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Forecasting Energy Consumption of Turkey by Arima Model

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  • Suat Ozturk
  • Feride Ozturk

Abstract

Forecasting energy consumption has an important role in planning energy strategies for both policy makers and related organizations in any country. In this study, coal, oil, natural gas, renewable and total energy consumption data for 1970-2015 is used to forecast energy consumption of Turkey for the next 25 years, using autoregressive integrated moving average (ARIMA) model. The ARIMA models are determined to be ARIMA(1, 1, 1) for coal consumption, ARIMA (0, 1, 0) for oil consumption, ARIMA (0, 0, 0) for natural gas consumption, ARIMA (1, 1, 0) for renewable energy consumption and ARIMA (0, 1, 2) for total energy consumption. The results indicate that Turkey's energy consumption will continue to increase by the end of 2040. Consumption of coal, oil, natural gas, renewable energy and total energy will continue to increase at an annual average rate of 4.87 %, 3.92 %, 4.39 %, 1.64 % and 4.20 %, respectively in the next 25 years.

Suggested Citation

  • Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60.
    • Handle: RePEc:asi:joasrj:v:8:y:2018:i:2:p:52-60:id:3874
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    Cited by:

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    7. Anca Mehedintu & Mihaela Sterpu & Georgeta Soava, 2018. "Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    8. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    9. Mihaela Simionescu & Wadim Strielkowski & Manuela Tvaronavičienė, 2020. "Renewable Energy in Final Energy Consumption and Income in the EU-28 Countries," Energies, MDPI, vol. 13(9), pages 1-18, May.
    10. Karakurt, Izzet & Aydin, Gokhan, 2023. "Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries," Energy, Elsevier, vol. 263(PA).
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    12. Mihaela Simionescu & Carmen Beatrice Păuna & Tiberiu Diaconescu, 2020. "Renewable Energy and Economic Performance in the Context of the European Green Deal," Energies, MDPI, vol. 13(23), pages 1-19, December.
    13. Feride OZTURK & Suat OZTURK, 2018. "Exploring the Nexus of Coal Consumption, Economic Growth, Energy Prices and Technological Innovation in Turkey," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 8(12), pages 1406-1414, December.
    14. Mihaela Simionescu & Yuriy Bilan & Emília Krajňáková & Dalia Streimikiene & Stanisław Gędek, 2019. "Renewable Energy in the Electricity Sector and GDP per Capita in the European Union," Energies, MDPI, vol. 12(13), pages 1-15, June.
    15. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.
    16. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    17. Irina A. Firsova & Dinara G. Vasbieva & Nikolay N. Kosarenko & Maria A. Khvatova & Lev R. Klebanov, 2019. "Energy Consumption Forecasting for Power Supply Companies," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 1-6.
    18. Salvatore Carta & Andrea Medda & Alessio Pili & Diego Reforgiato Recupero & Roberto Saia, 2018. "Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data," Future Internet, MDPI, vol. 11(1), pages 1-19, December.

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