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Impact of Covid-19 Outbreak on Turkey Electricity Generation

Year 2023, Volume: 6 Issue: 3, 2136 - 2153, 04.12.2023
https://doi.org/10.47495/okufbed.1181718

Abstract

With this study, it was aimed to analyze the change of electricity generation in Turkey depending on COVID-19 pandemic. For this purpose, analyzes of the changes in nine different electricity generation sectors besides total electricity generation were done. In addition, these analyze were also carried out on a monthly basis in order to better understand the change in total electricity generation. Trend, naive, exponential smoothing and holt linear trend methods were used for the analysis. The results were obtained by taking into account the 2020 forecast values of the method belonging to the mean absolute percentage error (MAPE) values obtained from these methods. According to these results, it was concluded that the COVID-19 epidemic negatively affected electricity generation from total, dam, lignite, stream, imported coal and wind and did not adversely affect electricity generation from natural gas, wind, solar, geothermal and biomass in Turkey for 2020. From the results of the analysis done for the monthly change in total electricity generation in 2020, it was seen that there was a decrease in the total generation compared to the expected in the months when there were restrictions.

References

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  • Alhajeri H.M., Almutairi A., Alenezi A. Energy demand in the State of Kuwait during the Covid-19 pandemic: technical, economic, and environmental perspectives. Energies 2020; 13(17): 4370. https://doi.org/10.3390/en13174370.
  • 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. https://doi.org/10.1016/j.erss.2020.101683.
  • Baran B. Prediction of Air Quality Index by Extreme Learning Machines. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP) 2019; 1-8, IEEE.
  • Baran B. Air Quality Index Prediction in Besiktas District by Artificial Neural Networks and K Nearest Neighbors. Journal of Engineering Sciences and Design 2021; 9(1): 52-63. https://doi.org/10.21923/jesd.671836.
  • Benli Y.K., Yıldız A. Altın fiyatının zaman serisi yöntemleri ve yapay sinir ağları ile öngörüsü. Dumlupınar University Journal of Social Sciences 2014; 42, 213-224.
  • Bulut M. Analysis of the Covid-19 Impact on Electricity Consumption and Production, Sakarya University Journal of Computer and Information Sciences 2020; 3:3. https://doi.org/10.35377/saucis.03.03.817595.
  • Ceylan Z. The impact of COVID-19 on the electricity demand: a case study for Turkey. International Journal of Energy Research, 2021, 45(9): 13022-13039. https://doi.org/10.1002/er.6631
  • Cicala S. Powering Work From Home. NBER Working 2020. Paper No. 27397 http://www.nber.org/papers/w27937
  • COVID-19 Timetable. URL: https://tr.wikipedia.org/wiki/T%C3%BCrkiye%27de_COVID-19_pandemisi_zaman_%C3%A7izelgesi, 2021.
  • Delgado D.B.d.M., Lima K.M.d., Cancela M.d.C., Siqueira C.A.d.S., Carvalho M., Souza D.L.B.d. Trend analyses of electricity load changes in Brazil due to COVID-19 shutdowns. Electric Power Systems Research 2021; 193, 107009. https://doi.org/10.1016/j.epsr.2020.107009.
  • Dincer I., Covid-19 coronavirus: Closing carbon age, but opening hydrogen age. International Journal of Energy Research 2020; 44: 6093–6097. https://doi.org/10.1002/er.5569.
  • Edomah N., Ndulue G. Energy transition in a lockdown: an analysis of the impact of COVID-19 on changes in electricity demand in Lagos Nigeria. Global Transitions 2020; 2: 127- 137. https://doi.org/10.1016/j.glt.2020.07.002.
  • Elavarasan R.M., Shafiullah G.M., Raju K., Mudgal V., Arif M.T., Jamal T., Subramanian S., Balaguru V.S.S., Reddy K.S., Subramaniam U. COVID-19: Impact analysis and recommendations for power sector operation. Applied Energy 2020; 279, 115739. https://doi.org/10.1016/j.apenergy.2020.115739.
  • Energy Exchange Istanbul (EXIST). Electricity generation. URL: https://seffaflik.epias.com.tr/transparency/uretim/gerceklesen-uretim/gercek-zamanli-uretim.xhtml, 2021.
  • Exponential Smoothing-1. https://www.researchgate.net/publication/345413376_Usage_of_Holt's_Linear_Trend_Exponential_Smoothing_for_Time_Series_Forecasting_in_Agricultural_Research, 2021.
  • Exponential Smoothing-2. https://courses.edx.org/asset-v1:MITx+CTL.SC1x_1+2T2015+type@asset+block/KeyConcept_Week3Lesson1.pdf, 2021.
  • Exponential Smoothing-3. https://en.wikipedia.org/wiki/Exponential_smoothing, 2021.
  • Exponential Smoothing-4. https://iopscience.iop.org/article/10.1088/1757-899X/166/1/012034/pdf, 2021.
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  • Forecasting Methods. https://studiousguy.com/forecasting-methods-with-examples, 2021.
  • Ghenai C., Bettayeb M. Data analysis of the electricity generation mix for clean energy transition during COVID-19 lockdowns, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2021; https://doi.org/10.1080/15567036.2021.1884772.
  • Halbrügge S., Schott P., Weibelzahl M. How did the German and other European electricity systems react to the COVID-19 pandemic?. Applied Energy 2021; 285: 116370. https://doi.org/10.1016/j.apenergy.2020.116370.
  • Holt’s Linear Trend-1. https://link.springer.com/referenceworkentry/10.1007%2F1-4020-0612-8_409, 2021.
  • Holt’s Linear Trend-2. https://www.real-statistics.com/time-series-analysis/basic-time-series-forecasting/holt-linear-trend/, 2021.
  • Huang L., Liao Q., Qiu R. Prediction-based analysis on power consumption gap under long-term emergency: a case in China under COVID-19. Applied Energy 2021. 283: 116339. https://doi.org/10.1016/j.apenergy.2020.116339.
  • İcel Y., Baran B., Kaygusuz A., Bektas O. Yenilenebilir Kaynakları İçeren Güç Sistemlerinin PowerWorld Programı ile Analizi, Otomatik Kontrol Ulusal Toplantısı, TOK 2013. 2013; 27-32.
  • Janzen B., Radulescu D. Electricity Use as a Real Time Indicator of the Economic Burden of the COVID-19-Related Lockdown: Evidence From Switzerland, CESifo Economic Studies 2020; 66(4): 303-321. https://doi.org/10.1093/cesifo/ifaa010.
  • Kanitkar T. The COVID-19 lockdown in India: impacts on the economy and the power sector. Global Transitions 2020; 2, 150- 156. https://doi.org/10.1016/j.glt.2020.07.005.
  • Naive Models. https://www.oreilly.com/library/view/budgeting-basics-and/9780470389683/9780470389683_naive_models.html, 2021.
  • Norouzi N., Gerardo Z., Rubens Z.d., Enevoldsen P., Forough A.B. The impact of COVID-19 on the electricity sector in Spain: An econometric approach based on prices. International Journal of Energy Research 2021; 45:6321-6332. https://doi.org/10.1002/er.6259.
  • Ozkurt N., Sekerci Oztura H., Guzelis C. 24-hour electricity consumption forecasting for day ahead market with long short term memory deep learning model, 12th International Conference on Electrical and Electronics Engineering (ELECO), IEEE 2020; 173-177, 2020.
  • Sahin U., Ballı S., Chen Y. Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods, Applied Energy 2021; 302, 117540. https://doi.org/10.1016/j.apenergy.2021.117540.
  • Snow S., Bean R., Glencross M., Horrocks N. Drivers behind residential electricity demand fluctuations due to COVID-19 restrictions. Energies 2020; 13: 5738. https://doi.org/10.3390/en13215738.
  • Wilder-Smith A., Freedman D.O. Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine 2020; 27:2. https://doi.org/10.1093/jtm/taaa020.

