YAPAY SİNİR AĞLARI İLE TÜRKİYE NET ENERJİ TALEP TAHMİNİ

Yıl 2014, , 0 - , 30.09.2014

Hüseyin Es , F. Yeşim Kalender , Coşkun Hamzaçebi

https://doi.org/10.17341/gummfd.41725

Cited By: 27

Öz

Bu çalışmada, yapay sinir ağları (YSA) ile Türkiye net enerji talebi tahmin edilmiştir. Türkiye net enerji talebini tahmin etmek için 1970-2010 yılları arasındaki Gayri Safi Yurtiçi Hâsıla (GSYH) , nüfus, ithalat, ihracat, bina yüz ölçümü ve taşıt sayısı değişken verileri YSA modelinin girdisi olarak kullanılmıştır. Kurulan YSA modelinin tahmin performansı, çoklu doğrusal regresyon tekniği ile karşılaştırmalı olarak ortaya konmuştur. Yapılan karşılaştırmalar, YSA’nın üstünlüğünü göstermektedir. Kabul edilebilir ve yüksek doğruluktaki YSA modeli ile 2011-2025 yılları arası Türkiye net enerji talebi tahmin edilmiştir.

Anahtar Kelimeler

YSA, Enerji, Çoklu doğrusal regresyon

Kaynakça

• Türkiye İstatistik Kurumu, “İstatistik Göstergeler 1923-2010”, Ankara, 2011.
• Türkiye Cumhuriyeti Enerji ve Tabii Kaynaklar Bakanlığı, “2011 Yılı Faaliyet Raporu”, Ankara, 2012.
• Türkiye Cumhuriyeti Enerji ve Tabii KaynaklarBakanlığı“Enerji”http://www.enerji.gov.tr/index.php?sf=webpages&b=enerji, 2013.
• Hamzaçebi, C., Yapay Sinir Ağları: Tahmin Amaçlı Kullanımı Matlab ve NeurosolutionUygulamalı, Ekin Yayınevi, Bursa, 2011.
• Himanshu, A.A. ve Lester, C.H., “Electricity demand for Sri Lanka: a time series analysis”, Energy, Cilt 33, No 5, 724-739, 2008.
• Akay, D. ve Atak, M., “Grey prediction with rolling mechanism for electricity demand forecasting of Turkey”, Energy, Cilt 32, No 9, 1670-1675, 2007.
• Bessec, M. ve Fouquau, J., “The non-linear link between electricity consumption and temperature in Europe: a threshold panel approach” Energy Economics, Cilt 30, No 5, 2705-2721, 2008.
• Wang, J., Zhu, S., Zhang, W. ve Lu, H., “Combined modeling for electric load forecasting with adaptive particle swarm optimization” Energy, Cilt 35, No 4, 1671-1678, 2010.
• Lee, Y.S. ve Tong, L.I., “Forecasting energy consumption using a grey model improved by incorporating genetic programming”, Energy Conversion and Management, Cilt 52, No 1, 147-152, 2011.
• Küçükali, S. ve Barış, K., “Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach”, Energy Policy, Cilt 38, No 5, 2438-2445, 2010.
• Hamzaçeb,i C., “Forecasting of Turkey’s net electricity energy consumption on sectoral bases”,Energy Policy, Cilt 35, No 3, 2009-2016, 2007.
• Suganthi, L. ve Samuel, A.A,” Energy models for demand forecasting-A review”, Renewable and Sustainable Energy Reviews, Cilt 16, No 2, 1223-1240 2012.
• Sözen, A., Arcaklıoğlu, E. ve Özkaymak, M., “Turkey’s net energy consumption”, Applied Energy, Cilt 81, No 2, 209-221, 2005.
• Sözen, A. ve Arcaklıoğlu, E.,” Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey”, Energy Policy, Cilt 35, No 10, 4981-4992, 2007.
• Pao, H.T., “Comparing linear and nonlinear forecasts for Taiwan's electricity consumption”, Energy, Cilt 31, No 12, 2129-2141, 2006.
• Kavaklıoğlu, K., Ceylan, H., Öztürk, H.K. ve Canyurt, O.E., “Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks”, Energy Conversion and Management, Cilt 50, No 11, 2719-2727, 2009.
• Geem, Z.W. ve Roper, W.E., “Energy demand estimation of South Korea using artificial neural network”, Energy Policy, Cilt 37, No 10, 4049-4054, 2009.
• Ekonomou, L., “Greek long-term energy consumption prediction using artificial neural networks”, Energy, 35 : 512-517 (2010).
• Limanond, T., Jomnonkwao, S. ve Srikaew, A.,” Projection of future transport energy demand of Thailand”, Energy Policy, Cilt 39, No 5, 2754-2763, 2011.
• Kankal, M., Akpınar, A., Kömürcü, M.İ. ve Özşahin, T.Ş., “Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables“, Applied Energy, Cilt 88, No 5, 1927-1939, 2011.
• Geem, Z.W., ”Transport energy demand modeling of South Korea using artificial neural network”, Energy Policy, Cilt 39, No 8, 4644-4650, 2011.
• Bilgili M., Şahin B., Yaşar A. ve Şimşek E., “Electric energy demands of Turkey in residential and industrial sectors” , Renewable and Sustainable Energy Reviews, Cilt 16, No 1, 404-414, 2012.
• Ceylan, H. ve Öztürk, H.K., “Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach” Energy Conversion and Management, Cilt 45, No 15-16, 2525–2537, 2004.
