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Forecasting Primary Energy Demand By Using ANFIS Model for Turkey

Year 2017, Volume: 17 Issue: 3, 431 - 446, 01.08.2017

Abstract

Energy demand depends on a series of factors such as economic growth, energy prices, population, urbanization and efficiency. Therefore, energy demand has a very important place for economic development. In this study, some indicators are benefited from determinants of energy demand and particularly emphasize indicators in literature. Energy demand of Turkey has been investigated by using ANFIS model between 2016-2030. The relevant indicators are used as growth rate, population and energy prices as independent variable. The dependent variable of this study is energy demand of these countries. According to ANFIS result, energy demand of Turkey will increase as it does in other countries. However, energy demand will not change dramatically depending on primary energy sources such as oil and natural gas. In literature, there are studies related to growth and population. Yet, in this study, not only growth and population, but also energy prices are considered. The gap in the literature is aimed to be filled by including energy prices with other variables

References

  • AbuAl-Foul, B.M, (2012), Forecasting Energy Demand in Jordan Using Artificial Neural Networks, Topics in Middle Eastern and African Economies Vol. 14, September 2012
  • Aydın, F.F., (2010), Enerji Tüketimi ve Ekonomik
  • Büyüme, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 35, Ocak-Temmuz 2010,317
  • Bayrak M., Esen Ö. (2013), Yapay Sinir Ağları
  • Yöntemiyle Türkiye’nin Enerji Talebi Tahmini ve Enerji Açığı Öngörüsü [2012-2020], Atatürk Üniversitesi 17.
  • Ulusal İktisat Sempozyumu Bayraktutan, Yusuf, Arslan, I., Özkan, G. S. ve Çevik, F. S. (2012a), “Industrial Sector Energy Consumption in
  • Turkey - The Relationship Between Economic Growth (1970-2010)”, Journal of Economics and International
  • Finance, 4 (2), 30-35. http:// dx.doi.org/10.5897/ JEIF11.140
  • Bp, (2013), Bp Energy Outlook 2030, http://www. bp.com/content/dam/bp/pdf/energy-economics/ e n e rg y- o u t l o o k- 2 0 1 5 / b p - e n e rg y- o u t l o o k- booklet_2013.pdf
  • BP, (2016), Energy Outlook 2035, http://www.bp.com/ en/global/corporate/energy-economics/energy- outlook-2035/energy-outlook-to-2035.html
  • Canyurt, E. O., Ceylan, H., Öztürk, H. K., ve Hepıbaşlı, A. (2004) “Energy Demand Estimation Based on Two
  • Different Genetic Algorithm Approaches” Energy Sources, 26: 1313-1320.
  • Ceylan, H. ve Öztürk, H. K. (2004) “Estimating Energy
  • Demand of Turkey Based on Economic Indicatiors Using Genetic Algorithm Approach” Energy Converısion and Management, 45:2525-2537.
  • Ceylan, H., Ceylan, H., Haldenbilen, S. ve Baskan, Ö. (2008) “Transport Energy Modeling With Meta
  • Heuristic Harmony Search Algorithm, An Application to Turkey” Energy Policy, 36:2527-2535.
  • Ceylan, H., Öztürk, H. K., Hepbaşlı, A., ve Utlu, Z. (2005) “Estimating Energy end Exergy Production and Consumption Values Using Three Different Genetic
  • Algorithm Approaches. Part 1: Model Development” Energy Sources, 27:621-627. Ceylan, H., Öztürk, H. K., Hepbaşlı, A., ve Utlu, Z. (2005) “Estimating Energy end Exergy Production and Consumption Values Using Three Different Genetic
