<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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 2% 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 Štore 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.</p></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 2% 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 Štore 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.

Natural gas consumption prediction in Slovenian industry – a case study

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

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

Univerza v Mariboru, Fakulteta za strojništvo, Smetanova ulica 17, 2000 Maribor,

Materials and Geoenvironment

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Natural gas consumption prediction in Slovenian industry \u2013 a case study"}

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...

Natural gas consumption prediction in Slovenian industry – a case study

Data publikacji: 26 paź 2016

Zakres stron: 91 - 96

Otrzymano: 11 maj 2016

Przyjęty: 16 cze 2016

DOI: https://doi.org/10.1515/rmzmag-2016-0008

Słowa kluczowe
natural gas, consumption, modeling, linear regression, genetic programming, artificial neural networks, industry

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Natural gas consumption prediction in Slovenian industry – a case study

Data publikacji: 26 paź 2016

Zakres stron: 91 - 96

Otrzymano: 11 maj 2016

Przyjęty: 16 cze 2016

DOI: https://doi.org/10.1515/rmzmag-2016-0008

Słowa kluczowenatural gas, consumption, modeling, linear regression, genetic programming, artificial neural networks, industry

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
natural gas, consumption, modeling, linear regression, genetic programming, artificial neural networks, industry