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

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  • 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.

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  • 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
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    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
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    Cited by:

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    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.
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    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.

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