• DocumentCode
    917803
  • Title

    Neural network based short term load forecasting

  • Author

    Lu, C.N. ; Wu, H.-T. ; Vemuri, S.

  • Author_Institution
    Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kahosiung, Taiwan
  • Volume
    8
  • Issue
    1
  • fYear
    1993
  • fDate
    2/1/1993 12:00:00 AM
  • Firstpage
    336
  • Lastpage
    342
  • Abstract
    The artificial neural network (ANN) technique for short-term load forecasting (STLF) has been proposed previously. In order to evaluate ANNs as a viable technique for STLF, one has to evaluate the performance of ANN methodology for practical considerations of STLF problems. The authors make an attempt to address these issues. The results of a study to investigate whether the ANN model is system dependent, and/or case dependent, are presented. Data from two utilities are used in modeling and forecasting. In addition, the effectiveness of a next 24 h ANN model in predicting 24 h load profile at one time was compared with the traditional next 1 h ANN model
  • Keywords
    load forecasting; neural nets; power engineering computing; 24 h; artificial neural network; load profile; short term load forecasting; Artificial neural networks; Economic forecasting; Load forecasting; Neural networks; Power generation economics; Power system economics; Power system modeling; Power system reliability; Predictive models; Testing;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/59.221223
  • Filename
    221223