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TÜRKİYE ELEKTRİK ENERJİSİ ÜRETİMİ VE TÜKETİMİNİN GRİ TAHMİN YÖNTEMİ İLE BELİRLENMESİ

Year 2018, Volume: 4 Issue: 2, 205 - 209, 19.12.2018

Utkucan Şahin

https://doi.org/10.22531/muglajsci.450307
Cited By: 7

Abstract

Bu çalışmanın amacı,
2017-2027 yılları arasındaki Türkiye elektrik enerjisi üretimi ve tüketiminin
tahmin edilmesidir. Bunu gerçekleştirmek için, 1996-2016 yılları arasındaki
Türkiye elektrik enerjisi üretimi ve tüketimi verileri gri tahmin yöntemi
GM(1,1) ile modellenmiştir. Sonuçlar, Türkiye elektrik enerjisi üretimi için
oluşturulan GM(1,1) modeline ait küçük hata olasılığı (p) ve sonsal hata oranı
(C) değerlerinin sırasıyla 0.12 ve 0.97 olduğunu, Türkiye elektrik enerjisi
tüketimi için oluşturulan GM(1,1) modeline ait p ve C değerlerinin ise
sırasıyla 0.11 ve 0.97 olduğunu göstermiştir. Bundan dolayı, Türkiye elektrik
enerjisi üretimi ve tüketimi için oluşturulan GM(1,1) modelleri iyi
seviyededir. Buna ek olarak, Türkiye elektrik enerjisi üretimi ve tüketimi için
oluşturulan GM(1,1) modellerinde ortalama mutlak yüzde hata (MAPE) değerlerinin
sırasıyla %3.12 ve %3.08 olduğu sonucuna ulaşılmıştır. F-testinin sonuçları,
Türkiye elektrik enerjisi üretimi ve tüketimi için oluşturulan GM(1,1)
modellerine ait p-değerinin 0.48 olduğunu göstermiştir. Bu sonuçlara göre,  oluşturulan GM(1,1) modellerinin Türkiye
elektrik enerjisi üretiminin ve tüketiminin tahmini için kullanılması uygundur.
Ayrıca, 2017-2027 yılları arasında Türkiye elektrik enerjisi üretimi ve
tüketiminin yıllık ortalama büyüme oranının sırasıyla %5.25 ve %5.58 olacağı
tahmin edilmiştir. Buna ek olarak, 2023 yılı için Türkiye elektrik enerjisi
üretimi ve tüketiminin 405310GWh ve 344672GWh olacağı tahmin edilmiştir.

Keywords

Gri tahmin modeli Türkiye elektrik enerjisi üretimi elektrik enerjisi tüketimi tahminleme

