Research Article
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Türkiye’de orta vadede elektrik talebine etki eden içsel değişkenlerin belirlenmesi

Year 2023, , 202 - 217, 31.07.2023
https://doi.org/10.58308/bemarej.1272685

Abstract

Bu çalışmada, elektrik talebine orta vadede etki eden içsel değişkenler belirlenmeye çalışılmıştır. Bu amaçla, 2007 Ocak-2020 Aralık dönemlerine ilişkin tüketici fiyat endeksi, işsizlik, ülkeye gelen turist sayısı ve sanayi üretim endeksi değişkenlerinin aylık gözlemleri ile araştırılmıştır. Yöntem olarak, varyans ayrıştırması ve Toda Yamamoto nedensellik testleri ile incelenmektedir. Bulgular ise 0.05 anlamlılık düzeyinde işsizlik ile elektrik talebi ve benzer şekilde 0.10 anlamlılık düzeyinde sanayi üretim endeksi ile çift yönlü nedensellik bulgusu elde edilirken, ülkeye gelen turist sayısından, elektrik talebine ise 0.01 anlamlılık düzeyinde tek yönlü nedensellik elde edilmiştir. Bu bulgulara paralel sonuçlar varyans ayrıştırması ile de elde edilmiştir. Dolayısıyla, elektrik talebinin cari dönemdeki değerlerinin açıklanmasında sanayi üretim endeksi, işsizlik ve özellikle ülkeye gelen turist sayısı değişkenlerinin gecikmeli değerlerinin katısı olduğu tespit edilmiştir. Planlayıcıların, elektrik tahmini ile ilgili projeksiyonlar oluştururken bu değişkenlerin tahmin modellerine dahil edilmesinin ve sabit değişkenler yerine farklı değişkenlerin tahmin modellerine dahil edilmesinin doğruya yakın sonuçlar elde edilmesine katkı sunacaktır.

References

  • Abual-Foul, B. M. (2012). Forecasting energy demand in Jordan using artificial neural networks. Topics in Middle Eastern and African Economies, 14(September), 473-478.
  • Baltaş, M. E., & Akbay, C. (2021). Akdeniz elektrik dağıtım bölgesi (Antalya-Isparta-Burdur) elektrik tüketim talep tahmini. Yönetim ve Ekonomi Araştırmaları Dergisi, 19(2), 222-238.
  • Çunkaş, M., & Altun, A. A. (2010). Long term electricity demand forecasting in Turkey using artificial neural networks. Energy Sources, Part B: Economics, Planning and Policy, 5(3), 279-289. doi: 10.1080/15567240802533542
  • Ediger, V. Ş., & Tatlidil, H. (2002). Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management, 43(4), 473-487. doi: 10.1016/S0196-8904(01)00033-4
  • Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517. doi: 10.1016/j.energy.2009.10.018
  • Enders, W. (2014). Applied econometric time series. In Angewandte Chemie International Edition, 6(11), 951-952. (4th ed.). Wiley.
  • Es, H. A., Kalender, F. Y., & Hamzaçebi, C. (2014). Yapay sinir ağları ile Türkiye net enerji talep tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 29(3), 495-504. doi: 10.17341/gummfd.41725
  • Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Essays in Econometrics Vol II: Collected Papers of Clive W. J. Granger, 37(3), 31-47. doi: 10.1017/ccol052179207x.002
  • Günay, M. E. (2016). Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic ındicators and climatic conditions: Case of Turkey. Energy Policy, 90, 92-101. doi: 10.1016/j.enpol.2015.12.019
  • Guta, F., Damte, A., & Rede, T. F. (2015). The residential demand for electricity in Ethiopia. Environment for Development Initiative, April, 38.
  • Halicioglu, F. (2007). Residential electricity demand dynamics in Turkey. Energy Economics, 29(2), 199-210. doi: 10.1016/j.eneco.2006.11.007
