Natural gas consumption prediction in Slovenian industry - a case study

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@Article{Kovacic:2016:RMZ,
  author =       "Miha Kovacic and Bozidar Sarler and Uros Zuperl",
  title =        "Natural gas consumption prediction in Slovenian
                 industry - a case study",
  journal =      "Materials and Geoenvironment",
  year =         "2016",
  volume =       "63",
  number =       "2",
  pages =        "91--96",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, natural gas,
                 consumption, modelling, linear regression, artificial
                 neural networks, industry",
  ISSN =         "1854-7400",
  URL =          "https://www.degruyter.com/view/j/rmzmag.2016.63.issue-2/rmzmag-2016-0008/rmzmag-2016-0008.xml?format=INT",
  DOI =          "doi:10.1515/rmzmag-2016-0008",
  size =         "6 pages",
  abstract =     "In accordance with the regulations of the Energy
                 Agency of the Republic of Slovenia, each natural gas
                 supplier regulates and determines the charges for the
                 differences between the ordered (predicted) and the
                 actually supplied quantities of natural gas. Yearly
                 charges for these differences represent up to 2percent
                 of supplied natural gas costs. All the natural gas
                 users, especially industry, have huge problems finding
                 the proper method for efficient natural gas consumption
                 prediction and, consequently, the decreasing of
                 mentioned costs. In this study, prediction of the
                 natural gas consumption in Store Steel Ltd. (steel
                 plant) is presented. On the basis of production data,
                 several models for natural gas consumption have been
                 developed using linear regression, genetic programming
                 and artificial neural network methods. The genetic
                 programming approach outperformed linear regression and
                 artificial neural networks.",
  notes =        "RMZ Product Type: Journals/Yearbooks Open Access

                 Store Steel d.o.o., Zelezarska cesta 3, Store, Slovenia
                 and Institute of Metals and Technology, Lepi pot 11,
                 Ljubljana, Slovenia

                 Institute of Metals and Technology, Lepi pot 11,
                 Ljubljana, Slovenia

                 Univerza v Mariboru, Fakulteta za strojnistvo,
                 Smetanova ulica 17, 2000 Maribor, Slovenia",
}

Genetic Programming entries for Miha Kovacic Bozidar Sarler Uros Zuperl

Citations