References

  • [1] ETKB, 2017. Dünya ve Türkiye Enerji ve Tabii Kaynaklar Görünümü, http://www.enerji.gov.tr/File/?path=ROOT%2f1%2fDocuments%2fEnerji%20ve%20Tabii%20Kaynaklar%20G%C3%B6r%C3%BCn%C3%BCm%C3%BC%2fSayi_15.pdf. [Accessed date: 01/08/2018]
  • [2] TEİAŞ, 2017a. http://www.teias.gov.tr/sites/default/files/2017-10/37.docx. Accessed date: 01/08/2018]
  • [3] TEİAŞ, 2017b. http://www.tuik.gov.tr/PreIstatistikTablo.do?istab_id=1579. Accessed date: 01/08/2018]
  • [4] Suganthi, L., Samuel, A.A., “Energy models for demand forecasting-a review”, Renewable and Sustainable Energy Reviews, 16, 1223-1240, 2012.
  • [5] Zhang, G.P., “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing, 50, 159-175, 2003.
  • [6] Hamzaçebi, C., “Forecasting of Turkey’s net electricity energy consumption on sectoral bases”, Energy Policy, 35, 2009-2016, 2007.
  • [7] Kavaklioglu, K., Ceylan, H., Ozturk, H.K., Canyurt, O.E., “Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks”, Energy Conversion and Management, 50, 2719-2727, 2009.
  • [8] Hamzacebi, C., Es, H.A., “Forecasting the annual electricity consumption of Turkey using an optimized grey model”, Energy, 70, 165-171, 2014.
  • [9] Tutun, S., Cho, C-A., Canıyılmaz, E., “A new forecasting framework behavior in net electricity consumption: a case study in Turkey”, Energy, 93, 2406-2422, 2015.
  • [10] Yukseltan, E., Yucekaya, A., Bilge, A.H., “Forecasting electricity demand for Turkey: modeling periodic variations and demand segregation”, Applied Energy, 193, 287-296, 2017.
  • [11] Şişman, B., “A comparison of ARIMA and Grey models for electricity consumption demand forecasting: The case of Turkey”, Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13, 234-245, 2017.
  • [12] Feng, S.J., Ma, Y.D., Song, Z.L., Ying, J., “Forecasting the energy consumption of China by the Grey prediction model”, Energy Sources, Part B: Economics, Planning, and Policy, 7 (4), 376-389, 2012.
  • [13] Kazemi, A., Modarres, M., Mehregan, M. R., Neshat, N., Foroughi, A., “A markov chain grey forecasting model: a case study of energy demand of industry sector in Iran”, 3rd International Conference on Information and Financial Engineering IPEDR, Singapore, 13-18, 2011.
  • [14] Hu, Y-C., Jiang, P., “Forecasting energy demand using neural-network-based grey residual modification models”, Journal of the Operational Research Society, 68, 556-565, 2017.
  • [15] Mostafaei, H., Kordnoori, S., “Hybrid grey forecasting model for Iran’s energy consumption and supply”, International Journal of Energy Economics and Policy, 2 (3), 97-102, 2012.
  • [16] Shen, X., Lu, Z., “The application of Grey theory model in the prediction of Jiangsu Province’s electric power demand”, AASRI Procedia, 7, 81-87, 2014.
  • [17] Xu, N., Dang, Y., Gong, Y., “Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China”, Energy, 118, 473-480, 2017.
  • [18] Zhan-li, M., Jin-hua, S., “Application of Grey-Markov model in forecasting fire accidents”, Procedia Engineering, 11, 314-318, 2011.

FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD

Year 2018, Volume: 4 Issue: 2, 205 - 209, 19.12.2018

Utkucan Şahin

https://doi.org/10.22531/muglajsci.450307
Cited By: 7

Abstract

The aim of this study is
forecasting of Turkey’s electricity generation and consumption for the period
2017-2027. To achieve this, Turkey’s electricity generation and consumption for
the period 1996-2016 was modelled using Grey prediction method GM(1,1). Results
showed that the small error probability (p) and posterior error ratio (C)
values of GM(1,1) model for Turkey’s electricity generation were obtained as
0.12 and 0.97, respectively, and 0.11 and 0.97, respectively for Turkey’s
electricity consumption. So, the level of established GM(1,1) models for
Turkey’s electricity generation and Turkey’s electricity consumption is in good
level. Additionally, mean absolute percentage error (MAPE) values of GM(1,1)
models for Turkey’s electricity generation and consumption were obtained as
3.12% and 3.08%, respectively. Results of F-test showed that p-value of GM(1,1)
model for Turkey’s electricity generation and consumption was 0.48. According
to these results, GM(1,1) models are suitable for prediction of Turkey’s
electricity generation and consumption. Furthermore, the average annual grow
rates of Turkey’s electricity generation and consumption for the period
2017-2027 were forecasted as 5.25% and 5.58%, respectively. In addition to
this, Turkey’s electricity generation and consumption were forecasted as
405310GWh and 344672GWh, respectively, for 2023.

Keywords

Grey prediction method Turkey electricity generation electricity consumption forecasting