Covid-19 Salgınının Türkiye Elektrik Üretimine Etkisi

Year 2023, Volume: 6 Issue: 3, 2136 - 2153, 04.12.2023
https://doi.org/10.47495/okufbed.1181718

Abstract

Bu çalışma ile COVID-19 salgınına bağlı olarak Türkiye'de elektrik üretim değişiminin analiz edilmesi amaçlanmıştır. Bu amaçla toplam elektrik üretiminin yanı sıra dokuz farklı elektrik üretim sektöründeki değişimlerin analizleri yapılmıştır. Ayrıca toplam elektrik üretimindeki değişimi daha iyi anlayabilmek için bu analizler aylık olarak ta yapılmıştır. Analizler için trend, naive, exponential smoothing ve holt linear trend yöntemleri kullanılmıştır. Bu yöntemlerden elde edilen ortalama mutlak yüzde hata (MAPE) ve değerlerine ait yöntemin 2020 tahmin değerleri dikkate alınarak sonuçlar elde edilmiştir. Bu sonuçlara göre 2020 yılında Türkiye’de COVID-19 salgınının toplam, baraj, linyit, akarsu, ithal kömür ve rüzgardan elektrik üretimini olumsuz etkilediği, doğalgaz, rüzgar, güneş, jeotermal ve biyokütleden elektrik üretimini olumsuz etkilemediği sonucuna varılmıştır. 2020 yılında toplam elektrik üretimindeki aylık değişim için yapılan analiz sonuçlarına göre ise kısıtlamaların olduğu aylarda toplam üretimde beklenene göre düşüş olduğu görülmüştür.

