• DocumentCode
    2089090
  • Title

    Forecasting peak loads with neural networks

  • Author

    Garcia, Luis F. ; Mohammed, Osama A.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA
  • fYear
    1994
  • fDate
    10-13 Apr 1994
  • Firstpage
    351
  • Lastpage
    356
  • Abstract
    This paper presents a new approach to power load forecasting using artificial neural networks (ANN). Based on weather conditions and past history of load consumption, a load forecast is made by the utility companies to deliver the appropriate load to its customers. Power systems operation and planning functions such as unit commitment, security analysis, state estimation, etc. are benefited with an accurate load forecast. Improving the accuracy of the load forecast can save a significant amount of money. Artificial neural networks permit adaptability to climate changes compared to other forecasting methods in use. The results obtained by using ANN have been found to give better results than other conventional techniques
  • Keywords
    load forecasting; neural nets; power system analysis computing; artificial neural networks; climate changes; load consumption; peak loads forecasting; power load forecasting; power systems operation; power systems planning; security analysis; state estimation; unit commitment; weather conditions; Artificial neural networks; Character generation; Load forecasting; Neural networks; Power engineering and energy; Power engineering computing; Power system planning; Power system security; Test pattern generators; Weather forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Southeastcon '94. Creative Technology Transfer - A Global Affair., Proceedings of the 1994 IEEE
  • Conference_Location
    Miami, FL
  • Print_ISBN
    0-7803-1797-1
  • Type

    conf

  • DOI
    10.1109/SECON.1994.324334
  • Filename
    324334