• DocumentCode
    233471
  • Title

    Dynamic forecasting of electric load consumption using adaptive multilayer perceptron(AMLP)

  • Author

    Lalis, Jeremias T. ; Maravillas, Elmer

  • Author_Institution
    Coll. of Comput. Studies, Cebu Inst. of Technol. - Univ., Cebu City, Philippines
  • fYear
    2014
  • fDate
    12-16 Nov. 2014
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    Electric energy plays a vital role in the achievement of social, economic and environment development of any nation. Thus, efficient demand planning and production of energy is needed to avoid too much over/under-estimation of electric load. In this study, the researchers proposed a scheme with eight steps for a dynamic time series forecasting using adaptive multilayer perceptron with minimal complexity. Two different data sets; each divided into three overlapping parts (training, validating and testing sets), from two different countries were used in the experiments to measure the robustness and accuracy of the models produced by the AMLP. Experiments results show the effectiveness of the proposed scheme for AMLP in forecasting the electric load consumption based on the calculated coefficient of variance of RMSD, CV(RMSD).
  • Keywords
    load forecasting; multilayer perceptrons; power consumption; CV-RMSD; RMSD coefficient of variance; adaptive multilayer perceptron; demand planning; dynamic time series forecasting; electric load consumption; energy production; Artificial neural networks; Data models; Forecasting; Multilayer perceptrons; Predictive models; Time series analysis; Training; adaptive multilayer perceptron; backpropagation; electric load consumption; long-term forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014 International Conference on
  • Conference_Location
    Palawan
  • Print_ISBN
    978-1-4799-4021-9
  • Type

    conf

  • DOI
    10.1109/HNICEM.2014.7016237
  • Filename
    7016237