  • Algorithm Approaches. Part 1: Model Development” Energy Sources, 27:621-627. Costantini, V. and Martini, C, 2010. “The causality between energy consumption and economic growth:
  • A multi-sectoral analysis using non-stationary cointegrated panel data”, Energy Economics 32, 591– Ediger, V.Ş ve Akar, S. (2007) “ARIMA Forecasting of
  • Primary Energy Demand by Fuel in Turkey” Energy Policy, 35:1701-1708.
  • Erdoğdu, E. (2007) “Electricity Demand Analysis Using
  • Cointegration and ARIMA Modelling: A Case Study of Turkey” Energy Policy, 35:1129-1146.
  • Ersoy, A. Yağmur (2010), “Ekonomik Büyüme
  • Bağlamında Enerji Tüketimi”, Akademik Bakış Dergisi, , 1-11. Es H. A.,Kalender F.Y.ve Hamzaçebi C.(2014), Yapay
  • Sinir Ağları İle Türkiye Net Enerji Talep Tahmini, Gazi Üniv. Müh. Mim. Fak. Der. Cilt 29, No 3, 495-504, Esen, Ö. ve Bayrak, M., (2015), Enerji Açığının
  • Belirleyicilerinin Teorik Perspektiften İncelenmesi, Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, Cilt:3 Sayı:1 Haziran: 2015
  • Fullér, R. 1995. Neural fuzzy systems.
  • Görücü, F.B. ve Gümrah, F. (2004) “Evaluation and Forecasting of Gas Consumption by Statistical
  • Analysis” Energy Sources, 26:267-276. Görücü, F.B., Geriş, P., U. ve Gumrah, F. (2004) “Artificial
  • Neural Network Modeling for Forecasting Gas Consumption” Energy Sources, 26:299-307. Haldenbilen, S. ve Ceylan, H. (2005) “Genetic
  • Algorithm Approach To Estimate Transport Energy Demand In Turkey” Energy Policy, 33:89–98. Hamzaçebi Coşkun ve Kutay Fevzi,(2004),Yapay Sinir
  • Ağları İle Türkiye Elektrik Enerjisi Tüketiminin 2010
  • Yılına Kadar Tahmini, Gazi Üniv. Müh. Mim. Fak. Der. Cilt 19, No 3, 227-233
  • Hirota, K., & Pedrycz, W. (1994a). A distributed model of fuzzy set connectives. Fuzzy Sets and Systems, 68(2), 157–170. http://doi.org/http://dx.doi. org/10.1016/0165-0114(94)90042-6
  • Hirota, K., & Pedrycz, W. (1994b). Or/and Neuron in
  • Modeling Fuzzy Set Connectives. Ieee Transactions on Fuzzy Systems, 2(2), 151–161. http://doi. org/10.1109/91.277963
  • Hotunoğlu H., ve Karakaya E.(2011), Forecasting
  • Turkey’s Energy Demand Using Artificial Neural Networks: Three Scenario Applications Yapay Sinir Ağları Yöntemiyle Türkiye’nin Enerji Talebi Tahmini: Üç Senaryo Uygulaması, Ege Akademik Bakış, Cilt 11 Özel Sayı, 87-94
  • IEA (2016), http://www.iea.org/statistics/ statisticssearch/report/?&country
  • IEA, (2014), World Energy Outlook, 2014, Aralık 2014 Yayın No: TÜSİAD-T/2014/12/564
  • Jang, J. 1991. Fuzzy Modeling Using Generalized
  • Neural Networks and Kalman Filter Algorithm. Proceedings of the 9th National Conference on Artificial Intelligence, 91, 762- Retrieved from http://www.aaai.org/Library/ AAAI/1991/aaai91-119.php
  • Jang, J.-S. R. 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems,
  • Man and Cybernetics, 23(3), 665–685. http://doi. org/10.1109/21.256541
  • Kandel, A. (1991). Fuzzy expert systems. CRC press.
  • Kavaklıoğlu, K., Ceylan, H., Öztürk, H. K. ve Canyurt, O. E. (2009) “Modeling and Prediction of Turkey’s
  • Electricity Consumption Using Artifical Neural Networks” Energy Conversion and Management, 50: 2727.
