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

被引:76
|
作者
Melikoglu, Mehmet [1 ]
机构
[1] Atilim Univ, Dept Energy Syst Engn, Ankara, Turkey
来源
关键词
Demand forecasting; Logistic equation; Natural gas; Vision; 2023; Turkey's energy policy; GENETIC ALGORITHM APPROACH; ENERGY-CONSUMPTION; ELECTRICITY CONSUMPTION; FUTURE PERSPECTIVES; ECONOMIC-GROWTH; TIME-SERIES; MODEL; PREDICTION; POLICY; GDP;
D O I
10.1016/j.rser.2013.01.048
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Natural gas is the primary source for electricity production in Turkey. However, Turkey does not have indigenous resources and imports more than 98.0% of the natural gas it consumes. In 2011, more than 20.0% of Turkey's annual trade deficit was due to imported natural gas, estimated at US$ 20.0 billion. Turkish government has very ambitious targets for the country's energy sector in the next decade according to the Vision 2023 agenda. Previously, we have estimated that Turkey's annual electricity demand would be 530,000 GWh at the year 2023. Considering current energy market dynamics it is almost evident that a substantial amount of this demand would be supplied from natural gas. However, meticulous analysis of the Vision 2023 goals clearly showed that the information about the natural gas sector is scarce. Most importantly there is no demand forecast for natural gas in the Vision 2023 agenda. Therefore, in this study the aim was to generate accurate forecasts for Turkey's natural gas demand between 2013 and 2030. For this purpose, two semi-empirical models based on econometrics, gross domestic product (GDP) at purchasing power parity (PPP) per capita, and demographics, population change, were developed. The logistic equation, which can be used for long term natural gas demand forecasting, and the linear equation, which can be used for medium term demand forecasting, fitted to the timeline series almost seamlessly. In addition, these two models provided reasonable fits according to the mean absolute percentage error, MAPE %, criteria. Turkey's natural gas demand at the year 2030 was calculated as 76.8 billion m(3) using the linear model and 83.8 billion m(3) based on the logistic model. Consequently, found to be in better agreement with the official Turkish petroleum pipeline corporation (BOTAS) forecast, 76.4 billion m(3), than results published in the literature. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:393 / 400
页数:8
相关论文
共 50 条
  • [1] Forecasting of Turkey natural gas demand using a hybrid algorithm
    Ozdemir, Gultekin
    Aydemir, Erdal
    Olgun, Mehmet Onur
    Mulbay, Zekeriya
    [J]. ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY, 2016, 11 (04) : 295 - 302
  • [2] Natural gas demand in Turkey
    Erdogdu, Erkan
    [J]. APPLIED ENERGY, 2010, 87 (01) : 211 - 219
  • [3] Natural gas implementation in Turkey. Part 1: Turkey's natural gas demand and supplies
    Ozturk, HK
    Hepbasli, A
    [J]. ENERGY SOURCES, 2004, 26 (03): : 277 - 286
  • [4] Forecasting of Turkey's natural gas demand using artifical neural networks and support vector machines
    Olgun, Mehmet Onur
    Ozdemir, Gultekin
    Aydemir, Erdal
    [J]. ENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCH, 2012, 30 (01): : 15 - 20
  • [5] Forecasting residential natural gas demand
    Aras, H
    Aras, N
    [J]. ENERGY SOURCES, 2004, 26 (05): : 463 - 472
  • [6] China's Natural Gas Demand Projections and Supply Capacity Analysis in 2030
    Ji, Qiang
    Fan, Ying
    Troilo, Mike
    Ripple, Ronald D.
    Feng, Lianyong
    [J]. ENERGY JOURNAL, 2018, 39 (06): : 53 - 70
  • [7] Modelling and forecasting the demand for natural gas in Pakistan
    Khan, Muhammad Arshad
    [J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 49 : 1145 - 1159
  • [8] Modeling and forecasting natural gas demand in Bangladesh
    Wadud, Zia
    Dey, Himadri S.
    Kabir, Md. Ashfanoor
    Khan, Shahidul I.
    [J]. ENERGY POLICY, 2011, 39 (11) : 7372 - 7380
  • [9] Modeling and Forecasting Residential Natural Gas Demand in IRAN
    Jafari, Fatemeh Daei
    Sadigh, Raissi
    [J]. REVISTA GESTAO & TECNOLOGIA-JOURNAL OF MANAGEMENT AND TECHNOLOGY, 2019, 19 (04): : 33 - 57
  • [10] Forecasting China's regional energy demand by 2030: A Bayesian approach
    Yuan, Xiao-Chen
    Sun, Xun
    Zhao, Weigang
    Mi, Zhifu
    Wang, Bing
    Wei, Yi-Ming
    [J]. RESOURCES CONSERVATION AND RECYCLING, 2017, 127 : 85 - 95