Estimation of the Electricity Demand of La Palma Island (Spain)

  • Begoña GonzálezEmail author
  • Antonio Pulido
  • Miguel Martínez
  • Gabriel Winter
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 36)


Historical data of electricity demand in La Palma island (Spain) were collected and electricity demand estimates conducted by different organizations were sought. Some factors that could affect these data were studied and its predictions by the next years were looked for. The idea was to use these factors as explanatory variables in order to predict the values of electricity demand in the next years. Moreover, with the aim of minimizing the limitation of predicting the future based only on relationships between variables that occurred in the past, it has been considered the annual demand forecast for various scenarios, taking into account, for each of them, different variations of the explanatory variables. All that with the goal that the estimate band of the demand for each year includes the real future demand with high probability. This provided a prediction model that takes into account population and gross domestic product. Results and their graphical representation along with the other estimates found are presented. A similar approach was carried out to predict peak powers.


Prediction model Electric demand Peak powers Flexible evolution algorithm Robust design optimization 


  1. 1.
    Beyer HG, Sendhoff B (2007) Robust optimization a comprehensive survey. Comput Meth Appl Mech Engrg 196:3190–3218CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Ghods L, Kalantar M (2011) Different methods of long-term electric load demand forecasting; a comprehensive review. Iran J Electr Electron Eng 7(4):249–259Google Scholar
  3. 3.
    NZIER (2009) Review of electricity demand forecast model. Report commissioned by the New Zealand electricity commission.
  4. 4.
  5. 5.
    Winter G et al (2005) Flexible evolutionary algorithms: cooperation and competition among real-coded evolutionary operators. Soft Comp 9(4):299–323CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Begoña González
    • 1
    Email author
  • Antonio Pulido
    • 1
  • Miguel Martínez
    • 1
  • Gabriel Winter
    • 1
  1. 1.University Institute of Computational Engineering (SIANI), Evolutionary Computation and Applications (CEANI)Universidad de Las Palmas de Gran CanariaLas Palmas de GCSpain

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