Using a Self Organizing Map Neural Network for Short-Term Load Forecasting, Analysis of Different Input Data Patterns

  • C. Senabre
  • S. Valero
  • J. Aparicio
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 79)


This research uses a Self-Organizing Map neural network model (SOM) as a short-term forecasting method. The objective is to obtain the demand curve of certain hours of the next day. In order to validate the model, an error index is assigned through the comparison of the results with the real known curves. This index is the Mean Absolute Percentage Error (MAPE), which measures the accuracy of fitted time series and forecasts. The pattern of input data and training parameters are being chosen in order to get the best results. The investigation is still in course and the authors are proving different patterns of input data to analyze the different results that they will be obtained with each one. Summing up, this research tries to establish a tool that helps the decision making process, forecasting the short-term global electric load demand curve.


Self-Organizing Maps Short-Term Load Forecasting 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • C. Senabre
    • 1
  • S. Valero
    • 1
  • J. Aparicio
    • 2
  1. 1.Department of Industrial Systems Engineering. E.P.S.E.Universidad Miguel Hernández de Elche Campus of Elche (Quorum-V Building)ElcheSpain
  2. 2.Operational Research CenterUniversidad Miguel Hernández de ElcheElcheSpain

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