- 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",
