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Can machine learning reduce volatility in electricity markets? Lessons from the economic calculation debate

Author

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  • Fuat Oğuz
  • Mustafa Çağrı Peker

Abstract

The knowledge problem and volatility in electricity markets have long been central to policy debates in energy markets. This study examines the successes and limitations of machine learning in addressing these issues, contributing to the existing literature. Machine learning has shown promise in tackling specific technical aspects of power markets, but its shortcomings in forecasting customer behaviour and managing decentralised, renewable‐driven systems highlight the need for further refinement. While machine learning offers potential in reducing certain aspects of market volatility, it is not a comprehensive solution to the broader challenges faced by the electricity market.

Suggested Citation

  • Fuat Oğuz & Mustafa Çağrı Peker, 2025. "Can machine learning reduce volatility in electricity markets? Lessons from the economic calculation debate," Economic Affairs, Wiley Blackwell, vol. 45(1), pages 62-77, February.
    • Handle: RePEc:bla:ecaffa:v:45:y:2025:i:1:p:62-77
    DOI: 10.1111/ecaf.12686
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    References listed on IDEAS

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