IDEAS home Printed from https://ideas.repec.org/r/eee/rensus/v22y2013icp393-400.html
   My bibliography  Save this item

Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030

Citations

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


Cited by:

  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. 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).
  3. Melikoglu, Mehmet, 2016. "The role of renewables and nuclear energy in Turkey׳s Vision 2023 energy targets: Economic and technical scrutiny," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1-12.
  4. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
  5. Salameh, R. & Chedid, R., 2020. "Economic and geopolitical implications of natural gas export from the East Mediterranean: The case of Lebanon," Energy Policy, Elsevier, vol. 140(C).
  6. 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.
  7. 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.
  8. Melikoglu, Mehmet, 2017. "Geothermal energy in Turkey and around the World: A review of the literature and an analysis based on Turkey's Vision 2023 energy targets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 485-492.
  9. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
  10. Kat, Bora, 2023. "Clean energy transition in the Turkish power sector: A techno-economic analysis with a high-resolution power expansion model," Utilities Policy, Elsevier, vol. 82(C).
  11. 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.
  12. 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.
  13. Wei, Sun & Yanfeng, Xu, 2017. "Research on China's energy supply and demand using an improved Grey-Markov chain model based on wavelet transform," Energy, Elsevier, vol. 118(C), pages 969-984.
  14. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
  15. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
  16. Melikoglu, Mehmet, 2017. "Pumped hydroelectric energy storage: Analysing global development and assessing potential applications in Turkey based on Vision 2023 hydroelectricity wind and solar energy targets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 146-153.
  17. Li, Jiaman & Dong, Xiucheng & Jiang, Qingzhe & Dong, Kangyin & Liu, Guixian, 2021. "Natural gas trade network of countries and regions along the belt and road: Where to go in the future?," Resources Policy, Elsevier, vol. 71(C).
  18. Berk, Istemi & Schulte, Simon, 2017. "Turkey's Role in Natural Gas - Becoming a Transit Country?," EWI Working Papers 2017-1, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), revised 27 Jan 2017.
  19. Liu, Guixian & Dong, Xiucheng & Jiang, Qingzhe & Dong, Cong & Li, Jiaman, 2018. "Natural gas consumption of urban households in China and corresponding influencing factors," Energy Policy, Elsevier, vol. 122(C), pages 17-26.
  20. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  21. 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.
  22. Ozcan, Mustafa, 2018. "The role of renewables in increasing Turkey's self-sufficiency in electrical energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2629-2639.
  23. Alparslan Neseli, Mehmet & Ozgener, Onder & Ozgener, Leyla, 2017. "Thermo-mechanical exergy analysis of Marmara Eregli natural gas pressure reduction station (PRS): An application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 80-88.
  24. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
  25. Sylvia Mardiana & Ferdinand Saragih & Martani Huseini, 2020. "Forecasting Gasoline Demand in Indonesia Using Time Series," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 132-145.
  26. 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.
  27. 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.
  28. Wang, Qi & Suo, Ruixia & Han, Qiutong, 2024. "A study on natural gas consumption forecasting in China using the LMDI-PSO-LSTM model: Factor decomposition and scenario analysis," Energy, Elsevier, vol. 292(C).
  29. Melikoglu, Mehmet, 2017. "Vision 2023: Status quo and future of biomass and coal for sustainable energy generation in Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 800-808.
  30. Melikoglu, Mehmet, 2013. "Hydropower in Turkey: Analysis in the view of Vision 2023," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 503-510.
  31. Berk, Istemi & Ediger, Volkan Ş., 2018. "A historical assessment of Turkey’s natural gas import vulnerability," Energy, Elsevier, vol. 145(C), pages 540-547.
  32. Şenol, Halil & Ali Dereli̇, Mehmet & Özbilgin, Ferdi, 2021. "Investigation of the distribution of bovine manure-based biomethane potential using an artificial neural network in Turkey to 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
  33. 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.
  34. 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).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.