Thanks

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References

  • Abu-Rayash A., Dincer I. Analysis of the electricity demand trends amidst the COVID-19 coronavirus pandemic. Energy Research & Social Science 2020; 68: 101682. https://doi.org/10.1016/j.erss.2020.101682.
  • Alhajeri H.M., Almutairi A., Alenezi A. Energy demand in the State of Kuwait during the Covid-19 pandemic: technical, economic, and environmental perspectives. Energies 2020; 13(17): 4370. https://doi.org/10.3390/en13174370.
  • 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. https://doi.org/10.1016/j.erss.2020.101683.
  • Baran B. Prediction of Air Quality Index by Extreme Learning Machines. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP) 2019; 1-8, IEEE.
  • Baran B. Air Quality Index Prediction in Besiktas District by Artificial Neural Networks and K Nearest Neighbors. Journal of Engineering Sciences and Design 2021; 9(1): 52-63. https://doi.org/10.21923/jesd.671836.
  • Benli Y.K., Yıldız A. Altın fiyatının zaman serisi yöntemleri ve yapay sinir ağları ile öngörüsü. Dumlupınar University Journal of Social Sciences 2014; 42, 213-224.
  • Bulut M. Analysis of the Covid-19 Impact on Electricity Consumption and Production, Sakarya University Journal of Computer and Information Sciences 2020; 3:3. https://doi.org/10.35377/saucis.03.03.817595.
  • Ceylan Z. The impact of COVID-19 on the electricity demand: a case study for Turkey. International Journal of Energy Research, 2021, 45(9): 13022-13039. https://doi.org/10.1002/er.6631
  • Cicala S. Powering Work From Home. NBER Working 2020. Paper No. 27397 http://www.nber.org/papers/w27937
  • COVID-19 Timetable. URL: https://tr.wikipedia.org/wiki/T%C3%BCrkiye%27de_COVID-19_pandemisi_zaman_%C3%A7izelgesi, 2021.
  • Delgado D.B.d.M., Lima K.M.d., Cancela M.d.C., Siqueira C.A.d.S., Carvalho M., Souza D.L.B.d. Trend analyses of electricity load changes in Brazil due to COVID-19 shutdowns. Electric Power Systems Research 2021; 193, 107009. https://doi.org/10.1016/j.epsr.2020.107009.
  • Dincer I., Covid-19 coronavirus: Closing carbon age, but opening hydrogen age. International Journal of Energy Research 2020; 44: 6093–6097. https://doi.org/10.1002/er.5569.
  • Edomah N., Ndulue G. Energy transition in a lockdown: an analysis of the impact of COVID-19 on changes in electricity demand in Lagos Nigeria. Global Transitions 2020; 2: 127- 137. https://doi.org/10.1016/j.glt.2020.07.002.
  • Elavarasan R.M., Shafiullah G.M., Raju K., Mudgal V., Arif M.T., Jamal T., Subramanian S., Balaguru V.S.S., Reddy K.S., Subramaniam U. COVID-19: Impact analysis and recommendations for power sector operation. Applied Energy 2020; 279, 115739. https://doi.org/10.1016/j.apenergy.2020.115739.
  • Energy Exchange Istanbul (EXIST). Electricity generation. URL: https://seffaflik.epias.com.tr/transparency/uretim/gerceklesen-uretim/gercek-zamanli-uretim.xhtml, 2021.
  • Exponential Smoothing-1. https://www.researchgate.net/publication/345413376_Usage_of_Holt's_Linear_Trend_Exponential_Smoothing_for_Time_Series_Forecasting_in_Agricultural_Research, 2021.
  • Exponential Smoothing-2. https://courses.edx.org/asset-v1:MITx+CTL.SC1x_1+2T2015+type@asset+block/KeyConcept_Week3Lesson1.pdf, 2021.
  • Exponential Smoothing-3. https://en.wikipedia.org/wiki/Exponential_smoothing, 2021.
  • Exponential Smoothing-4. https://iopscience.iop.org/article/10.1088/1757-899X/166/1/012034/pdf, 2021.
  • Exponential Smoothing-5. http://course1.winona.edu/bdeppa/FIN%20335/Handouts/Exponential_Smoothing%20(part%202).html, 2021.
  • Forecasting Methods. https://studiousguy.com/forecasting-methods-with-examples, 2021.
  • Ghenai C., Bettayeb M. Data analysis of the electricity generation mix for clean energy transition during COVID-19 lockdowns, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2021; https://doi.org/10.1080/15567036.2021.1884772.
  • Halbrügge S., Schott P., Weibelzahl M. How did the German and other European electricity systems react to the COVID-19 pandemic?. Applied Energy 2021; 285: 116370. https://doi.org/10.1016/j.apenergy.2020.116370.
  • Holt’s Linear Trend-1. https://link.springer.com/referenceworkentry/10.1007%2F1-4020-0612-8_409, 2021.
  • Holt’s Linear Trend-2. https://www.real-statistics.com/time-series-analysis/basic-time-series-forecasting/holt-linear-trend/, 2021.
  • Huang L., Liao Q., Qiu R. Prediction-based analysis on power consumption gap under long-term emergency: a case in China under COVID-19. Applied Energy 2021. 283: 116339. https://doi.org/10.1016/j.apenergy.2020.116339.
  • İcel Y., Baran B., Kaygusuz A., Bektas O. Yenilenebilir Kaynakları İçeren Güç Sistemlerinin PowerWorld Programı ile Analizi, Otomatik Kontrol Ulusal Toplantısı, TOK 2013. 2013; 27-32.
  • Janzen B., Radulescu D. Electricity Use as a Real Time Indicator of the Economic Burden of the COVID-19-Related Lockdown: Evidence From Switzerland, CESifo Economic Studies 2020; 66(4): 303-321. https://doi.org/10.1093/cesifo/ifaa010.
  • Kanitkar T. The COVID-19 lockdown in India: impacts on the economy and the power sector. Global Transitions 2020; 2, 150- 156. https://doi.org/10.1016/j.glt.2020.07.005.
  • Naive Models. https://www.oreilly.com/library/view/budgeting-basics-and/9780470389683/9780470389683_naive_models.html, 2021.
  • Norouzi N., Gerardo Z., Rubens Z.d., Enevoldsen P., Forough A.B. The impact of COVID-19 on the electricity sector in Spain: An econometric approach based on prices. International Journal of Energy Research 2021; 45:6321-6332. https://doi.org/10.1002/er.6259.
  • Ozkurt N., Sekerci Oztura H., Guzelis C. 24-hour electricity consumption forecasting for day ahead market with long short term memory deep learning model, 12th International Conference on Electrical and Electronics Engineering (ELECO), IEEE 2020; 173-177, 2020.
  • Sahin U., Ballı S., Chen Y. Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods, Applied Energy 2021; 302, 117540. https://doi.org/10.1016/j.apenergy.2021.117540.
  • Snow S., Bean R., Glencross M., Horrocks N. Drivers behind residential electricity demand fluctuations due to COVID-19 restrictions. Energies 2020; 13: 5738. https://doi.org/10.3390/en13215738.
  • Wilder-Smith A., Freedman D.O. Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine 2020; 27:2. https://doi.org/10.1093/jtm/taaa020.
There are 35 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section RESEARCH ARTICLES
Authors