  • Hamzaçebi, C. (2007). Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy, 35(3), 2009-2016. doi: 10.1016/j.enpol.2006.03.014
  • Hasanov, F. J., Hunt, L. C., & Mikayilov, C. I. (2016). Modeling and forecasting electricity demand in Azerbaijan using cointegration techniques. Energies, 9(12), 1-31. doi: 10.3390/en9121045
  • Kandananond, K. (2011). Forecasting electricity demand in Thailand with an artificial neural network approach. Energies, 4(8), 1246-1257. doi: 10.3390/en4081246
  • Kavaklioglu, K., Ceylan, H., Ozturk, H. K., & Canyurt, O. E. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management, 50(11), 2719-2727. doi: 10.1016/j.enconman.2009.06.016
  • Keating, J. W. (1990). Identifying VAR models under rational expectations. Journal of Monetary Economics, 25(3), 453-476. doi: 10.1016/0304-3932(90)90063-A
  • Kirikkaleli, D., Sokri, A., Candemir, M., & Ertugrul, H. M. (2018). Panel cointegration: Long-run relationship between internet, electricity consumption and economic growth. Evidence from OECD countries. Investigacion Economica, 77(303), 161-176. doi: 10.22201/fe.01851667p.2018.303.64158
  • Kocaman, B. (2012). Elektronik sayaç kullanımında tarife seçiminin önemi. BEÜ Fen Bilimleri Dergisi, 1(1), 59-65.
  • Kumar, V., Leone, R. P., & Gaskins, J. N. (1995). Aggregate and disaggregate sector forecasting using consumer confidence measures. International Journal of Forecasting, 11(3), 361-377. doi: 10.1016/0169-2070(95)00594-2
  • Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer-Verlag Berlin Heidelberg.
  • Mamingi, N. (2005). Theoretical and empirical exercises in econometrics.
  • Özgen, F. B., & Güloğlu, B. (2004). Türkiye’de iç borçların iktisadî etkilerinin VAR tekniğiyle analizi. ODTÜ Gelişme Dergisi, 31, 93-114.
  • Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1. doi: 10.2307/1912017
  • Sözen, A., & Arcaklioglu, E. (2007). Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy, 35(10), 4981-4992. doi: 10.1016/j.enpol.2007.04.029
  • Tarı, P. D. R., & Bozkurt, Y. D. D. H. (2006). Türkiye’de istikrarsız büyümenin VAR modelleri ile analizi (1991.1-2004.3). Ekonometri ve İstatistik E-Dergisi, 4, 12-28.
  • Taylor, J. W., de Menezes, L. M., & McSharry, P. E. (2006). A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting, 22(1), 1-16. doi: 10.1016/j.ijforecast.2005.06.006
  • Tezikci, S. (2005). Türkiye’de enerji sektörü ve elektrik enerjisi talep projeksiyonu (Kaynaklar-Politikalar). İstanbul Üniversitesi.
  • Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. doi: 10.1016/0304-4076(94)01616-8
  • Wahid, F., Ullah, H., Ali, S., Jan, S. A., Ali, A., Khan, A., Khan, I. A., & Bibi, M. (2021). The determinants and forecasting of electricity consumption in Pakistan. International Journal of Energy Economics and Policy, 11(1), 241-248. doi: 10.32479/ijeep.10646
  • Yang, L., & Pang, J. (2021). Analysis and research on forecasting electricity demand based on ARMA and VAR model. IOP Conference Series: Earth and Environmental Science, 804(4). doi: 10.1088/1755-1315/804/4/042008
  • Zivanovic, R. (2002). Nonparametric trend model for short term electricity demand forecasting. IEE Conference Publication, 488, 347-352. doi: 10.1049/cp:20020060

