1. bookVolumen 63 (2016): Heft 2 (July 2016)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1854-7400
Erstveröffentlichung
30 Mar 2016
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
access type Uneingeschränkter Zugang

Natural gas consumption prediction in Slovenian industry – a case study

Online veröffentlicht: 26 Oct 2016
Seitenbereich: 91 - 96
Eingereicht: 11 May 2016
Akzeptiert: 16 Jun 2016
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1854-7400
Erstveröffentlichung
30 Mar 2016
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
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.

[1] Kovačič M., Šarler B., Genetic programming prediction of the natural gas consumption in a steel plant. Energy 2014;66:273-84. doi: 10.1016/j.energy.2014.02.001.Search in Google Scholar

[2] Kovačič M., Dolenc F., Prediction of the natural gas consumption in chemical processing facilities with genetic programming. Genet Program Evolvable Mach 2016. doi: 10.1007/s10710-016-9264-x.Search in Google Scholar

[3] Kovačič M., Šarler B., Genetic Algorithm-Based Batch Filling Scheduling in the Steel Industry. Mater Manuf Process 2011;26:464-74. doi: 10.1080/10426914.2010.525576.Search in Google Scholar

[4] Kovačič M. Modeling of Total Decarburization of Spring Steel with Genetic Programming. Mater Manuf Process 2014;30:434-43. doi: 10.1080/10426914.2014.961477.Search in Google Scholar

[5] Kovačič M., Rožej U., Brezočnik M., Genetic Algorithm Rolling Mill Layout Optimization. Mater Manuf Process 2013;28:783-7. doi: 10.1080/10426914.2012.718475.Search in Google Scholar

[6] Zuperl U., Cus F., System for off-line feedrate optimization and neural force control in end milling. Int J Adapt Control Signal Process 2012;26:105-23. doi: 10.1002/acs.1277.Search in Google Scholar

[7] Zuperl U., Cus F., Reibenschuh M., Modeling and adaptive force control of milling by using artificial techniques. J Intell Manuf 2012;23:1805-15. doi: 10.1007/s10845-010-0487-z.Search in Google Scholar

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