IDEAS home Printed from https://ideas.repec.org/p/bou/wpaper/2014-10.html
   My bibliography  Save this paper

A Comparison of Stochastic Models of Natural Gas Consumption

Author

Listed:
  • Serli Kiremitciyan
  • Ahmet Goncu
  • Tolga Umut Kuzubas

Abstract

No abstract is available for this item.

Suggested Citation

  • Serli Kiremitciyan & Ahmet Goncu & Tolga Umut Kuzubas, 2014. "A Comparison of Stochastic Models of Natural Gas Consumption," Working Papers 2014/10, Bogazici University, Department of Economics.
  • Handle: RePEc:bou:wpaper:2014/10
    as

    Download full text from publisher

    File URL: http://www.econ.boun.edu.tr/public_html/RePEc/pdf/201410.pdf
    File Function: First version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    2. Crompton, Paul & Wu, Yanrui, 2005. "Energy consumption in China: past trends and future directions," Energy Economics, Elsevier, vol. 27(1), pages 195-208, January.
    3. Liu, Lon-Mu & Lin, Maw-Wen, 1991. "Forecasting residential consumption of natural gas using monthly and quarterly time series," International Journal of Forecasting, Elsevier, vol. 7(1), pages 3-16, May.
    4. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    5. Erdogdu, Erkan, 2010. "A review of Turkish natural gas distribution market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(2), pages 806-813, February.
    6. Gutiérrez, R. & Gutiérrez-Sánchez, R. & Nafidi, A., 2006. "Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors," Applied Energy, Elsevier, vol. 83(10), pages 1139-1151, October.
    7. Ahmet GÖNCÜ & Mehmet Oğuz KARAHAN & Tolga Umut KUZUBAŞ, 2013. "A Stochastic Model for Natural Gas Consumption: An Application for Turkey," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 28(332), pages 33-46.
    8. Erdogdu, Erkan, 2010. "Natural gas demand in Turkey," Applied Energy, Elsevier, vol. 87(1), pages 211-219, January.
    9. Gutiérrez, R. & Nafidi, A. & Gutiérrez Sánchez, R., 2005. "Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model," Applied Energy, Elsevier, vol. 80(2), pages 115-124, February.
    10. Sarak, H & Satman, A, 2003. "The degree-day method to estimate the residential heating natural gas consumption in Turkey: a case study," Energy, Elsevier, vol. 28(9), pages 929-939.
    11. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jean Gaston Tamba & Salomé Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouandélé & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    2. Potočnik, Primož & Soldo, Božidar & Šimunović, Goran & Šarić, Tomislav & Jeromen, Andrej & Govekar, Edvard, 2014. "Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia," Applied Energy, Elsevier, vol. 129(C), pages 94-103.
    3. Ahmet Goncu & Mehmet Oguz Karahan & Tolga Umut Kuzubas, 2019. "Forecasting Daily Residential Natural Gas Consumption: A Dynamic Temperature Modelling Approach," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 33(1), pages 1-22.
    4. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra & Agca Aktunc, Esra, 2021. "Forecasting models for daily natural gas consumption considering periodic variations and demand segregation," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    5. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    6. Ergun Yukseltan & Ahmet Yucekaya & Ayse Humeyra Bilge & Esra Agca Aktunc, 2020. "Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation," Papers 2003.13385, arXiv.org.
    7. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    8. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    9. Ahmet Goncu & Mehmet Oguz Karahan & Tolga Umut Kuzubas, 2013. "Forecasting Daily Residential Natural Gas Consumption: A Dynamic Temperature Modelling Approach," Working Papers 2013/11, Bogazici University, Department of Economics.
    10. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    11. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
    12. Mustafa Akpinar & Nejat Yumusak, 2016. "Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods," Energies, MDPI, Open Access Journal, vol. 9(9), pages 1-17, September.
    13. Melikoglu, Mehmet, 2013. "Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 393-400.
    14. Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Scenario analysis of nonresidential natural gas consumption in Italy," Applied Energy, Elsevier, vol. 113(C), pages 392-403.
    15. M. Brabec & O. Kon�r & M. Malý & I. Kasanický & E. Pelik�n, 2015. "Statistical models for disaggregation and reaggregation of natural gas consumption data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 921-937, May.
    16. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    17. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    18. Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
    19. Xu, Weijun & Gu, Ren & Liu, Youzhu & Dai, Yongwu, 2015. "Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China," Economic Modelling, Elsevier, vol. 45(C), pages 127-135.
    20. Gutiérrez, R. & Gutiérrez-Sánchez, R. & Nafidi, A., 2009. "The trend of the total stock of the private car-petrol in Spain: Stochastic modelling using a new gamma diffusion process," Applied Energy, Elsevier, vol. 86(1), pages 18-24, January.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bou:wpaper:2014/10. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/deboutr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lutfu Gozgucu (email available below). General contact details of provider: https://edirc.repec.org/data/deboutr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.