Burhan Baran 0000-0001-6394-412X

Publication Date December 4, 2023
Submission Date September 29, 2022
Acceptance Date February 13, 2023
Published in Issue Year 2023 Volume: 6 Issue: 3

Cite

APA Baran, B. (2023). Impact of Covid-19 Outbreak on Turkey Electricity Generation. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(3), 2136-2153. https://doi.org/10.47495/okufbed.1181718
AMA Baran B. Impact of Covid-19 Outbreak on Turkey Electricity Generation. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. December 2023;6(3):2136-2153. doi:10.47495/okufbed.1181718
Chicago Baran, Burhan. “Impact of Covid-19 Outbreak on Turkey Electricity Generation”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6, no. 3 (December 2023): 2136-53. https://doi.org/10.47495/okufbed.1181718.
EndNote Baran B (December 1, 2023) Impact of Covid-19 Outbreak on Turkey Electricity Generation. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6 3 2136–2153.
IEEE B. Baran, “Impact of Covid-19 Outbreak on Turkey Electricity Generation”, Osmaniye Korkut Ata University Journal of Natural and Applied Sciences, vol. 6, no. 3, pp. 2136–2153, 2023, doi: 10.47495/okufbed.1181718.
ISNAD Baran, Burhan. “Impact of Covid-19 Outbreak on Turkey Electricity Generation”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 6/3 (December 2023), 2136-2153. https://doi.org/10.47495/okufbed.1181718.
JAMA Baran B. Impact of Covid-19 Outbreak on Turkey Electricity Generation. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. 2023;6:2136–2153.
MLA Baran, Burhan. “Impact of Covid-19 Outbreak on Turkey Electricity Generation”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 6, no. 3, 2023, pp. 2136-53, doi:10.47495/okufbed.1181718.
Vancouver Baran B. Impact of Covid-19 Outbreak on Turkey Electricity Generation. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. 2023;6(3):2136-53.

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