A Neural Network Model for Energy Consumption Prediction of CIESOL Bioclimatic Building

  • Rafael Mena Yedra
  • Francisco Rodríguez Díaz
  • María del Mar Castilla Nieto
  • Manuel R. Arahal
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)


Energy efficiency in buildings is a topic that is being widely studied. In order to achieve energy efficiency it is necessary to perform both, a proper management of the electric demand, and an optimal exploitation of renewable sources, using for that appropriate control strategies. The main objective of this paper is to develop a short term predictive model, based on neural networks, of the electricity demand for the CIESOL research center. The performed experiments, using different techniques for weather forecast, show a quick prediction with acceptable final results for real data, obtaining a maximum root mean squared error of 5 % in validation data, with a short-term prediction horizon of 60 minutes.


Electric demand prediction Predictive model Neural network 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rafael Mena Yedra
    • 1
  • Francisco Rodríguez Díaz
    • 1
  • María del Mar Castilla Nieto
    • 1
  • Manuel R. Arahal
    • 2
  1. 1.ceiA3 CIESOL, Joint Center University of Almería - CIEMATUniversity of Almería Agrifood Campus of International ExcellenceAlmeríaSpain
  2. 2.Dpto. Ingeniería de Sistemas y Automática, Escuela Técnica Superior de IngenieríaUniversity of SevillaSevillaSpain

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