Progress in Artificial Intelligence

, Volume 5, Issue 2, pp 129–135 | Cite as

Random extreme learning machines to predict electric load in buildings

  • Gonzalo Vergara
  • Juan I. Alonso-Barba
  • Emilio Soria-Olivas
  • José A. Gámez
  • Manuel Domínguez
Regular Paper


The study of energy efficiency in buildings is an active field of research. Modeling and prediction of power-related magnitudes allow us to analyse the electrical consumption. This can lead to environmental and economical benefits. In this study we compare different techniques to predict active power consumed by buildings of the University of León (Spain). The original dataset contains time, environmental and electric data for 30 buildings. In our study we follow a two-step procedure: first, we grouped the buildings in terms of their electric load using principal component analysis (PCA) and k-medoids based clustering. From this clustering we selected three prototype buildings. Second, we have applied neural network-based machine learning techniques to carry out the prediction task. In particular, we used well-known multi layer perceptron (MLP) and extreme learning machine (ELM) algorithms, as well as a new model proposed in this paper which constitutes its major contribution: random extreme learning machines (RELM). Our analysis shows that RELM advantages MLP and ELM in labour days, which is the most interested case because of it highest electric demand.


Ensembles Extreme learning machines Multi layer perceptron Prediction Electric load 



This work has been partially supported with JCCM funds by means of the project PEII-2014-049-P. Authors want to thank the SUPRESS research group of the university of León for their collaboration.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Gonzalo Vergara
    • 1
  • Juan I. Alonso-Barba
    • 1
  • Emilio Soria-Olivas
    • 2
  • José A. Gámez
    • 1
  • Manuel Domínguez
    • 3
  1. 1.SIMD, I3AUniversidad de Castilla-La ManchaAlbaceteSpain
  2. 2.IDAL, E.T.S.EUniversidad de ValenciaBurjassotSpain
  3. 3.SUPRESS, Escuela de IngenieríasUniversidad de LeónLeónSpain

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