• ISSN: 2148-2225 (online)

Ulaştırma ve Lojistik Kongreleri

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods

bib

Mehmet Kayakuş, Ph.D.


Abstract

Energy demand and consumption are very important for the development and progress of countries. Energy demand is increasing rapidly day by day, especially in developing countries. Energy policies should be determined correctly to sustain the industry sector and make the right investments. Forecasting energy demand in the near and long term is important for the strategy that countries will follow. In this study, by using the monthly electricity energy data realized in Turkey between January 2016 and March 2020 and other data affecting this, a model to estimate electrical energy consumption was developed. In this model, artificial neural networks (ANN) and support vector regression (SVR) were used as methods. This study used 15 independent variables as the input value, and Turkey's energy consumption value as the dependent variable was estimated. Correlation, coefficient of determination, MAE, MSE, RMSE, and MAPE statistical methods were used to measure success and error rate, and both models were found to have acceptable error values and success estimation rates. According to the results, it was concluded that the ANN method was more successful than the SVR method.

Keywords: Artificial Neural Networks, Energy Consumption, Support Vector Regression

Jel Classification: C46

Suggested citation

Kayakuş, M. (). The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods. Alphanumeric Journal, 8(2), 227-236. http://dx.doi.org/10.17093/alphanumeric.756651