• Hamzaçebi, C. ve Kutay, F.,” Yapay Sinir Ağları İle Türkiye Elektrik Enerjisi Tüketiminin 2010 Yılına Kadar Tahmini”, Journal of The Faculty of Engineering and Architecture of Gazi University, Cilt 19, No 3, 227-233 2004.
• Sözen, A., Arcaklıoğlu, E. ve Özalp, M., “Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data” , Energy Conversion and Management, Cilt 45, No 18-19, 3033-3052, 2004.
• Öztürk, H.K., Ceylan, H., Hepbaşlı, A. ve Utlu, Z., “Estimating petroleum exergy production and consumption using vehicle ownership and GDP based on genetic algorithm approach”, Renewable and Sustainable Energy Reviews, Cilt 8, No 3, 289-302, 2004.
• Haldenbilen, S. ve Ceylan, H., “Genetic algorithm approach to estimate transport energy demand in Turkey” Energy Policy, Cilt 33, No 1, 89-98, 2005.
• Ediger, V.Ş., Akar, S. ve Uğurlu, B., “Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model”, Energy Policy, Cilt 34, No 18,3836-3846, 2006.
• Murat, Y.S. ve Ceylan, H., “Use of artificial neural networks for transport energy demand modeling”, Energy Policy, Cilt 34, No 17, 3165–3172, 2006.
• Ediger, V.Ş. ve Akar, S., “ARIMA forecasting of primary energy demand by fuel in Turkey” , Energy Policy, Cilt 35, No 3, 1701-1708, 2007.
• Erdoğdu, E., “Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey”,Energy Policy, Cilt 35, No 2, 1129-1146, 2007.
• Toksarı, M.D., “Ant colony optimization approach to estimate energy demand of Turkey”, Energy Policy, Cilt 35, No 8, 3984-3990, 2007.
• Canyurt, O.E. ve Öztürk, H.K., “Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey”, Energy Policy, Cilt 36, No 7, 2562-2569, 2008.
• Ünler, A., “Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025”, Energy Policy, Cilt 36, No 6, 1937-1944, 2008.
• Toksarı, M.D., “Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey”, Energy Policy, Cilt 37, No 3, 1181-1187, 2009.
• Çınar, D., Kayakutlu, G. ve Daim, T., “Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey”, Energy, Cilt 35, No 4, 1724-1729, 2010.
• Demirel, Ö., Kakilli, A. ve Tektaş, M., “Anfis ve Arma Modelleri İle Elektrik EnerjisiYük Tahmini”, Journal of The Faculty of Engineering and Architecture of Gazi University., Cilt 25, No 3, 601-610, 2010.
• Erdoğdu, E., “Natural gas demand in Turkey”, Applied Energy, Cilt 87, No 1, 211-219, 2010.
• Filik, Ü.B., Gerek, Ö.N. ve Kurban, M., “A novel modeling approach for hourly forecasting of long-term electric energy demand”, Energy Conversion and Management, Cilt 52, No 1, 199-211, 2011.
• Kavaklıoğlu, K., “Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression”, Applied Energy, Cilt 88, No 1, 368-375, 2011.
• Dilaver Z. ve Lester C. H., “Industrial electricity demand for Turkey: A structural time series analysis”, Energy Economics, Cilt 33, No 3, 426-436, 2011.
• Kıran, M.S., Özceylan, E., Gündüz, M. ve Paksoy, T.,”A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey”, Energy Conversion and Management, Cilt 53, No 1, 75-83, 2012.
• Fausett, L., Fundamentals of Neural Networks, 3, Prentice-Hall, New Jersey, 1994.
• Zhang, G., Patuwo, B.E. ve Hu, M.Y., “Forecasting with Artificial Neural Networks:The State of the Art”, International Journal of Forecasting, Cilt 14, No 1, 35-62, 1998.
• Lin, C.T. ve Hsu, P.F., “Forecast of non-alcoholic beverage sales in Taiwan using the grey theory”, Asia Pacific Journal of Marketing and Logistics, Cilt 14, No 4, 3-12, 2002.
• Kabak, M. ve Dağdeviren, M., “Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology”, Energy Conversion and Management, Cilt 79, 25-33, 2014.
• Yüksel, İ., “Global warming and environmental benefits of hydroelectric for sustainable energy in Turkey”, Renewable and Sustainable Energy Reviews, Cilt 16, No 6, 3816-3825, 2012.
• Hamzaçebi, C. ve Es, H.A., “Forecasting the annual electricity consumption of Turkey using an optimized grey model”, Energy, Cilt 70, 165-171, 2014.
• Balat, M., “Security of energy supply in Turkey: Challenges and solutions”, Energy Conversion and Management, Cilt 51, No 10, 1998-2011, 2010.
• Şimşek, H.A. ve Şimşek, N., “Recent incentives for renewable energy in Turkey” Energy Policy, Cilt 63, 521-530, 2013.