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Year 2022, Volume: 6 Issue: 3, 420 - 435, 30.09.2022
https://doi.org/10.30521/jes.1106313

Abstract

References

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  • [26] Kaytez, F, Taplamacioglu, MC, Cam, E, Hardalac, F. Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. Int. Journal of Electrical Power & Energy Systems 2015; 67: 43.
  • [27] Kavcıoğlu, Ş. Yenilenebilir Enerji ve Türkiye [Renewable Energy and Türkiye]. Finansal Araştırmalar ve Çalışmalar Dergisi 2019; 11(21): 209-227.
  • [28] Akman, T, Yılmaz, C, Sönmez, Y. Elektrik Yükü Tahmin Yöntemlerinin Analizi [Analysis of Electrical Load Forecasting Methods]. Gazi Mühendislik Bilimleri Dergisi 2018; 4(3): 168-175.
  • [29] Akpınar, M., Yumuşak, N. Günlük temelli orta vadeli şehir doğal gaz talebinin tek değişkenli istatistik teknikleri ile tahmini [Daily basis mid-term demand forecast of city natural gas using univariate statistical techniques]. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2020; 35(2): 725-742.
  • [30] Box, GE, Jenkins, GM, Reinsel, GC, Ljung, GM. Time series analysis: Forecasting and control. John Wiley & Sons, 2015.
  • [31] Montgomery, DC, Jennings, CL, Kulahci, M. Introduction to time series analysis and forecasting. John Wiley & Sons, 2015.

Forecasting of monthly electricity generation from the conventional and renewable resources following the corona virus pandemic in Turkey

Year 2022, Volume: 6 Issue: 3, 420 - 435, 30.09.2022
https://doi.org/10.30521/jes.1106313

Abstract

In the present paper, a forecasting study on the monthly electricity generation of Türkiye from the conventional and renewable resources is performed. The effect of the CoVid-19 pandemic on the sector has been considered. For this aim, the trend before the pandemic has been initially considered and later the post-pandemic situation has been handled. It has been observed that the electricity generation supply/demand mechanism changes drastically compared to the pre- and post-pandemic cases. The rate of the generation from the renewable resources especially shows a sharp variation compared to the rates from the fossil fuels. According to the forecasting scenario, in 2021, the electricity generation shows different attitudes with regard to the resources used. In 2022, especially increasing trends are expected for wind, biogas, natural gas, imported coal and fuel oil, whereas diesel and mineral coal are expected to be decreased in Türkiye.