References

  • Akay, D., & Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), 1670-1675.
  • Ali, S., & Smith-Miles, K. A. (2006). Improved Support Vector Machine Generalization Using Normalized Input Space, Berlin, Heidelberg.
  • Asilkan, Ö., & IRMAK, S. (2009). İkinci el otomobillerin gelecekteki fiyatlarının yapay sinir ağları ile tahmin edilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(2), 375-391.
  • Ataseven, B. (2013). Yapay Sinir Ağları İle Öngörü Modellemesi. Öneri Dergisi, 10(39), 101-115.
  • Bayramoğlu, T., Pabuçcu, H., & Boz, F. Ç. (2017). Türkiye için anfis modeli ile birincil enerji talep tahmini. Ege Akademik Bakis, 17(3), 431-445.
  • Bilgili, M., Sahin, B., Yasar, A., & Simsek, E. (2012). Electric energy demands of Turkey in residential and industrial sectors. Renewable and Sustainable Energy Reviews, 16(1), 404-414.
  • Bishop, C. M. (1995). Neural networks for pattern recognition: Oxford university press.
  • Çınar, D. (2007). Hidroelektrik enerji üretiminin hibrid bir model ile tahmini. Fen Bilimleri Enstitüsü,
  • Çomak, E. (2008). Destek vektör makinelerinin etkin eğitimi için yeni yaklaşımlar. Selçuk Üniversitesi Fen Bilimleri Enstitüsü,
  • Çuhadar, M., Güngör, İ., & Göksu, A. (2009). Turizm Talebinin Yapay Sinir Ağlari İle Tahmini Ve Zaman Serisi Yöntemleri İle Karşilaştirmali Analizi: Antalya İline Yönelik Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1), 99-114.
  • Demirel, Ö., Kakilli, A., & Tektaş, M. (2010). Anfis ve arma modelleri ile elektrik enerjisi yük tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 25(3).
  • Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517.
  • Es, H. A., Kalender, F. Y., & Hamzaçebi, C. (2014). Yapay sinir ağlari ile Türkiye net enerji talep tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 29(3), 495-504.
  • Fidan, U., Uzunhisarcıklı, E., & Çalıkuşu, İ. (2019). Classification of Dermatological Data with Self Organizing Maps and Support Vector Machine. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 19(3), 894-901.
  • Gallant, S. I., & Gallant, S. I. (1993). Neural network learning and expert systems: MIT press.
  • Geem, Z. W. (2011). Transport energy demand modeling of South Korea using artificial neural network. Energy policy, 39(8), 4644-4650.
  • Geem, Z. W., & Roper, W. E. (2009). Energy demand estimation of South Korea using artificial neural network. Energy policy, 37(10), 4049-4054.
  • Jung, H. C., Kim, J. S., & Heo, H. (2015). Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach. Energy and buildings, 90, 76-84.
  • Kankal, M., Akpınar, A., Kömürcü, M. İ., & Özşahin, T. Ş. (2011). Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88(5), 1927-1939.
  • Karaca, C., & Karacan, H. (2016). Çoklu regresyon metoduyla elektrik tüketim talebini etkileyen faktörlerin incelenmesi.
  • Kavaklioglu, K. (2011). Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression. Applied Energy, 88(1), 368-375.
  • Kavaklioglu, K., Ceylan, H., Ozturk, H. K., & Canyurt, O. E. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management, 50(11), 2719-2727.
  • Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with applications, 37(1), 479-489.
  • Kocadayi, Y., Erkaymaz, O., & Uzun, R. (2017). Yapay sinir ağları ile Tr81 bölgesi yıllık elektrik enerjisi tüketiminin tahmini. BİLDİRİ ÖZETLERİ KİTABI, 239.
  • Küçüksille, E. U., & Ateş, N. (2013). Destek Vektör Makineleri ile Yaramaz Elektronik Postaların Filtrelenmesi. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 6(1), 1-7.
  • Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting: Butterworth-Heinemann.
  • Limanond, T., Jomnonkwao, S., & Srikaew, A. (2011). Projection of future transport energy demand of Thailand. Energy policy, 39(5), 2754-2763.
  • Özden, S., & Öztürk, A. (2018). Yapay sinir ağları ve zaman serileri yöntemi ile bir endüstri alanının (ivedik OSB) elektrik enerjisi ihtiyaç tahmini. Bilişim Teknolojileri Dergisi, 11(3), 255-261.
  • Pao, H.-T. (2006). Comparing linear and nonlinear forecasts for Taiwan's electricity consumption. Energy, 31(12), 2129-2141.
  • Shen, J., Pei, Z., Fisher, G., & Lee, E. (2006). Modelling and analysis of waviness reduction in soft-pad grinding of wire-sawn silicon wafers by support vector regression. International journal of production research, 44(13), 2605-2623.
  • Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality—a case study. Ecological Modelling, 220(6), 888-895.
  • Slaughter, G. E., & Hobson, R. S. (2006). Artificial Neural Network for Temporal Impedance Recognition of Neurotoxins. Paper presented at the The 2006 IEEE International Joint Conference on Neural Network Proceedings.
  • Sözen, A., & Arcaklioglu, E. (2007). Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy policy, 35(10), 4981-4992.
  • Sözen, A., Arcaklioğlu, E., & Özkaymak, M. (2005). Turkey’s net energy consumption. Applied Energy, 81(2), 209-221.
  • Vapnik, V. (1995). The Nature of Statistical Learning Theory Springer, 2000. Google Scholar Google Scholar Digital Library Digital Library.
  • Wang, S., Yu, L., Tang, L., & Wang, S. (2011). A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy, 36(11), 6542-6554.
  • Witt, S. F., & Witt, C. A. (1992). Modeling and forecasting demand in tourism: Academic Press Ltd.
  • Yiğit, V. (2011). Genetik algoritma ile Türkiye net elektrik enerjisi tüketiminin 2020 yılına kadar tahmini. International Journal of Engineering Research and Development, 3(2), 37-41.
  • Yüzük, F. (2019). Çoklu regresyon analizi ve yapay sinir ağları ile Türkiye enerji talep tahmini. (Master thesis), Sivas Cumhuriyet Üniversitesi, Sivas.

Volume 8, Issue 2, 2020

2020.08.02.OR.03

alphanumeric journal

Volume 8, Issue 2, 2020

Pages 227-236

Received: June 23, 2020

Accepted: Nov. 25, 2020

Published: Dec. 31, 2020

Full Text [543.3 KB]

2020 Kayakuş, M.

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Creative Commons Attribution licence

scan QR code to access this article from your mobile device

Contact Us

Faculty of Transportation and Logistics, Istanbul University
Beyazit Campus 34452 Fatih/Istanbul/TURKEY

Bahadır Fatih Yıldırım, Ph.D.
editor@alphanumericjournal.com
+ 90 (212) 440 00 00 - 13219

alphanumeric journal

alphanumeric journal has been publishing as "International Peer-Reviewed Journal" every six months since 2013. alphanumeric serves as a vehicle for researchers and practitioners in the field of quantitative methods, and is enabling a process of sharing in all fields related to the operations research, statistics, econometrics and management informations systems in order to enhance the quality on a globe scale.