References

  • [1] ETKB, 2017. Dünya ve Türkiye Enerji ve Tabii Kaynaklar Görünümü, http://www.enerji.gov.tr/File/?path=ROOT%2f1%2fDocuments%2fEnerji%20ve%20Tabii%20Kaynaklar%20G%C3%B6r%C3%BCn%C3%BCm%C3%BC%2fSayi_15.pdf. [Accessed date: 01/08/2018]
  • [2] TEİAŞ, 2017a. http://www.teias.gov.tr/sites/default/files/2017-10/37.docx. Accessed date: 01/08/2018]
  • [3] TEİAŞ, 2017b. http://www.tuik.gov.tr/PreIstatistikTablo.do?istab_id=1579. Accessed date: 01/08/2018]
  • [4] Suganthi, L., Samuel, A.A., “Energy models for demand forecasting-a review”, Renewable and Sustainable Energy Reviews, 16, 1223-1240, 2012.
  • [5] Zhang, G.P., “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing, 50, 159-175, 2003.
  • [6] Hamzaçebi, C., “Forecasting of Turkey’s net electricity energy consumption on sectoral bases”, Energy Policy, 35, 2009-2016, 2007.
  • [7] Kavaklioglu, K., Ceylan, H., Ozturk, H.K., Canyurt, O.E., “Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks”, Energy Conversion and Management, 50, 2719-2727, 2009.
  • [8] Hamzacebi, C., Es, H.A., “Forecasting the annual electricity consumption of Turkey using an optimized grey model”, Energy, 70, 165-171, 2014.
  • [9] Tutun, S., Cho, C-A., Canıyılmaz, E., “A new forecasting framework behavior in net electricity consumption: a case study in Turkey”, Energy, 93, 2406-2422, 2015.
  • [10] Yukseltan, E., Yucekaya, A., Bilge, A.H., “Forecasting electricity demand for Turkey: modeling periodic variations and demand segregation”, Applied Energy, 193, 287-296, 2017.
  • [11] Şişman, B., “A comparison of ARIMA and Grey models for electricity consumption demand forecasting: The case of Turkey”, Kastamonu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13, 234-245, 2017.
  • [12] Feng, S.J., Ma, Y.D., Song, Z.L., Ying, J., “Forecasting the energy consumption of China by the Grey prediction model”, Energy Sources, Part B: Economics, Planning, and Policy, 7 (4), 376-389, 2012.
  • [13] Kazemi, A., Modarres, M., Mehregan, M. R., Neshat, N., Foroughi, A., “A markov chain grey forecasting model: a case study of energy demand of industry sector in Iran”, 3rd International Conference on Information and Financial Engineering IPEDR, Singapore, 13-18, 2011.
  • [14] Hu, Y-C., Jiang, P., “Forecasting energy demand using neural-network-based grey residual modification models”, Journal of the Operational Research Society, 68, 556-565, 2017.
  • [15] Mostafaei, H., Kordnoori, S., “Hybrid grey forecasting model for Iran’s energy consumption and supply”, International Journal of Energy Economics and Policy, 2 (3), 97-102, 2012.
  • [16] Shen, X., Lu, Z., “The application of Grey theory model in the prediction of Jiangsu Province’s electric power demand”, AASRI Procedia, 7, 81-87, 2014.
  • [17] Xu, N., Dang, Y., Gong, Y., “Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China”, Energy, 118, 473-480, 2017.
  • [18] Zhan-li, M., Jin-hua, S., “Application of Grey-Markov model in forecasting fire accidents”, Procedia Engineering, 11, 314-318, 2011.
There are 18 citations in total.

Details

Primary Language English
Journal Section Journals
Authors

Utkucan Şahin 0000-0002-5869-8451

Publication Date December 19, 2018
Published in Issue Year 2018 Volume: 4 Issue: 2

Cite

APA Şahin, U. (2018). FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD. Mugla Journal of Science and Technology, 4(2), 205-209. https://doi.org/10.22531/muglajsci.450307
AMA Şahin U. FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD. MJST. December 2018;4(2):205-209. doi:10.22531/muglajsci.450307
Chicago Şahin, Utkucan. “FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD”. Mugla Journal of Science and Technology 4, no. 2 (December 2018): 205-9. https://doi.org/10.22531/muglajsci.450307.
EndNote Şahin U (December 1, 2018) FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD. Mugla Journal of Science and Technology 4 2 205–209.
IEEE U. Şahin, “FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD”, MJST, vol. 4, no. 2, pp. 205–209, 2018, doi: 10.22531/muglajsci.450307.
ISNAD Şahin, Utkucan. “FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD”. Mugla Journal of Science and Technology 4/2 (December 2018), 205-209. https://doi.org/10.22531/muglajsci.450307.
JAMA Şahin U. FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD. MJST. 2018;4:205–209.
MLA Şahin, Utkucan. “FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD”. Mugla Journal of Science and Technology, vol. 4, no. 2, 2018, pp. 205-9, doi:10.22531/muglajsci.450307.
Vancouver Şahin U. FORECASTING OF TURKEY’S ELECTRICITY GENERATION AND CONSUMPTION WITH GREY PREDICTION METHOD. MJST. 2018;4(2):205-9.

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