Genetic programming prediction of the natural gas consumption in a steel plant

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@Article{Kovacic:2014:energy,
  author =       "Miha Kovacic and Bozidar Sarler",
  title =        "Genetic programming prediction of the natural gas
                 consumption in a steel plant",
  journal =      "Energy",
  year =         "2014",
  volume =       "66",
  number =       "1",
  pages =        "273--284",
  month =        "1 " # mar,
  keywords =     "genetic algorithms, genetic programming, Natural gas
                 consumption, CH4, Methane, Steel plant, Modeling",
  ISSN =         "0360-5442",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0360544214001340",
  DOI =          "doi:10.1016/j.energy.2014.02.001",
  size =         "12 pages",
  abstract =     "Highlights

                 Energy Agency of the Republic of Slovenia regulates the
                 natural gas market and determines charging for
                 gas-related imbalances.

                 Store Steel's (steelmaking company) natural gas
                 consumption represents 1.1percent of Slovenian
                 consumption.

                 In the attempt to predict the gas consumption the
                 linear regression and the genetic programming
                 approaches were used.

                 The genetic programming model performs approximately
                 two times more favourable.

                 Yearly savings of developed gas consumption model, used
                 from April 2005, amounts to approximately 100,000
                 Euros.

                 Abstract

                 The Energy Agency of the Republic of Slovenia regulates
                 and determines the operations of the natural-gas
                 market, charges for related gas imbalances, decides on
                 suppliers and controls penalty provisions relating to
                 breaches of stipulated provisions. Each supplier
                 regulates and determines the charges for the
                 differences between the ordered (predicted) and the
                 actually supplied quantities. Store Steel Company is
                 one of the major spring-steel producers in Europe. Its
                 natural gas consumption represents approximately
                 1.1percent of Slovenia's national natural gas
                 consumption. The company is contractually bound to a
                 supplier which exacts penalties according to the
                 differences mentioned above. A successful approach to
                 gas consumption prediction is elaborated in this paper,
                 with the aim of minimising associated costs. In the
                 attempt to model and predict the gas consumption and,
                 accordingly, to minimise ordered and supplied gas
                 quantity error, we used linear regression and the
                 genetic programming approach. The genetic programming
                 model performs approximately two times more favourably.
                 The developed gas consumption model has been used in
                 practice since April 2005. The results show good
                 agreement between the model-based ordered quantities
                 and the actually supplied quantities, with savings
                 amounting to approximately 100,000 EUR per year.",
  notes =        "Store Steel Company and the Slovenian Research
                 Agency",
}

Genetic Programming entries for Miha Kovacic Bozidar Sarler

Citations