  • Kaynar O., Taştan S., ve Demirkoparan F. Yapay Sinir Ağları İle Doğalgaz Tüketim Tahmini, Atatürk Ü. İİBF Dergisi, 10. Ekonometri ve İstatistik Sempozyumu Özel Sayısı, 2011
  • Khan, M. A. and Ahmed, U. 2008. “Energy Demand in Pakistan: A Disaggregate Analysis”, The Pakistan
  • Development Review, 47(4),437-455. Mahmutoğlu M., ve Öztürk F.(2015), Türkiye Elektrik
  • Tüketimi Öngörüsü ve Bu Kapsamda Geliştirilebilecek Politika Önerileri, EY International Congress On Economics II “Europe And Global Economıc Rebalancing” Ankara, November 5-6, 2015
  • Murat, Y.,S. ve Ceylan, H. (2006) “Use of Artificial Neural
  • Networks for Transport Energy Demand Modeling” Energy Policy, 34:3165-3172.
  • Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of
  • Hydrology, 291, 52–66. http://doi.org/10.1016/j. jhydrol.2003.12.010
  • OECD, (2016), OECD.stat, http://stats.oecd.org/
  • O E C D . s t a t .h t t p : / / s t a t s . o e c d . o r g / I n d e x . aspx?DatasetCode=POP_FIVE_HIST#http://knoema. com/yxptpab/crude-oil-price-forecast-long-term- to-2025-data-and-charts
  • Öztürk, H. K., Ceylan, H., Canyurt, O. E. ve Hepbaşlı, A. (2005) “Electricity Estimation Using Genetic
  • Algorithm Approach: A Case Study of Turkey” Energy, :1003-1012.
  • Paul, S. And Bhattacharya, R. N. 2004. “Causality
  • Between Energy Consumption and Economic Growth in India: A Note on Conflicting Results”, Energy Economics, 26, 977-983
  • Say N.P. ve Yücel M.(2006), Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth,Volume 34, Issue 18, 3870-3876.
  • Solak A.O.,“Petrol Fiyatlarını Belirleyici Faktörler”, Uluslararası Alanya İşletme Fakültesi Dergisi, 4(2), 124. Sözen A., ve Arcaklioğlu E., ( 2007) “Prediction of Net
  • Energy Consumption Based on Economic Indicators (GNP and GDP) in Turkey” Energy Policy, 35:4981
  • Sözen, A., Arcaklioğlu E. ve Özkaymak, M., (2005),
  • “Turkey’s net energy consumption”, Applied Energy, , pp. 209–221. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control.
  • Systems, Man and Cybernetics, IEEE Transactions on, SMC-15(1), 116–132. http://doi.org/10.1109/ TSMC.1985.6313399.
  • TENVA (Türkiye Enerji Vakfı), 2015, 2030’lara
  • Doğru Türkiye’nin Enerji Görünümü, http://www. tenva.org/2030lara-dogru-turkiyenin-enerji- gorunumu/#prettyPhoto
  • TMMOB(2012), Enerji Verimliliği Raporu, Elektrik
  • Mühendisleri Odası, 42. Dönem Enerji Çalışma Grubu, EMO Yayınları Toksarı, M. D. (2007) “Ant Colony Optimization
  • Approach to Estimate Energy Demand of Turkey” Energy Policy, 35:3984-3990.
  • Tunç, M., Çamdalı, Ü. ve Parmaksızoğlu, C. (2006) “Comparison of Turkey’s Electrical Energy
  • Consumption and Production with Some European Countries and Optimization of Future Electrical Power Supply Investments in Turkey” Energy Policy, :50-59. Ünler, A. (2008) “ Improvement of Energy Demand
  • Forecasts Using Swarm Intelligence: The Case of Turkey with Projections to 2025” Energy Policy, :1937-1944.
  • World Bank (WB), (2016), GDP per capita growth
  • (annual %) http://data.worldbank.org/indicator/ NY.GDP.PCAP.KD.ZG WWF-Türkiye, (2015), Türkiye İçin Düşük Karbonlu
  • Kalkınma Yolları ve Öncelikleri, http://awsassets. wwftr.panda.org/downloads Yumurtacı, Z. ve Asmaz, E. (2004) “Electric Energy
  • Demand of Turkey for the Year 2050”, Energy Sources, :1157-1164.

