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Future Electricity Demand of the Emerging European Countries and the CIS Countries

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  • Mehmet Fatih Bayramoglu

    (Bulent Ecevit University, Department of Business Administration, Zonguldak)

Abstract

Nowadays, one of the leading factors used in the evaluation of a country’s economic development is energy consumption. Because of economic growth, demand for energy is also increasing. In this study, the emerging European countries’ (the Czech Republic, Poland, Romania, Turkey) and the CIS countries’ (Kazakhstan, Russia, Ukraine, Uzbekistan) electricity consumption has been forecasted for five years period (2015-2019). In the study, GM(1,1) Rolling Model, which is developed in the framework of Grey System Theory is used as a mathematical model for real-time forecasting. The results of the study show that there will not be a significant change in electricity demand in this two area during the 2015-2109 period.

Suggested Citation

  • Mehmet Fatih Bayramoglu, 2016. "Future Electricity Demand of the Emerging European Countries and the CIS Countries," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 5(6), pages 15-23, October.
    • Handle: RePEc:rbs:ijfbss:v:5:y:2016:i:6:p:15-23
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    References listed on IDEAS

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    1. Ming Meng & Dongxiao Niu & Wei Sun, 2011. "Forecasting Monthly Electric Energy Consumption Using Feature Extraction," Energies, MDPI, vol. 4(10), pages 1-13, September.
    2. Mr. Clinton R. Shiells & Mr. John R Dodsworth & Mr. Paul Henri Mathieu, 2002. "Cross-Border Issues in Energy Trade in the CIS Countries," IMF Policy Discussion Papers 2002/013, International Monetary Fund.
    3. Hamidreza Mostafaei & Shaghayegh Kordnoori, 2012. "Hybrid Grey Forecasting Model for Iran s Energy Consumption and Supply," International Journal of Energy Economics and Policy, Econjournals, vol. 2(3), pages 97-102.
    4. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio & Minea, Alina A., 2010. "Analysis and forecasting of nonresidential electricity consumption in Romania," Applied Energy, Elsevier, vol. 87(11), pages 3584-3590, November.
    5. Ping Jiang & Qingping Zhou & Haiyan Jiang & Yao Dong, 2014. "An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-13, June.
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