Author_Institution
    Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
  • fYear
    1991
  • fDate
    18-21 Nov 1991
  • Firstpage
    1172
  • Abstract
    The authors demonstrate how an artificial neural network can be used to forecast electrical load demand. This network is based on the nonstatistical neural paradigm, backpropagation, which is found to be effective for accurate forecasting of electrical load. The major advantage of using an artificial neural network as opposed to other techniques for electrical load forecasting is that the network produces an immediate decision with minimal computation for the given input data, whereas classical techniques require complex mathematical calculations to predict future load values. The performance of the proposed network has been compared to that of some traditional methods of load forecasting and the results have shown the superiority of this approach
  • Keywords
    load forecasting; neural nets; power engineering computing; backpropagation; electrical load forecasting; load demand; neural network; power engineering computing; Biological neural networks; Energy consumption; Fuels; Load forecasting; Neural networks; Power generation planning; Power industry; Power supplies; Power system planning; Reliability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1991. 1991 IEEE International Joint Conference on
  • Print_ISBN
    0-7803-0227-3
  • Type

    conf

  • DOI
    10.1109/IJCNN.1991.170555
  • Filename
    170555