Türkiye İçin Anfıs Modeli İle Birincil Enerji Talep Tahmini

Year 2017, Volume: 17 Issue: 3, 431 - 446, 01.08.2017

Abstract

Enerji talebi; ekonomik büyüme, enerji fiyatları, nüfus, şehirleşme ve verimlilik gibi bir dizi faktöre bağlıdır. Bu nedenle, ülkelerin iktisadi açıdan gelişiminde enerji talebinin oldukça önemli bir yeri vardır. Buradan hareketle bu çalışmada, enerji talebinin belirleyicisi olan ve literatürde üzerinde en fazla durulan göstergelerden yararlanılarak, 2016-2030 yılları arasında Türkiye’nin enerji talebi, Adaptive Neuro Fuzzy Inference System (ANFIS) yöntemi ile belirlenmeye çalışılmıştır. Söz konusu göstergeler; OECD ülkelerinin 1990-2030 yılları arasındaki dönemi kapsayan büyüme rakamları, nüfus ve enerji fiyatları bağımsız değişkenler olarak ele alınmıştır. Araştırmanın bağımlı değişkeni ise bu ülkelerin birincil enerji talepleridir. Yapılan analiz sonucunda, Türkiye’nin gelecekteki enerji talebinde diğer gelişmiş ülkelere benzer bir şekilde büyük bir artış olabileceği tespit edilmiştir. Ancak petrol ve doğal gaz gibi birincil kaynaklara dayalı enerji talebinde çok ciddi bir değişme olmayacağı sonucuna varılmıştır. Yapılan analizle, literatürde daha önce büyüme ve nüfusa dayalı çalışmalar yapılmış ancak enerji fiyatlarına dayalı olarak kayda değer bir çalışmaya rastlanmamıştır. Diğer değişkenlerle beraber enerji fiyatları da çalışmaya dâhil edilerek literatürdeki bir boşluk doldurulmaya çalışılmıştır

