• DocumentCode
    2548139
  • Title

    Electricity forecasting for small scale power system using artificial neural network

  • Author

    Khamis, M.F.I. ; Baharudin, Z. ; Hamid, N.H. ; Abdullah, M.F. ; Solahuddin, S.

  • Author_Institution
    Sch. of Electr. & Electron. Eng., Univ. Teknol. PETRONAS, Tronoh, Malaysia
  • fYear
    2011
  • fDate
    6-7 June 2011
  • Firstpage
    54
  • Lastpage
    59
  • Abstract
    Short term load forecasting (STLF) method is the basis of efficient operation for power system. It has an important role in planning and operation of power system. In this paper, a practical STLF using artificial neural network method (ANN) for Gas District Cooling (GDC) power plant of Universiti Teknologi PETRONAS (UTP) is presented. As a sole customer of GDC power plant, the load data from 2006 till 2009 are gathered and utilized for model developments. The developed models forecast electricity load for one week ahead. The paper proposes a method of a multilayer perceptron neural network and it is trained and simulated by using MATLAB. The models have been tested with the actual load data and perform relatively good results.
  • Keywords
    load forecasting; mathematics computing; multilayer perceptrons; power engineering computing; ANN; GDC power plant; Matlab; STLF method; artificial neural network; electricity forecasting; gas district cooling power plant; multilayer perceptron neural network; short term load forecasting method; small scale power system; Artificial neural networks; Data models; Electricity; Load forecasting; Load modeling; Mathematical model; Predictive models; Artificial Neural Network; Electric Power System; Gas District Cooling; Multilayer Perceptron; Short Term Load Forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering and Optimization Conference (PEOCO), 2011 5th International
  • Conference_Location
    Shah Alam, Selangor
  • Print_ISBN
    978-1-4577-0355-3
  • Type

    conf

  • DOI
    10.1109/PEOCO.2011.5970423
  • Filename
    5970423