Building energy consumption forecast using multi-objective genetic programming

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@Article{Tahmassebi:2018:Measurement,
  author =       "Amirhessam Tahmassebi and Amir H. Gandomi",
  title =        "Building energy consumption forecast using
                 multi-objective genetic programming",
  journal =      "Measurement",
  year =         "2018",
  volume =       "118",
  pages =        "164--171",
  keywords =     "genetic algorithms, genetic programming, Energy
                 performance, Symbolic regression",
  ISSN =         "0263-2241",
  URL =          "https://www.sciencedirect.com/science/article/pii/S0263224118300447",
  DOI =          "doi:10.1016/j.measurement.2018.01.032",
  abstract =     "A multi-objective genetic programming (MOGP) technique
                 with multiple genes is proposed to formulate the energy
                 performance of residential buildings. Here, it is
                 assumed that loads have linear relation in terms of
                 genes. On this basis, an equation is developed by MOGP
                 method to predict both heating and cooling loads. The
                 proposed evolutionary approach optimizes the most
                 significant predictor input variables in the model for
                 both accuracy and complexity, while simultaneously
                 solving the unknown parameters of the model. In the
                 proposed energy performance model, relative compactness
                 has the most and orientation the least contribution.
                 The proposed MOGP model is simple and has a high degree
                 of accuracy. The results show that MOGP is a suitable
                 tool to generate solid models for complex nonlinear
                 systems with capability of solving big data problems
                 via parallel algorithms.",
}

Genetic Programming entries for Amirhessam Tahmassebi A H Gandomi

Citations