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Forecasting electricity consumption of OECD countries: A global machine learning modeling approach

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  • Sen, Doruk
  • Tunç, K.M. Murat
  • Günay, M. Erdem

Abstract

Electricity is a critical utility for social growth. Accurate estimation of its consumption plays a vital role in economic development. A database that included past electricity consumption data from all OECD countries was prepared. Since national trends may be transferable from one country to another, the entire database was modeled and simulated via machine learning techniques to forecast the energy consumption of each country. Understanding similarities among the profiles of different countries could increase predictive accuracy and improve associated public policies.

Suggested Citation

  • Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:juipol:v:70:y:2021:i:c:s0957178721000564
    DOI: 10.1016/j.jup.2021.101222
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    Cited by:

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    4. Sen, Doruk & Hamurcuoglu, K. Irem & Ersoy, Melisa Z. & Tunç, K.M. Murat & Günay, M. Erdem, 2023. "Forecasting long-term world annual natural gas production by machine learning," Resources Policy, Elsevier, vol. 80(C).
    5. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    6. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).

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