References

  • [1] Dinç, BÇ, Fahrioglu, M, Batunlu, C. Techno-economic analysis of a transmission line between an island grid and a mainland grid. Journal of Energy Systems 2021; 5(3), 186-198.
  • [2] Eroğlu, H, Erdem, C. Solar energy sector under the influence of Covid-19 pandemic: A critical review. Journal of Energy Systems 2021; 5(3), 244-251.
  • [3] Almeshaiei, E, Soltan, H. A methodology for electric power load forecasting. Alexandria Engineering Journal 2021; 50(2): 137-144.
  • [4] Haleem, A, Javaid, M, Vaishya, R. Effects of COVID-19 pandemic in daily life. Current medicine research and practice 2020; 10(2): 78.
  • [5] Chaturvedi, K, Vishwakarma, D. K, Singh, N. COVID-19 and its impact on education, social life and mental health of students: A survey. Children and youth services review 2021; 121: 105866.
  • [6] Zambrano-Monserrate, M. A, Ruano, MA, Sanchez-Alcalde, L. Indirect effects of COVID-19 on the environment. Science of the total environment 2020;728; 138813.
  • [7] Hiemstra, AF, Rambonnet, L, Gravendeel, B, Schilthuizen, M. The effects of COVID-19 litter on animal life. Animal Biology 2021; 71(2); 215-231.
  • [8] Bahmanyar, A, Estebsari, A, Ernst, D. The impact of different COVID-19 containment measures on electricity consumption in Europe. Energy Research & Social Science 2020; 68; 101683.
  • [9] Le Quéré, C, Jackson, RB, Jones, MW, Smith, AJ, Abernethy, S, Andrew, RM, Peters, GP. Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement. Nature climate change 2020; 10(7); 647-653.
  • [10] Gillingham, KT, Knittel, CR, Li, J, Ovaere, M, Reguant, M. The Short-run and Long-run Effects of Covid-19 on Energy and the Environment. Joule 2020; 4(7): 1337-1341.
  • [11] Wang, Q, Lu, M, Bai, Z, Wang, K. Coronavirus pandemic reduced China’s CO2 emissions in short-term, while stimulus packages may lead to emissions growth in medium-and long-term. Applied energy 2020; 278: 115735.
  • [12] Magazzino, C, Mele, M, Schneider, N. The relationship between air pollution and COVID-19-related deaths: an application to three French cities. Applied Energy 2020; 279: 115835.
  • [13] Sayed, K, Gabbar, HA. SCADA and smart energy grid control automation. In Smart energy grid engineering Academic Press 2017; 481-514.
  • [14] Hossein Motlagh, N, Mohammadrezaei, M, Hunt, J, Zakeri, B. Internet of Things (IoT) and the energy sector. Energies 2020; 13(2): 494.
  • [15] Bizon, N, Tabatabaei, NM, Blaabjerg, F, Kurt, E. Energy harvesting and energy efficiency. In Technology, Methods, and Applications, vol. 37 of Lecture Notes in Energy. Springer International Publishing; 2017.
  • [16] Özkurt, N, Öztura, H, Güzeliş, C. Electricity energy forecasting for Turkey: A review of the years 2003–2020. Turkish Journal of Electrical Power and Energy Systems. 2021; 1: 118-128.
  • [17] Topalli, AK, Erkmen, I. A hybrid learning for neural networks applied to short term load forecasting. Neurocomputing 2003; 51: 495-500.
  • [18] Topalli, AK, Erkmen, I, Topalli, I. Intelligent short-term load forecasting in Turkey. International Journal of Electrical Power & Energy Systems 2006; 28(7): 437-447.
  • [19] Hamzaçebi, C, Kutay, F. Yapay sinir ağlari ile Türkiye elektrik enerjisi tüketiminin 2010 yilina kadar Tahmini [Estimation of electric energy consumption in Turkey until 2010 with artificial neural networks]. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 2004; 19(3): 227-233.
  • [20]Yalcinoz, T, Eminoglu, U. Short term and medium term power distribution load forecasting by neural networks. Energy Conversion and Management 2005; 46(9-10): 1393-1405.
  • [21] Kavaklioglu, K, Ceylan, H, Ozturk, HK., Canyurt, OE. Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management 2009; 50(11): 2719-2727.
  • [22] Demirel, Ö, Kakilli, A, Tektaş, M. Anfis ve arma modelleri ile elektrik enerjisi yük tahmini [Electrical energy load estimation with Anfis and Arma models]. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2010; 25(3):601-610.
  • [23] Kucukali, S, Baris, K. Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach. Energy policy 2010; 38(5): 2438-2445.
  • [24] Kıran, MS, Özceylan, E, Gündüz, M, Paksoy, T. Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems 2012; 36: 93-103.
  • [25] Tutun, S, Chou, CA, Canıyılmaz, E. A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey. Energy 2015; 93: 2406-2422.
  • [26] Kaytez, F, Taplamacioglu, MC, Cam, E, Hardalac, F. Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. Int. Journal of Electrical Power & Energy Systems 2015; 67: 43.
  • [27] Kavcıoğlu, Ş. Yenilenebilir Enerji ve Türkiye [Renewable Energy and Türkiye]. Finansal Araştırmalar ve Çalışmalar Dergisi 2019; 11(21): 209-227.
  • [28] Akman, T, Yılmaz, C, Sönmez, Y. Elektrik Yükü Tahmin Yöntemlerinin Analizi [Analysis of Electrical Load Forecasting Methods]. Gazi Mühendislik Bilimleri Dergisi 2018; 4(3): 168-175.
  • [29] Akpınar, M., Yumuşak, N. Günlük temelli orta vadeli şehir doğal gaz talebinin tek değişkenli istatistik teknikleri ile tahmini [Daily basis mid-term demand forecast of city natural gas using univariate statistical techniques]. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 2020; 35(2): 725-742.
  • [30] Box, GE, Jenkins, GM, Reinsel, GC, Ljung, GM. Time series analysis: Forecasting and control. John Wiley & Sons, 2015.
  • [31] Montgomery, DC, Jennings, CL, Kulahci, M. Introduction to time series analysis and forecasting. John Wiley & Sons, 2015.
There are 31 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Research Articles
Authors

Erol Kurt 0000-0002-3615-6926

Reşat Kasap 0000-0002-9306-3101

Kayhan Çelik 0000-0003-0371-0473

Publication Date September 30, 2022
Acceptance Date August 30, 2022
Published in Issue Year 2022 Volume: 6 Issue: 3

Cite

Vancouver Kurt E, Kasap R, Çelik K. Forecasting of monthly electricity generation from the conventional and renewable resources following the corona virus pandemic in Turkey. Journal of Energy Systems. 2022;6(3):420-35.

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