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Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach

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  • Kucukali, Serhat
  • Baris, Kemal
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    Abstract

    This paper aims to forecast Turkey's short-term gross annual electricity demand by applying fuzzy logic methodology while general information on economical, political and electricity market conditions of the country is also given. Unlike most of the other forecast models about Turkey's electricity demand, which usually uses more than one parameter, gross domestic product (GDP) based on purchasing power parity was the only parameter used in the model. Proposed model made good predictions and captured the system dynamic behavior covering the years of 1970-2014. The model yielded average absolute relative errors of 3.9%. Furthermore, the model estimates a 4.5% decrease in electricity demand of Turkey in 2009 and the electricity demand growth rates are projected to be about 4% between 2010 and 2014. It is concluded that forecasting the Turkey's short-term gross electricity demand with the country's economic performance will provide more reliable projections. Forecasting the annual electricity consumption of a country could be made by any designer with the help of the fuzzy logic procedure described in this paper. The advantage of this model lies on the ability to mimic the human thinking and reasoning.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Policy.

    Volume (Year): 38 (2010)
    Issue (Month): 5 (May)
    Pages: 2438-2445

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    Handle: RePEc:eee:enepol:v:38:y:2010:i:5:p:2438-2445

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    Web page: http://www.elsevier.com/locate/enpol

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    Keywords: Electricity demand Turkey Fuzzy logic;

    References

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    1. Sanders, I. & Batty, W. J. & Probert, S. D. & Hagino, K. & Aida, S., 1993. "Supply of, and demand for, a resource: Fuzzy logistical optimisation technique," Applied Energy, Elsevier, vol. 46(4), pages 285-302.
    2. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    3. Erdogdu, Erkan, 2007. "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, Elsevier, vol. 35(2), pages 1129-1146, February.
    4. Tunc, Murat & Camdali, Unal & Parmaksizoglu, Cem, 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, Elsevier, vol. 34(1), pages 50-59, January.
    5. Yoo, Seung-Hoon & Kwak, So-Yoon, 2010. "Electricity consumption and economic growth in seven South American countries," Energy Policy, Elsevier, vol. 38(1), pages 181-188, January.
    6. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    7. Mahadevan, Renuka & Asafu-Adjaye, John, 2007. "Energy consumption, economic growth and prices: A reassessment using panel VECM for developed and developing countries," Energy Policy, Elsevier, vol. 35(4), pages 2481-2490, April.
    8. Hamzacebi, Coskun, 2007. "Forecasting of Turkey's net electricity energy consumption on sectoral bases," Energy Policy, Elsevier, vol. 35(3), pages 2009-2016, March.
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    Cited by:
    1. Serhat, Kucukali, 2011. "Risk assessment of river-type hydropower plants using fuzzy logic approach," Energy Policy, Elsevier, vol. 39(10), pages 6683-6688, October.
    2. Mondal, Md. Alam Hossain & Boie, Wulf & Denich, Manfred, 2010. "Future demand scenarios of Bangladesh power sector," Energy Policy, Elsevier, vol. 38(11), pages 7416-7426, November.
    3. Rentizelas, Athanasios & Georgakellos, Dimitrios, 2014. "Incorporating life cycle external cost in optimization of the electricity generation mix," Energy Policy, Elsevier, vol. 65(C), pages 134-149.
    4. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.
    5. Zhao, Weigang & Wang, Jianzhou & Lu, Haiyan, 2014. "Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model," Omega, Elsevier, vol. 45(C), pages 80-91.
    6. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.

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