Identification of endogenous variables affecting medium-term electricity demand in Turkey

Year 2023, , 202 - 217, 31.07.2023
https://doi.org/10.58308/bemarej.1272685

Abstract

This study has tried to determine the endogenous variables that affect electricity demand in the medium term. For this purpose, monthly observations of the consumer price index, unemployment, number of tourists visiting the country, and industrial production index for the 2007 January-2020 December period were investigated. As a method, we use variance decomposition and Toda Yamamoto causality tests. Findings: Two-way causality was obtained with unemployment and electricity demand at the 0.05 significance level and the industrial production index at the 0.10 significance level, while unidirectional causality was obtained with the number of tourists visiting the country and electricity demand at the 0.01 significance level. Results parallel to these findings were also obtained by variance decomposition. Therefore, it has been determined that the variables of the industrial production index, unemployment, and especially the number of tourists coming to the country are multiples of the lagged values in explaining the current period values of electricity demand. Including these variables in the estimation models while the planners are creating projections related to the electricity estimation and including different variables in the estimation models instead of the fixed variables will contribute to obtaining near-accurate results.

References

  • Abual-Foul, B. M. (2012). Forecasting energy demand in Jordan using artificial neural networks. Topics in Middle Eastern and African Economies, 14(September), 473-478.
  • Baltaş, M. E., & Akbay, C. (2021). Akdeniz elektrik dağıtım bölgesi (Antalya-Isparta-Burdur) elektrik tüketim talep tahmini. Yönetim ve Ekonomi Araştırmaları Dergisi, 19(2), 222-238.
  • Çunkaş, M., & Altun, A. A. (2010). Long term electricity demand forecasting in Turkey using artificial neural networks. Energy Sources, Part B: Economics, Planning and Policy, 5(3), 279-289. doi: 10.1080/15567240802533542
  • Ediger, V. Ş., & Tatlidil, H. (2002). Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management, 43(4), 473-487. doi: 10.1016/S0196-8904(01)00033-4
  • Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517. doi: 10.1016/j.energy.2009.10.018
  • Enders, W. (2014). Applied econometric time series. In Angewandte Chemie International Edition, 6(11), 951-952. (4th ed.). Wiley.
  • Es, H. A., Kalender, F. Y., & Hamzaçebi, C. (2014). Yapay sinir ağları ile Türkiye net enerji talep tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 29(3), 495-504. doi: 10.17341/gummfd.41725
  • Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Essays in Econometrics Vol II: Collected Papers of Clive W. J. Granger, 37(3), 31-47. doi: 10.1017/ccol052179207x.002
  • Günay, M. E. (2016). Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic ındicators and climatic conditions: Case of Turkey. Energy Policy, 90, 92-101. doi: 10.1016/j.enpol.2015.12.019
  • Guta, F., Damte, A., & Rede, T. F. (2015). The residential demand for electricity in Ethiopia. Environment for Development Initiative, April, 38.
  • Halicioglu, F. (2007). Residential electricity demand dynamics in Turkey. Energy Economics, 29(2), 199-210. doi: 10.1016/j.eneco.2006.11.007
  • Hamzaçebi, C. (2007). Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy, 35(3), 2009-2016. doi: 10.1016/j.enpol.2006.03.014
  • Hasanov, F. J., Hunt, L. C., & Mikayilov, C. I. (2016). Modeling and forecasting electricity demand in Azerbaijan using cointegration techniques. Energies, 9(12), 1-31. doi: 10.3390/en9121045
  • Kandananond, K. (2011). Forecasting electricity demand in Thailand with an artificial neural network approach. Energies, 4(8), 1246-1257. doi: 10.3390/en4081246
  • Kavaklioglu, K., Ceylan, H., Ozturk, H. K., & Canyurt, O. E. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management, 50(11), 2719-2727. doi: 10.1016/j.enconman.2009.06.016
  • Keating, J. W. (1990). Identifying VAR models under rational expectations. Journal of Monetary Economics, 25(3), 453-476. doi: 10.1016/0304-3932(90)90063-A
  • Kirikkaleli, D., Sokri, A., Candemir, M., & Ertugrul, H. M. (2018). Panel cointegration: Long-run relationship between internet, electricity consumption and economic growth. Evidence from OECD countries. Investigacion Economica, 77(303), 161-176. doi: 10.22201/fe.01851667p.2018.303.64158
  • Kocaman, B. (2012). Elektronik sayaç kullanımında tarife seçiminin önemi. BEÜ Fen Bilimleri Dergisi, 1(1), 59-65.
  • Kumar, V., Leone, R. P., & Gaskins, J. N. (1995). Aggregate and disaggregate sector forecasting using consumer confidence measures. International Journal of Forecasting, 11(3), 361-377. doi: 10.1016/0169-2070(95)00594-2
  • Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer-Verlag Berlin Heidelberg.
  • Mamingi, N. (2005). Theoretical and empirical exercises in econometrics.
  • Özgen, F. B., & Güloğlu, B. (2004). Türkiye’de iç borçların iktisadî etkilerinin VAR tekniğiyle analizi. ODTÜ Gelişme Dergisi, 31, 93-114.
  • Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1. doi: 10.2307/1912017
  • Sözen, A., & Arcaklioglu, E. (2007). Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy, 35(10), 4981-4992. doi: 10.1016/j.enpol.2007.04.029
  • Tarı, P. D. R., & Bozkurt, Y. D. D. H. (2006). Türkiye’de istikrarsız büyümenin VAR modelleri ile analizi (1991.1-2004.3). Ekonometri ve İstatistik E-Dergisi, 4, 12-28.
  • Taylor, J. W., de Menezes, L. M., & McSharry, P. E. (2006). A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting, 22(1), 1-16. doi: 10.1016/j.ijforecast.2005.06.006
  • Tezikci, S. (2005). Türkiye’de enerji sektörü ve elektrik enerjisi talep projeksiyonu (Kaynaklar-Politikalar). İstanbul Üniversitesi.
  • Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. doi: 10.1016/0304-4076(94)01616-8
  • Wahid, F., Ullah, H., Ali, S., Jan, S. A., Ali, A., Khan, A., Khan, I. A., & Bibi, M. (2021). The determinants and forecasting of electricity consumption in Pakistan. International Journal of Energy Economics and Policy, 11(1), 241-248. doi: 10.32479/ijeep.10646
  • Yang, L., & Pang, J. (2021). Analysis and research on forecasting electricity demand based on ARMA and VAR model. IOP Conference Series: Earth and Environmental Science, 804(4). doi: 10.1088/1755-1315/804/4/042008
  • Zivanovic, R. (2002). Nonparametric trend model for short term electricity demand forecasting. IEE Conference Publication, 488, 347-352. doi: 10.1049/cp:20020060
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Operation
Journal Section Research Article
Authors

Savaş Tarkun 0000-0002-2684-184X

Erkan Işığıçok 0000-0003-4037-0869

Publication Date July 31, 2023
Acceptance Date July 30, 2023
Published in Issue Year 2023

Cite

APA Tarkun, S., & Işığıçok, E. (2023). Türkiye’de orta vadede elektrik talebine etki eden içsel değişkenlerin belirlenmesi. Business Economics and Management Research Journal, 6(2), 202-217. https://doi.org/10.58308/bemarej.1272685

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