Prediction of energy performance of residential buildings: a genetic programming approach

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@Article{Castelli:2015:EB,
  author =       "Mauro Castelli and Leonardo Trujillo and 
                 Leonardo Vanneschi and Ales Popovic",
  title =        "Prediction of energy performance of residential
                 buildings: a genetic programming approach",
  journal =      "Energy and Buildings",
  year =         "2015",
  volume =       "102",
  number =       "1",
  pages =        "67--74",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Energy
                 consumption, Heating load, Cooling load, Machine
                 learning",
  ISSN =         "0378-7788",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0378778815003849",
  DOI =          "doi:10.1016/j.enbuild.2015.05.013",
  size =         "8 pages",
  abstract =     "Energy consumption has long been emphasized as an
                 important policy issue in today's economies. In
                 particular, the energy efficiency of residential
                 buildings is considered a top priority of a country's
                 energy policy. The paper proposes a genetic
                 programming-based framework for estimating the energy
                 performance of residential buildings. The objective is
                 to build a model able to predict the heating load and
                 the cooling load of residential buildings. An accurate
                 prediction of these parameters facilitates a better
                 control of energy consumption and, moreover, it helps
                 choosing the energy supplier that better fits the
                 energy needs, which is considered an important issue in
                 the deregulated energy market. The proposed framework
                 blends a recently developed version of genetic
                 programming with a local search method and linear
                 scaling. The resulting system enables us to build a
                 model that produces an accurate estimation of both
                 considered parameters. Extensive simulations on 768
                 diverse residential buildings confirm the suitability
                 of the proposed method in predicting heating load and
                 cooling load. In particular, the proposed method is
                 more accurate than the existing state-of-the-art
                 techniques.",
}

Genetic Programming entries for Mauro Castelli Leonardo Trujillo Leonardo Vanneschi Ales Popovic

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