References

  • AbuAl-Foul, B.M, (2012), Forecasting Energy Demand in Jordan Using Artificial Neural Networks, Topics in Middle Eastern and African Economies Vol. 14, September 2012
  • Aydın, F.F., (2010), Enerji Tüketimi ve Ekonomik
  • Büyüme, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 35, Ocak-Temmuz 2010,317
  • Bayrak M., Esen Ö. (2013), Yapay Sinir Ağları
  • Yöntemiyle Türkiye’nin Enerji Talebi Tahmini ve Enerji Açığı Öngörüsü [2012-2020], Atatürk Üniversitesi 17.
  • Ulusal İktisat Sempozyumu Bayraktutan, Yusuf, Arslan, I., Özkan, G. S. ve Çevik, F. S. (2012a), “Industrial Sector Energy Consumption in
  • Turkey - The Relationship Between Economic Growth (1970-2010)”, Journal of Economics and International
  • Finance, 4 (2), 30-35. http:// dx.doi.org/10.5897/ JEIF11.140
  • Bp, (2013), Bp Energy Outlook 2030, http://www. bp.com/content/dam/bp/pdf/energy-economics/ e n e rg y- o u t l o o k- 2 0 1 5 / b p - e n e rg y- o u t l o o k- booklet_2013.pdf
  • BP, (2016), Energy Outlook 2035, http://www.bp.com/ en/global/corporate/energy-economics/energy- outlook-2035/energy-outlook-to-2035.html
  • Canyurt, E. O., Ceylan, H., Öztürk, H. K., ve Hepıbaşlı, A. (2004) “Energy Demand Estimation Based on Two
  • Different Genetic Algorithm Approaches” Energy Sources, 26: 1313-1320.
  • Ceylan, H. ve Öztürk, H. K. (2004) “Estimating Energy
  • Demand of Turkey Based on Economic Indicatiors Using Genetic Algorithm Approach” Energy Converısion and Management, 45:2525-2537.
  • Ceylan, H., Ceylan, H., Haldenbilen, S. ve Baskan, Ö. (2008) “Transport Energy Modeling With Meta
  • Heuristic Harmony Search Algorithm, An Application to Turkey” Energy Policy, 36:2527-2535.
  • Ceylan, H., Öztürk, H. K., Hepbaşlı, A., ve Utlu, Z. (2005) “Estimating Energy end Exergy Production and Consumption Values Using Three Different Genetic
  • Algorithm Approaches. Part 1: Model Development” Energy Sources, 27:621-627. Ceylan, H., Öztürk, H. K., Hepbaşlı, A., ve Utlu, Z. (2005) “Estimating Energy end Exergy Production and Consumption Values Using Three Different Genetic
  • Algorithm Approaches. Part 1: Model Development” Energy Sources, 27:621-627. Costantini, V. and Martini, C, 2010. “The causality between energy consumption and economic growth:
  • A multi-sectoral analysis using non-stationary cointegrated panel data”, Energy Economics 32, 591– Ediger, V.Ş ve Akar, S. (2007) “ARIMA Forecasting of
  • Primary Energy Demand by Fuel in Turkey” Energy Policy, 35:1701-1708.
  • Erdoğdu, E. (2007) “Electricity Demand Analysis Using
  • Cointegration and ARIMA Modelling: A Case Study of Turkey” Energy Policy, 35:1129-1146.
  • Ersoy, A. Yağmur (2010), “Ekonomik Büyüme
  • Bağlamında Enerji Tüketimi”, Akademik Bakış Dergisi, , 1-11. Es H. A.,Kalender F.Y.ve Hamzaçebi C.(2014), Yapay
  • Sinir Ağları İle Türkiye Net Enerji Talep Tahmini, Gazi Üniv. Müh. Mim. Fak. Der. Cilt 29, No 3, 495-504, Esen, Ö. ve Bayrak, M., (2015), Enerji Açığının
  • Belirleyicilerinin Teorik Perspektiften İncelenmesi, Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, Cilt:3 Sayı:1 Haziran: 2015
  • Fullér, R. 1995. Neural fuzzy systems.
  • Görücü, F.B. ve Gümrah, F. (2004) “Evaluation and Forecasting of Gas Consumption by Statistical
  • Analysis” Energy Sources, 26:267-276. Görücü, F.B., Geriş, P., U. ve Gumrah, F. (2004) “Artificial
  • Neural Network Modeling for Forecasting Gas Consumption” Energy Sources, 26:299-307. Haldenbilen, S. ve Ceylan, H. (2005) “Genetic
  • Algorithm Approach To Estimate Transport Energy Demand In Turkey” Energy Policy, 33:89–98. Hamzaçebi Coşkun ve Kutay Fevzi,(2004),Yapay Sinir
  • Ağları İle Türkiye Elektrik Enerjisi Tüketiminin 2010
  • Yılına Kadar Tahmini, Gazi Üniv. Müh. Mim. Fak. Der. Cilt 19, No 3, 227-233
  • Hirota, K., & Pedrycz, W. (1994a). A distributed model of fuzzy set connectives. Fuzzy Sets and Systems, 68(2), 157–170. http://doi.org/http://dx.doi. org/10.1016/0165-0114(94)90042-6
  • Hirota, K., & Pedrycz, W. (1994b). Or/and Neuron in
  • Modeling Fuzzy Set Connectives. Ieee Transactions on Fuzzy Systems, 2(2), 151–161. http://doi. org/10.1109/91.277963
  • Hotunoğlu H., ve Karakaya E.(2011), Forecasting
  • Turkey’s Energy Demand Using Artificial Neural Networks: Three Scenario Applications Yapay Sinir Ağları Yöntemiyle Türkiye’nin Enerji Talebi Tahmini: Üç Senaryo Uygulaması, Ege Akademik Bakış, Cilt 11 Özel Sayı, 87-94
