Two-Stage Approach for Electricity Consumption Forecasting in Public Buildings

  • Antonio Morán
  • Miguel A. Prada
  • Serafín Alonso
  • Pablo Barrientos
  • Juan J. Fuertes
  • Manuel Domínguez
  • Ignacio Díaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


Many preprocessing and prediction techniques have been used for large-scale electricity load forecasting. However, small-scale prediction, such as in the case of public buildings, has received little attention. This field presents certain specific features. The most distinctive one is that consumption is extremely influenced by the activity in the building. For that reason, a suitable approach to predict the next 24-hour consumption profiles is presented in this paper. First, the features that influence the consumption are processed and selected. These environmental variables are used to cluster the consumption profiles in subsets of similar behavior using neural gas. A direct forecasting approach based on Support Vector Regression (SVR) is applied to each cluster to enhance the prediction. The input vector is selected from a set of past values. The approach is validated on teaching and research buildings at the University of León.


Time-series prediction electricity consumption forecasting feature selection clustering regression 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Antonio Morán
    • 1
  • Miguel A. Prada
    • 1
  • Serafín Alonso
    • 1
  • Pablo Barrientos
    • 1
  • Juan J. Fuertes
    • 1
  • Manuel Domínguez
    • 1
  • Ignacio Díaz
    • 2
  1. 1.Esc. de Ing. Industrial e InformáticaSUPPRESS Research GroupLeónSpain
  2. 2.Dept. de Ing. Elétrica, Electrónica, de Computadores y SistemasUniversidad de OviedoGijónSpain

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