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Technical institutes

Advances in Machine Learning for Green Energy Systems

Strategic Insights

— 1 min read

Research from Istanbul Technical University has highlighted the importance of advanced models in transitioning to green energy, with a focus on agent-based modeling (ABM) and multi-agent systems. Machine learning (ML) methods are being integrated into ABM to simulate complex energy systems, including renewable power plants, electric vehicles, and battery energy storage systems. A systematic literature review has examined the integration of ML within ABM frameworks, identifying gaps in research areas such as long-term and data prediction studies.

Key Takeaways:

  • The transition to green energy requires advanced models to analyze complex energy systems, with ABM and ML being pivotal simulation techniques.
  • Machine learning methods are integrated into ABM to simulate real-world scenarios and the behavior of emerging energy market agents.
  • Traditional reinforcement learning (RL) methods dominate agent learning, but significant gaps are evident in terms of the inclusion of long-term and data prediction studies.
  • Future studies should integrate more observations from other energy markets along with electricity.
  • The study provides an overview of the current state of the field and identifies potential research gaps.
  • Istanbul Technical University researchers, including Burak Gokce and Gulgun Kayakutlu, are involved in the research.

Statistics:

  • The study examined articles published from 2014 to 2024.
  • The review methodology utilized self-organizing map (SOM) based clustering using market data types, simulation periods, and ML contribution types as the clustering attributes.
  • The findings indicate that agent learning and traditional RL methods dominate, with 75% of articles focusing on these areas.
  • The study highlighted significant gaps in research areas, including long-term and data prediction studies.

Sources:

  • Multi-Agent Energy Market Simulations With Machine Learning Integration: A Systematic Literature Review. IEEE Access, 2025, 13():106003-106018. (DOI: 10.1109/ACCESS.2025.3580735)
  • Journal of Engineering, p 1549, July 7, 2025.

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