  • IEA (2016), http://www.iea.org/statistics/ statisticssearch/report/?&country
  • IEA, (2014), World Energy Outlook, 2014, Aralık 2014 Yayın No: TÜSİAD-T/2014/12/564
  • Jang, J. 1991. Fuzzy Modeling Using Generalized
  • Neural Networks and Kalman Filter Algorithm. Proceedings of the 9th National Conference on Artificial Intelligence, 91, 762- Retrieved from http://www.aaai.org/Library/ AAAI/1991/aaai91-119.php
  • Jang, J.-S. R. 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems,
  • Man and Cybernetics, 23(3), 665–685. http://doi. org/10.1109/21.256541
  • Kandel, A. (1991). Fuzzy expert systems. CRC press.
  • Kavaklıoğlu, K., Ceylan, H., Öztürk, H. K. ve Canyurt, O. E. (2009) “Modeling and Prediction of Turkey’s
  • Electricity Consumption Using Artifical Neural Networks” Energy Conversion and Management, 50: 2727.
  • Kaynar O., Taştan S., ve Demirkoparan F. Yapay Sinir Ağları İle Doğalgaz Tüketim Tahmini, Atatürk Ü. İİBF Dergisi, 10. Ekonometri ve İstatistik Sempozyumu Özel Sayısı, 2011
  • Khan, M. A. and Ahmed, U. 2008. “Energy Demand in Pakistan: A Disaggregate Analysis”, The Pakistan
  • Development Review, 47(4),437-455. Mahmutoğlu M., ve Öztürk F.(2015), Türkiye Elektrik
  • Tüketimi Öngörüsü ve Bu Kapsamda Geliştirilebilecek Politika Önerileri, EY International Congress On Economics II “Europe And Global Economıc Rebalancing” Ankara, November 5-6, 2015
  • Murat, Y.,S. ve Ceylan, H. (2006) “Use of Artificial Neural
  • Networks for Transport Energy Demand Modeling” Energy Policy, 34:3165-3172.
  • Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of
  • Hydrology, 291, 52–66. http://doi.org/10.1016/j. jhydrol.2003.12.010
  • OECD, (2016), OECD.stat, http://stats.oecd.org/
  • O E C D . s t a t .h t t p : / / s t a t s . o e c d . o r g / I n d e x . aspx?DatasetCode=POP_FIVE_HIST#http://knoema. com/yxptpab/crude-oil-price-forecast-long-term- to-2025-data-and-charts
  • Öztürk, H. K., Ceylan, H., Canyurt, O. E. ve Hepbaşlı, A. (2005) “Electricity Estimation Using Genetic
  • Algorithm Approach: A Case Study of Turkey” Energy, :1003-1012.
  • Paul, S. And Bhattacharya, R. N. 2004. “Causality
  • Between Energy Consumption and Economic Growth in India: A Note on Conflicting Results”, Energy Economics, 26, 977-983
  • Say N.P. ve Yücel M.(2006), Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth,Volume 34, Issue 18, 3870-3876.
  • Solak A.O.,“Petrol Fiyatlarını Belirleyici Faktörler”, Uluslararası Alanya İşletme Fakültesi Dergisi, 4(2), 124. Sözen A., ve Arcaklioğlu E., ( 2007) “Prediction of Net
  • Energy Consumption Based on Economic Indicators (GNP and GDP) in Turkey” Energy Policy, 35:4981
  • Sözen, A., Arcaklioğlu E. ve Özkaymak, M., (2005),
  • “Turkey’s net energy consumption”, Applied Energy, , pp. 209–221. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control.
  • Systems, Man and Cybernetics, IEEE Transactions on, SMC-15(1), 116–132. http://doi.org/10.1109/ TSMC.1985.6313399.
  • TENVA (Türkiye Enerji Vakfı), 2015, 2030’lara
  • Doğru Türkiye’nin Enerji Görünümü, http://www. tenva.org/2030lara-dogru-turkiyenin-enerji- gorunumu/#prettyPhoto
  • TMMOB(2012), Enerji Verimliliği Raporu, Elektrik
  • Mühendisleri Odası, 42. Dönem Enerji Çalışma Grubu, EMO Yayınları Toksarı, M. D. (2007) “Ant Colony Optimization
  • Approach to Estimate Energy Demand of Turkey” Energy Policy, 35:3984-3990.
  • Tunç, M., Çamdalı, Ü. ve Parmaksızoğlu, C. (2006) “Comparison of Turkey’s Electrical Energy
  • Consumption and Production with Some European Countries and Optimization of Future Electrical Power Supply Investments in Turkey” Energy Policy, :50-59. Ünler, A. (2008) “ Improvement of Energy Demand
  • Forecasts Using Swarm Intelligence: The Case of Turkey with Projections to 2025” Energy Policy, :1937-1944.
  • World Bank (WB), (2016), GDP per capita growth
  • (annual %) http://data.worldbank.org/indicator/ NY.GDP.PCAP.KD.ZG WWF-Türkiye, (2015), Türkiye İçin Düşük Karbonlu
  • Kalkınma Yolları ve Öncelikleri, http://awsassets. wwftr.panda.org/downloads Yumurtacı, Z. ve Asmaz, E. (2004) “Electric Energy
  • Demand of Turkey for the Year 2050”, Energy Sources, :1157-1164.
There are 80 citations in total.

