The problems of selection of optimal mathematical model of energy consumption at industrial enterprises

Authors

DOI:

https://doi.org/10.15587/1729-4061.2013.18122

Keywords:

multi-criteria model, quality of data description model, morphological criterion

Abstract

The determination of so called “standards” of energy consumption is one of the most essential stages of creation and efficient operation of energy consumption control systems that are widely used in foreign practice and are known in Ukraine as energy consumption control and planning systems. This “standard”, in general, is a more or less complex mathematical model of fuel or energy consumption by certain technological objects (plant, unit, and processing line) depending on key criteria essentially affecting energy use of such object.

It is proposed to use multi-criteria optimization method in order to obtain the best mathematical model. The situations, when one mathematical model is more accurate according to generalized additive criterion, while the other one is accurate according to multiplicative criterion, arise in the process of selection of optimal mathematical model, applying the criteria of equal significance. In this case, it does not ensure unique determination of optimal energy consumption mathematical model. It is proposed to introduce a decisive generalized morphological criterion for “n” number of criterion cases in order to distinguish necessary model, concerning the precision of its description of incoming data for industrial enterprises using different types of resources.

Author Biographies

Анатолій Васильович Волошко, IEE NTUU "KPI" Str. Borschagivska, 115, 03056

Associate Professor
Department of Electric power supply

Ярослав Семенович Бедерак, PAT "AZOT" c. Cherkassy

engineer

Тетяна Миколаївна Лутчин, IEE NTUU "KPI" Str. Borschagivska, 115, 03056

PhD student
Department of Electric power supply

References

  1. Pentland, A. Honest Signals: How They Shape Our World [Текст] / A. Pentland, S. Pentland. – The MIT Press, 2008. – 208 c.
  2. Mayer, P. Data Recovery: Choosing the Right Technologies [Текст] / P. Mayer. – Datalink, 2003.
  3. Holden, J. M. Development of a multinutrient data quality evaluation system [Текст] / J. M. Holden, S. A. Bhagwat, K. Y. Patterson // J. Food Compos. Anal. – 2002. – 15(4). – C. 339348.
  4. Литтл, Р. Дж. А. Статистический анализ данных с пропусками [Текст] / Р. Дж. А. Литтл, Д. Б. Рубин. – М.: Финансы и статистика, 1990. – 336 c.
  5. Злоба, Е. Статистические методы восстановления пропущенных данных [Текст] / Е. Злоба, И. Яцкив // Computer Modelling & New Technologies. – 2002. – T. 6(1). – С. 5161.
  6. Schafer, J. Missing data: our view of the state of the art [Текст] / J. Schafer, J. Graham // Psychological Methods. – 2002. – 7 (2). – C. 147177.
  7. Новицкий, П. В. Оценка погрешностей результатов измерений [Текст] / П. В. Новицкий, И. А. Зограф // Л.: Энергоатомиздат, Ленингр. отд-ние, 1985. – 302 c.
  8. Волошко, А. В. Відновлення втрачених облікових [Текст] / А. В. Волошко, Т. М. Лутчин, Д. К. Міщенко, Я. С. Бедерак // Вісник КНУ ім. М. Остроградського. – 2012. – T. 2 (73). – C. 426428.
  9. Стеценко, І. В. Побудова багатофакторних математичних моделей енергоспоживання на хімічному виробництві [Текст] / І. В. Стеценко, Я. С. Бедерак // Энергосбережение, энергетика, энергоаудит. – 2013. – T. 7.
  10. Ивахненко, А. Г. Самоорганизация прогнозирующих моделей [Текст] / А. Г. Ивахненко, Й. А. К. Мюллер. – К. : Наукова думка, 1985. – 219 c.
  11. Горбунов, В. М. Теория принятия решений [Текст] : учеб. пос. ГОУВПО / В. М. Горбунов. – Национальный исследовательский Томский политехнический университет, 2010. – 67 c.
  12. Zwillinger, D. CRC Standard Mathematical Tables and Formulae [Текст] / D. Zwillinger // CRC, Boca Raton, 2003. – 857 c.
  13. Pentland, A., Pentland, S. (2008). Honest Signals: How They Shape Our World. The MIT Press, 208.
  14. Mayer, P. (2003). Data Recovery: Choosing the Right Technologies. Datalink.
  15. Holden, J. M., Bhagwat, S. A. & Patterson, K. Y. (2002). Development of a multinutrient data quality evaluation system. J. Food Compos. Anal, 15(4), 339348.
  16. Littl, R. J. A., Rubin, D. B. (1990). Statisticheskiy analiz dannykh s propuskami. M.: Finansy i statistika, 336.
  17. Zloba, E., Yatskiv, I. (2002). Statisticheskie metody vosstanovleniya propushchennykh dannykh. Computer Modelling & New Technologies, 6(1), 51-61.
  18. Schafer, J., Graham, J. (2002). Missing data: our view of the state of the art. Psychological Methods, 7(2), 147-177.
  19. Novitskiy, P. V., Zograf, I. A. (1985). Otsenka pogreshnostey rezul'tatov izmereniy. L.: Energoatomizdat, Leningr. otd-nie, 302.
  20. Voloshko, A. V., Lutchyn, T. M., Mishchenko, D. K., Bederak, Ya. S. (2012). Vidnovlennya vtrachenykh oblikovykh. Visnyk KNU im. Mykhayla Ostrohrads'koho, 2(73), 426-428.
  21. Stetsenko I. V., Bederak, Ya. S. (2013). Pobudova bahatofaktornykh matematychnykh modeley enerhospozhyvannya na khimichnomu vyrobnytstvi. Enerhosberezhenye, enerhetyka, enerhoaudyt, 7.
  22. Ivakhnenko, A. G., Myuller, Y. A. K. (1985). Samoorganizatsiya prognoziruyushchikh modeley. K.: Naukova dumka, 219.
  23. Gorbunov, V. M. (2010). Teoriya prinyatiya resheniy: Uchebnoe posobie GOUVPO. «Natsional'nyy issledovatel'skiy Tomskiy politekhnicheskiy universitet», 67.
  24. Zwillinger, D. (2003). CRC Standard Mathematical Tables and Formulae. CRC, Boca Raton, 857.

Published

2013-10-25

How to Cite

Волошко, А. В., Бедерак, Я. С., & Лутчин, Т. М. (2013). The problems of selection of optimal mathematical model of energy consumption at industrial enterprises. Eastern-European Journal of Enterprise Technologies, 5(8(65), 19–23. https://doi.org/10.15587/1729-4061.2013.18122