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Modeling and forecasting industrial end-use natural gas consumption

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  1. 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.
  2. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
  3. Greening, Lorna A. & Boyd, Gale & Roop, Joseph M., 2007. "Modeling of industrial energy consumption: An introduction and context," Energy Economics, Elsevier, vol. 29(4), pages 599-608, July.
  4. 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.
  5. 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.
  6. 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.
  7. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
  8. Sreekanth, K.J., 2016. "Review on integrated strategies for energy policy planning and evaluation of GHG mitigation alternatives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 837-850.
  9. Peñasco, Cristina & del Río, Pablo & Romero-Jordán, Desiderio, 2017. "Gas and electricity demand in Spanish manufacturing industries: An analysis using homogeneous and heterogeneous estimators," Utilities Policy, Elsevier, vol. 45(C), pages 45-60.
  10. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
  11. Inchauspe, Julian & Li, Jun & Park, Jason, 2020. "Seasonal patterns of global oil consumption: Implications for long term energy policy," Journal of Policy Modeling, Elsevier, vol. 42(3), pages 536-556.
  12. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
  13. 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.
  14. Palanisamy Manigandan & MD Shabbir Alam & Majed Alharthi & Uzma Khan & Kuppusamy Alagirisamy & Duraisamy Pachiyappan & Abdul Rehman, 2021. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, MDPI, vol. 14(19), pages 1-17, September.
  15. 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.
  16. Chávez-Rodríguez, Mauro F. & Dias, Luís & Simoes, Sofia & Seixas, Júlia & Hawkes, Adam & Szklo, Alexandre & Lucena, Andre F.P., 2017. "Modelling the natural gas dynamics in the Southern Cone of Latin America," Applied Energy, Elsevier, vol. 201(C), pages 219-239.
  17. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
  18. Rehman, Aniqa & Zhu, Jun-Jie & Segovia, Javier & Anderson, Paul R., 2022. "Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain," Energy, Elsevier, vol. 244(PA).
  19. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
  20. 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.
  21. Zhou, Zhong-bing & Dong, Xiu-cheng, 2012. "Analysis about the seasonality of China's crude oil import based on X-12-ARIMA," Energy, Elsevier, vol. 42(1), pages 281-288.
  22. 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.
  23. 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.
  24. Santiago Moreno-Carbonell & Eugenio F. Sánchez-Úbeda & Antonio Muñoz, 2020. "Time Series Decomposition of the Daily Outdoor Air Temperature in Europe for Long-Term Energy Forecasting in the Context of Climate Change," Energies, MDPI, vol. 13(7), pages 1-28, March.
  25. 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.
  26. 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.
  27. An, Jinyu & Peng, Shini, 2018. "Prediction and verification of risk loss cost for improved natural gas network layout optimization," Energy, Elsevier, vol. 148(C), pages 1181-1190.
  28. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, vol. 24(4), pages 659-678.
  29. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  30. 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.
  31. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
  32. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
  33. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
  34. 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.
  35. Ahmadian Behrooz, Hesam & Boozarjomehry, R. Bozorgmehry, 2017. "Dynamic optimization of natural gas networks under customer demand uncertainties," Energy, Elsevier, vol. 134(C), pages 968-983.
  36. Ríos-Mercado, Roger Z. & Borraz-Sánchez, Conrado, 2015. "Optimization problems in natural gas transportation systems: A state-of-the-art review," Applied Energy, Elsevier, vol. 147(C), pages 536-555.
  37. 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.
  38. 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.
  39. Azadeh, A. & Asadzadeh, S.M. & Mirseraji, G.H. & Saberi, M., 2015. "An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 47-63.
  40. Mustafa Akpinar & M. Fatih Adak & Nejat Yumusak, 2017. "Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey," Energies, MDPI, vol. 10(6), pages 1-20, June.
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