IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v83y2015icp252-266.html
   My bibliography  Save this article

Forecasting semi-dynamic response of natural gas networks to nodal gas consumptions using genetic fuzzy systems

Author

Listed:
  • Askari, S.
  • Montazerin, N.
  • Zarandi, M.H. Fazel

Abstract

-Semi-dynamic behavior of natural gas distribution network and nodal gas consumptions are predicted. Traditional Hardy-Cross method for analysis of the gas network is replaced with a direct mathematical solution of mass conservation equations at network nodes to yield nodal static pressures and volumetric flow rates for the coming days. After the calculation of static pressure distribution in a network for near future days, the problem of pressure drop in the network which is a serious problem in cold seasons can be managed in advance. TSK (Takagi-Sugeno-Kang) fuzzy system is used for forecasting. Structure identification of the system is carried out using CVIs (Cluster Validity Indices) and PFCM (Possibilistic Fuzzy C-Means algorithm) to determine number of rules which is also chosen such that testing error of the system does not exceed a predefined value. Premise and t-norm parameters of the TSK system are tuned by GAs (Genetic Algorithms) and their consequent parameters are adjusted using LSE (Least Square Estimate). Comparison of testing error of the TSK system for modeling benchmark data with other popular methods demonstrates its suitability for forecasting nodal gas consumptions.

Suggested Citation

  • Askari, S. & Montazerin, N. & Zarandi, M.H. Fazel, 2015. "Forecasting semi-dynamic response of natural gas networks to nodal gas consumptions using genetic fuzzy systems," Energy, Elsevier, vol. 83(C), pages 252-266.
    • Handle: RePEc:eee:energy:v:83:y:2015:i:c:p:252-266
    DOI: 10.1016/j.energy.2015.02.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215001723
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.02.020?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Herbert, John H. & Burns, Eugene M., 1991. "Analysis of natural gas consumption in commercial buildings using sample survey data," Energy, Elsevier, vol. 16(6), pages 903-908.
    2. Sailor, David J. & Muñoz, J.Ricardo, 1997. "Sensitivity of electricity and natural gas consumption to climate in the U.S.A.—Methodology and results for eight states," Energy, Elsevier, vol. 22(10), pages 987-998.
    3. Herbert, John H. & Sitzer, Scott & Eades-Pryor, Yvonne, 1987. "A statistical evaluation of aggregate monthly industrial demand for natural gas in the U.S.A," Energy, Elsevier, vol. 12(12), pages 1233-1238.
    4. Maggio, G. & Cacciola, G., 2009. "A variant of the Hubbert curve for world oil production forecasts," Energy Policy, Elsevier, vol. 37(11), pages 4761-4770, November.
    5. 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.
    6. 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.
    7. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
    8. Brkic, Dejan, 2009. "An improvement of Hardy Cross method applied on looped spatial natural gas distribution networks," Applied Energy, Elsevier, vol. 86(7-8), pages 1290-1300, July.
    9. Sailor, David J. & Rosen, Jesse N. & Muñoz, J.Ricardo, 1998. "Natural gas consumption and climate: a comprehensive set of predictive state-level models for the United States," Energy, Elsevier, vol. 23(2), pages 91-103.
    10. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    11. 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.
    12. 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.
    13. Fernandez, Roque B, 1981. "A Methodological Note on the Estimation of Time Series," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 471-476, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Balitskiy, Sergey & Bilan, Yuriy & Strielkowski, Wadim & Štreimikienė, Dalia, 2016. "Energy efficiency and natural gas consumption in the context of economic development in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 156-168.
    3. Emmanuel Flavian Sapnken & Jean Gaston Tamba & Salome Njakomo Essiane & Francis Djanna Koffi & Donatien Njomo, 2018. "Modeling and Forecasting Gasoline Consumption in Cameroon using Linear Regression Models," International Journal of Energy Economics and Policy, Econjournals, vol. 8(2), pages 111-120.
    4. Szoplik, Jolanta, 2016. "Improving the natural gas transporting based on the steady state simulation results," Energy, Elsevier, vol. 109(C), pages 105-116.
    5. Spoladore, Alessandro & Borelli, Davide & Devia, Francesco & Mora, Flavio & Schenone, Corrado, 2016. "Model for forecasting residential heat demand based on natural gas consumption and energy performance indicators," Applied Energy, Elsevier, vol. 182(C), pages 488-499.
    6. Zhihua Chen & Hui Wang & Tongxia Li & Ieongcheng Si, 2021. "Demand for Storage and Import of Natural Gas in China until 2060: Simulation with a Dynamic Model," Sustainability, MDPI, vol. 13(15), pages 1-19, August.
    7. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    8. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
    9. 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.

    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 & 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.
    2. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    3. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. Yau, Y.H. & Pean, H.L., 2011. "The climate change impact on air conditioner system and reliability in Malaysia—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4939-4949.
    10. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    11. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    12. Kim Abildgren, 2016. "A century of macro-financial linkages," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 8(4), pages 458-471, November.
    13. Pieroni, Luca & d'Agostino, Giorgio & Lorusso, Marco, 2008. "Can we declare military Keynesianism dead?," Journal of Policy Modeling, Elsevier, vol. 30(5), pages 675-691.
    14. Campbell Leith & Jim Malley, 2007. "A Sectoral Analysis of Price-Setting Behavior in U.S. Manufacturing Industries," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 335-342, May.
    15. Jürgen Bierbaumer-Polly & Sandra Bilek-Steindl, 2017. "Quarterly National Accounts – Manual for Austria. Description of Applied Methods and Data Sources," WIFO Studies, WIFO, number 60427, June.
    16. Wenzel, Lars & Wolf, André, 2013. "Short-term forecasting with business surveys: Evidence for German IHK data at federal state level," HWWI Research Papers 140, Hamburg Institute of International Economics (HWWI).
    17. Emanuel Mönch & Harald Uhlig, 2005. "Towards a Monthly Business Cycle Chronology for the Euro Area," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 43-69.
    18. Huang, Yu-Lieh, 2012. "Measuring business cycles: A temporal disaggregation model with regime switching," Economic Modelling, Elsevier, vol. 29(2), pages 283-290.
    19. Tao Zha & Kaiji Chen, 2017. "The Asymmetric Transmission of China's Monetary Policy," 2017 Meeting Papers 516, Society for Economic Dynamics.
    20. David Aristei & Luca Pieroni, 2005. "Estimating the Role of Government Expenditure in Long-run Consumption," Quaderni del Dipartimento di Economia, Finanza e Statistica 13/2005, Università di Perugia, Dipartimento Economia.

    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:eee:energy:v:83:y:2015:i:c:p:252-266. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.