IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2003.13385.html
   My bibliography  Save this paper

Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation

Author

Listed:
  • Ergun Yukseltan
  • Ahmet Yucekaya
  • Ayse Humeyra Bilge
  • Esra Agca Aktunc

Abstract

Due to expensive infrastructure and the difficulties in storage, supply conditions of natural gas are different from those of other traditional energy sources like petroleum or coal. To overcome these challenges, supplier countries require take-or-pay agreements for requested natural gas quantities. These contracts have many pre-clauses; if they are not met due to low/high consumption or other external factors, buyers must completely fulfill them. A similar contract is then imposed on distributors and wholesale consumers. It is thus important for all parties to forecast their daily, monthly, and annual natural gas demand to minimize their risk. In this paper, a model consisting of a modulated expansion in Fourier series, supplemented by deviations from comfortable temperatures as a regressor is proposed for the forecast of monthly and weekly consumption over a one-year horizon. This model is supplemented by a day-ahead feedback mechanism for the forecast of daily consumption. The method is applied to the study of natural gas consumption for major residential areas in Turkey, on a yearly, monthly, weekly, and daily basis. It is shown that residential heating dominates winter consumption and masks all other variations. On the other hand, weekend and holiday effects are visible in summer consumption and provide an estimate for residential and industrial use. The advantage of the proposed method is the capability of long term projections and to outperform time series methods.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2003.13385
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2003.13385
    File Function: Latest 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. 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.
    3. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 2011. "A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE," Applied Energy, Elsevier, vol. 88(11), pages 3850-3859.
    4. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    5. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
    6. 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.
    7. Hubbard, R Glenn & Weiner, Robert J, 1986. "Regulation and Long-term Contracting in U.S. Natural Gas Markets," Journal of Industrial Economics, Wiley Blackwell, vol. 35(1), pages 71-79, September.
    8. Masten, Scott E, 1988. "Minimum Bill Contracts: Theory and Policy," Journal of Industrial Economics, Wiley Blackwell, vol. 37(1), pages 85-97, September.
    9. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    10. Wadud, Zia & Dey, Himadri S. & Kabir, Md. Ashfanoor & Khan, Shahidul I., 2011. "Modeling and forecasting natural gas demand in Bangladesh," Energy Policy, Elsevier, vol. 39(11), pages 7372-7380.
    11. repec:dau:papers:123456789/5372 is not listed on IDEAS
    12. Parikh, Jyoti & Purohit, Pallav & Maitra, Pallavi, 2007. "Demand projections of petroleum products and natural gas in India," Energy, Elsevier, vol. 32(10), pages 1825-1837.
    13. 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.
    14. Mustafa Akpinar & Nejat Yumusak, 2016. "Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods," Energies, MDPI, vol. 9(9), pages 1-17, September.
    15. 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.
    16. Chen, Ying & Chua, Wee Song & Koch, Thorsten, 2018. "Forecasting day-ahead high-resolution natural-gas demand and supply in Germany," Applied Energy, Elsevier, vol. 228(C), pages 1091-1110.
    17. Li, Junchen & Dong, Xiucheng & Shangguan, Jianxin & Hook, Mikael, 2011. "Forecasting the growth of China’s natural gas consumption," Energy, Elsevier, vol. 36(3), pages 1380-1385.
    18. Masten, Scott E & Crocker, Keith J, 1985. "Efficient Adaptation in Long-term Contracts: Take-or-Pay Provisions for Natural Gas," American Economic Review, American Economic Association, vol. 75(5), pages 1083-1093, December.
    19. Durmayaz, Ahmet & Kadıoǧlu, Mikdat & Şen, Zekai, 2000. "An application of the degree-hours method to estimate the residential heating energy requirement and fuel consumption in Istanbul," Energy, Elsevier, vol. 25(12), pages 1245-1256.
    20. Forouzanfar, Mehdi & Doustmohammadi, Ali & Menhaj, M. Bagher & Hasanzadeh, Samira, 2010. "Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran," Applied Energy, Elsevier, vol. 87(1), pages 268-274, January.
    21. 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.
    22. 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.
    23. Hubbard, R Glenn & Weiner, Robert J, 1991. "Efficient Contracting and Market Power: Evidence from the U.S. Natural Gas Industry," Journal of Law and Economics, University of Chicago Press, vol. 34(1), pages 25-67, April.
    24. Fouquet, Roger & Pearson, Peter & Hawdon, David & Robinson, Colin & Stevens, Paul, 1997. "The future of UK final user energy demand," Energy Policy, Elsevier, vol. 25(2), pages 231-240, February.
    25. 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. 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).
    2. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & 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.
    3. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    4. Song, Jiancai & Zhang, Liyi & Jiang, Qingling & Ma, Yunpeng & Zhang, Xinxin & Xue, Guixiang & Shen, Xingliang & Wu, Xiangdong, 2022. "Estimate the daily consumption of natural gas in district heating system based on a hybrid seasonal decomposition and temporal convolutional network model," Applied Energy, Elsevier, vol. 309(C).
    5. 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.
    6. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    7. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    8. Bartłomiej Gaweł & Andrzej Paliński, 2021. "Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree," Energies, MDPI, vol. 14(16), pages 1-26, August.
    9. Raza, Muhammad Yousaf & Lin, Boqiang, 2023. "Future outlook and influencing factors analysis of natural gas consumption in Bangladesh: An economic and policy perspectives," Energy Policy, Elsevier, vol. 173(C).
    10. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    11. 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.
    12. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    13. Askari, S. & Montazerin, N. & Fazel Zarandi, M.H., 2016. "Gas networks simulation from disaggregation of low frequency nodal gas consumption," Energy, Elsevier, vol. 112(C), pages 1286-1298.
    14. Konstantinos Papageorgiou & Elpiniki I. Papageorgiou & Katarzyna Poczeta & Dionysis Bochtis & George Stamoulis, 2020. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 13(9), pages 1-32, May.
    15. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
    16. 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.
    17. Mustafa Akpinar & Nejat Yumusak, 2016. "Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods," Energies, MDPI, vol. 9(9), pages 1-17, September.
    18. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    19. Tomasz Cieślik & Piotr Narloch & Adam Szurlej & Krzysztof Kogut, 2022. "Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland," Energies, MDPI, vol. 15(4), pages 1-18, February.
    20. Karadede, Yusuf & Ozdemir, Gultekin & Aydemir, Erdal, 2017. "Breeder hybrid algorithm approach for natural gas demand forecasting model," Energy, Elsevier, vol. 141(C), pages 1269-1284.

    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:arx:papers:2003.13385. See general information about how to correct material in RePEc.

    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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.