Forecasting of Turkey’s Electricity Consumption with Support Vector Regression and Chaotic Particle Swarm Algorithm

Yıl 2017, Cilt: 15 Sayı: 29, 211 - 224, 12.07.2017

Oğuz Kaynar Halil Özekicioğlu Ferhan Demirkoparan

Öz

Energy is a very important factor in terms of sustaining the economic development for developing and industrialized countries. Electricity is one of the most important forms of energy for industrialization and improvement of living standards. The estimation and modeling of electricity consumption has a special importance in Turkey which is a foreign-dependent country in energy. In this study, a forecasting application is made by using Turkey’s electricity consumption, population, import, export and gross domestic product between 1975-2014 employing support vector regression methods. Chaotic particle swarm optimization algorithm CPSO is used to choose the parameters of SVR

Anahtar Kelimeler

Electricity consumption, Support Vector Regression, Chaotic Particle

Kaynakça

• Abosedra, S., Dah, A., & Ghosh, S. (2009). ‘Electricity consumption and economic growth, the case of Lebanon. Applied Energy’, 86(4), 429-432.
• Akay, D., & Atak, M. (2007). ‘Grey prediction with rolling mechanism for electricity de- mand forecasting of Turkey’. Energy, 32(9), 1670-1675.
• Alatas, B., Akin, E., & Ozer, A. B. (2009). ‘Chaos embedded particle swarm optimization algorithms’. Chaos, Solitons & Fractals, 40(4), 1715-1734.
• Altinay, G., & Karagol, E. (2005). ‘Electricity consumption and economic growth: evidence from Turkey’. Energy Economics, 27(6), 849-856.
• Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy us- ing linear regression models. Energy, 34(9), 1413-1421.
• Chen, S. T., Kuo, H. I., & Chen, C. C. (2007). ‘The relationship between GDP and electricity consumption in 10 Asian countries’. Energy Policy, 35(4), 2611-2621.
• Eberhart, R. C., & Kennedy, J. (1995, October). ‘A new optimizer using particle swarm theo- ry’. In Proceedings of the sixth international symposium on micro machine and human science (Vol. 1, pp. 39-43).
• Ekonomou, L. (2010). ‘Greek long-term energy consumption prediction using artificial neu- ral networks’. Energy, 35(2), 512-517.
• Fan, S., Chen, L., & Lee, W. J. (2008). ‘Machine learning based switching model for electric- ity load forecasting’. Energy Conversion and Management, 49(6), 1331-1344.
• Gürbüz, F., Öztürk, C., & Pardalos, P. (2013). ‘Prediction of electricity energy consumption of Turkey via artificial bee colony: a case study’. Energy Systems, 4(3), 289-300.
• Hamzaçebi, C. (2007). ‘Forecasting of Turkey’s net electricity energy consumption on sec- toral bases’. Energy Policy, 35(3), 2009-2016.
• Hefny, H. A., & Azab, S. S. (2010, March). ‘Chaotic particle swarm optimization’. In Infor- matics and Systems (INFOS), 2010 The 7th International Conference on (pp. 1-8). IEEE.
• Hong, W. C. (2009a). ‘Electric load forecasting by support vector model’. Applied Math- ematical Modelling, 33(5), 2444-2454.
• Hong, W. C. (2009b). ‘Chaotic particle swarm optimization algorithm in a support vec- tor regression electric load forecasting model’. Energy Conversion and Management, 50(1), 105-117.
• Hong, W. C. (2010). ‘Application of chaotic ant swarm optimization in electric load forecas- ting’. Energy Policy, 38(10), 5830-5839.
• Hong, W. C. (2013). Intelligent energy demand forecasting. New York: Springer.
• Hu, Z., Bao, Y., & Xiong, T. (2013). ‘Electricity load forecasting using support vector regres- sion with memetic algorithms’. The Scientific World Journal, 2013.
• Kavaklioglu, K., Ceylan, H., Ozturk, H. K., & Canyurt, O. E. (2009). ‘Modeling and pre- diction of Turkey’s electricity consumption using artificial neural networks’. Energy Conversion and Management, 50(11), 2719-2727.
• Kavaklioglu, K. (2011). ‘Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression’. Applied Energy, 88(1), 368-375.
• Kavaklioglu, K. (2014). ‘Robust electricity consumption modeling of Turkey using singular value decomposition’. International Journal of Electrical Power & Energy Systems, 54, 268- 276.
• Kucukali, S., & Baris, K. (2010). ‘Turkey’s short-term gross annual electricity demand fore- cast by fuzzy logic approach’. Energy Policy, 38(5), 2438-2445.
• Li, Y. B., Zhang, N., & Li, C. B. (2009). ‘Support vector machine forecasting method imp- roved by chaotic particle swarm optimization and its application’. Journal of Central South University of Technology, 16, 478-481.
• Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). ‘Improved particle swarm opti- mization combined with chaos’. Chaos, Solitons & Fractals, 25(5), 1261-1271.
• Narayan, P. K., & Smyth, R. (2009). ‘Multivariate Granger causality between electricity consumption, exports and GDP: evidence from a panel of Middle Eastern countries’. Energy Policy, 37(1), 229-236.
• Oğcu, G., Demirel, O. F., & Zaim, S. (2012). ‘Forecasting electricity consumption with neu- ral networks and support vector regression’. Procedia-Social and Behavioral Sciences, 58, 1576-1585.
• Pao, H. T. (2009).’ Forecast of electricity consumption and economic growth in Taiwan by state space modeling’. Energy, 34(11), 1779-1791.
• Song, Y., Chen, Z., & Yuan, Z. (2007). ‘New chaotic PSO-based neural network predictive control for nonlinear process’. IEEE Transactions on Neural Networks, 18(2), 595-601.
• Sözen, A., & Arcaklioglu, E. (2007). ‘Prediction of net energy consumption based on eco- nomic indicators (GNP and GDP) in Turkey’. Energy policy, 35(10), 4981-4992.
• Toksarı, M. D. (2007). ‘Ant colony optimization approach to estimate energy demand of Turkey’. Energy Policy, 35(8), 3984-3990.
• Türedi S., Berber M. (2007). ‘Enerji Tüketimi ve Ekonomik Büyüme İlişkisi Uzun Dönem Analizi: Türkiye Örneği (1976-2005)’, İkinci Uluslararası İşletme ve Ekonomi Çalıştayı, Giresun, Türkiye.
• Vapnik, V. (1995) The Nature of Statistic Learning Theory, Springer–Verlag, New York, 1995.
• Wu, Q. (2010). ‘A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization’. Expert Systems with Applications, 37(3), 2388- 2394.
• Yang, H. Y. (2000). ‘A note on the causal relationship between energy and GDP in Taiwan’. Energy economics, 22(3), 309-317.
• Yoo, S. H. (2006). ‘The causal relationship between electricity consumption and economic growth in the ASEAN countries’. Energy policy, 34(18), 3573-3582.