Details

Other ID JA62JK44AD
Journal Section Research Article
Authors

Turgut Bayramoğlu This is me

Hakan Pabuçcu This is me

Füsun Çelebi Boz This is me

Publication Date August 1, 2017
Published in Issue Year 2017 Volume: 17 Issue: 3

Cite

APA Bayramoğlu, T., Pabuçcu, H., & Boz, F. . Ç. (2017). Forecasting Primary Energy Demand By Using ANFIS Model for Turkey. Ege Academic Review, 17(3), 431-446.
AMA Bayramoğlu T, Pabuçcu H, Boz FÇ. Forecasting Primary Energy Demand By Using ANFIS Model for Turkey. ear. August 2017;17(3):431-446.
Chicago Bayramoğlu, Turgut, Hakan Pabuçcu, and Füsun Çelebi Boz. “Forecasting Primary Energy Demand By Using ANFIS Model for Turkey”. Ege Academic Review 17, no. 3 (August 2017): 431-46.
EndNote Bayramoğlu T, Pabuçcu H, Boz FÇ (August 1, 2017) Forecasting Primary Energy Demand By Using ANFIS Model for Turkey. Ege Academic Review 17 3 431–446.
IEEE T. Bayramoğlu, H. Pabuçcu, and F. . Ç. Boz, “Forecasting Primary Energy Demand By Using ANFIS Model for Turkey”, ear, vol. 17, no. 3, pp. 431–446, 2017.
ISNAD Bayramoğlu, Turgut et al. “Forecasting Primary Energy Demand By Using ANFIS Model for Turkey”. Ege Academic Review 17/3 (August 2017), 431-446.
JAMA Bayramoğlu T, Pabuçcu H, Boz FÇ. Forecasting Primary Energy Demand By Using ANFIS Model for Turkey. ear. 2017;17:431–446.
MLA Bayramoğlu, Turgut et al. “Forecasting Primary Energy Demand By Using ANFIS Model for Turkey”. Ege Academic Review, vol. 17, no. 3, 2017, pp. 431-46.
Vancouver Bayramoğlu T, Pabuçcu H, Boz FÇ. Forecasting Primary Energy Demand By Using ANFIS Model for Turkey. ear. 2017;17(3):431-46.