Estimating building energy consumption using extreme learning machine method

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@Article{Naji:2016:Energy,
  author =       "Sareh Naji and Afram Keivani and 
                 Shahaboddin Shamshirband and U. Johnson Alengaram and 
                 Mohd Zamin Jumaat and Zulkefli Mansor and Malrey Lee",
  title =        "Estimating building energy consumption using extreme
                 learning machine method",
  journal =      "Energy",
  volume =       "97",
  pages =        "506--516",
  year =         "2016",
  ISSN =         "0360-5442",
  DOI =          "doi:10.1016/j.energy.2015.11.037",
  URL =          "http://www.sciencedirect.com/science/article/pii/S036054421501587X",
  abstract =     "The current energy requirements of buildings comprise
                 a large percentage of the total energy consumed around
                 the world. The demand of energy, as well as the
                 construction materials used in buildings, are becoming
                 increasingly problematic for the earth's sustainable
                 future, and thus have led to alarming concern. The
                 energy efficiency of buildings can be improved, and in
                 order to do so, their operational energy usage should
                 be estimated early in the design phase, so that
                 buildings are as sustainable as possible. An early
                 energy estimate can greatly help architects and
                 engineers create sustainable structures. This study
                 proposes a novel method to estimate building energy
                 consumption based on the ELM (Extreme Learning Machine)
                 method. This method is applied to building material
                 thicknesses and their thermal insulation capability
                 (K-value). For this purpose up to 180 simulations are
                 carried out for different material thicknesses and
                 insulation properties, using the EnergyPlus software
                 application. The estimation and prediction obtained by
                 the ELM model are compared with GP (genetic
                 programming) and ANNs (artificial neural network)
                 models for accuracy. The simulation results indicate
                 that an improvement in predictive accuracy is
                 achievable with the ELM approach in comparison with GP
                 and ANN.",
  keywords =     "genetic algorithms, genetic programming, Energy
                 consumption, Residential buildings, Estimation, Energy
                 efficiency, ELM (extreme learning machine)",
  notes =        "Department of Civil Engineering, Faculty of
                 Engineering, University of Malaya, Kuala Lumpur,
                 Malaysia",
}

Genetic Programming entries for Sareh Naji Afram Keivani Shahaboddin Shamshirband U Johnson Alengaram Mohd Zamin Jumaat Zulkefli Mansor Malrey Lee

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